2Vh.ddlZddlZddlmZdadadadZdada eddgd Z ed d gd Z ed dgdZ eddgdZ eddgdZeddgdZeddZeddZeddZdZd ej(vrej(j+d ZnVej.j1d!Zej4eej6sd"Zej.j9ed#Zd$Zej.j1ej.j9ed%Zej.j?er e e5Z!ejDe!Z#ddde#j+d&e Z%e%d'vsJe#j+d(e Z&e'e&e(sJe#j+d)eZ)e#j+d*eZ*e*d+vsJe e%e e&ee*e)Zej.j?es ejVeej.j?esLe e eed,Z# e ed-5Z!e!j[ej\e#d./dddd0ej(vrej(d0Z)e)re)Zd1ej(vre0ej(d1ad2ej(vre0ej(d2a edk7rd3ej(d4<ed5d6gd7Z1ed8gd9Z2ed:gd;Z3edgd?Z5y#1swYxYw#e$$riZ#YwxYw#e,$rYRwxYw#1swYxYw#e/$rYwxYw)@N) keras_exportfloat32gHz> channels_last tensorflowzkeras.config.floatxzkeras.backend.floatxctS)zReturn the default float type, as a string. E.g. `'bfloat16'`, `'float16'`, `'float32'`, `'float64'`. Returns: String, the current default float type. Example: >>> keras.config.floatx() 'float32' )_FLOATXH/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/backend/config.pyfloatxr s  Nr zkeras.config.set_floatxzkeras.backend.set_floatxcLhd}||vrtd|d|t|ay)aSet the default float dtype. Note: It is not recommended to set this to `"float16"` for training, as this will likely cause numeric stability issues. Instead, mixed precision, which leverages a mix of `float16` and `float32`. It can be configured by calling `keras.mixed_precision.set_dtype_policy('mixed_float16')`. Args: value: String; `'bfloat16'`, `'float16'`, `'float32'`, or `'float64'`. Examples: >>> keras.config.floatx() 'float32' >>> keras.config.set_floatx('float64') >>> keras.config.floatx() 'float64' >>> # Set it back to float32 >>> keras.config.set_floatx('float32') Raises: ValueError: In case of invalid value. >float16rfloat64bfloat16zUnknown `floatx` value: z. Expected one of N) ValueErrorstrr)valueaccepted_dtypess r set_floatxr)sA8DO O#&ug../ 1  %jGr zkeras.config.epsilonzkeras.backend.epsilonctS)zReturn the value of the fuzz factor used in numeric expressions. Returns: A float. Example: >>> keras.config.epsilon() 1e-07 _EPSILONr r r epsilonrNs  Or zkeras.config.set_epsilonzkeras.backend.set_epsilonc|ay)aWSet the value of the fuzz factor used in numeric expressions. Args: value: float. New value of epsilon. Examples: >>> keras.config.epsilon() 1e-07 >>> keras.config.set_epsilon(1e-5) >>> keras.config.epsilon() 1e-05 >>> # Set it back to the default value. >>> keras.config.set_epsilon(1e-7) Nr)rs r set_epsilonr^s (Hr zkeras.config.image_data_formatzkeras.backend.image_data_formatctS)zReturn the default image data format convention. Returns: A string, either `'channels_first'` or `'channels_last'`. Example: >>> keras.config.image_data_format() 'channels_last' )_IMAGE_DATA_FORMATr r r image_data_formatrus $ r z"keras.config.set_image_data_formatz#keras.backend.set_image_data_formatc^t|j}|dvrtd||ay)aSet the value of the image data format convention. Args: data_format: string. `'channels_first'` or `'channels_last'`. Examples: >>> keras.config.image_data_format() 'channels_last' >>> keras.config.set_image_data_format('channels_first') >>> keras.config.image_data_format() 'channels_first' >>> # Set it back to `'channels_last'` >>> keras.config.set_image_data_format('channels_last') >rchannels_firsteThe `data_format` argument must be one of {'channels_first', 'channels_last'}. Received: data_format=N)rlowerrr data_formats r set_image_data_formatr%sD4k"((*K== %%0M 3  %r z#keras.config.enable_flash_attentionc4ddlm}|jddy)aEnable flash attention. Flash attention offers performance optimization for attention layers, making it especially useful for large language models (LLMs) that benefit from faster and more memory-efficient attention computations. Once enabled, supported layers like `MultiHeadAttention` will **attempt** to use flash attention for faster computations. By default, this feature is enabled. Note that enabling flash attention does not guarantee it will always be used. Typically, the inputs must be in `float16` or `bfloat16` dtype, and input layout requirements may vary depending on the backend. r global_stateflash_attentionNkeras.src.backend.commonr(set_global_attributer's r enable_flash_attentionr-s 6%%&7>r z$keras.config.disable_flash_attentionc4ddlm}|jddy)aqDisable flash attention. Flash attention offers performance optimization for attention layers, making it especially useful for large language models (LLMs) that benefit from faster and more memory-efficient attention computations. Once disabled, supported layers like `MultiHeadAttention` will not use flash attention for faster computations. rr'r)FNr*r's r disable_flash_attentionr/s6%%&7?r z'keras.config.is_flash_attention_enabledc4ddlm}|jddS)aChecks whether flash attention is globally enabled in Keras. Flash attention is a performance-optimized method for computing attention in large models, such as transformers, allowing for faster and more memory-efficient operations. This function checks the global Keras configuration to determine if flash attention is enabled for compatible layers (e.g., `MultiHeadAttention`). Note that enabling flash attention does not guarantee it will always be used. Typically, the inputs must be in `float16` or `bfloat16` dtype, and input layout requirements may vary depending on the backend. Returns: `False` if disabled; otherwise, it indicates that it is enabled. rr'r)N)default)r+r(get_global_attributer's r is_flash_attention_enabledr3s"6  , ,-> , MMr ct| tSt|j}|dvrtd||S)N>rr r!)rrr"rr#s r standardize_data_formatr5sQ ""k"((*K== %%0M 3  r KERAS_HOME~z/tmpz.kerasctS)N) _KERAS_DIRr r r keras_homer:s r z keras.jsonr >rrrrbackendr>rr )r rr;rw)indent KERAS_BACKENDKERAS_MAX_EPOCHSKERAS_MAX_STEPS_PER_EPOCHtrueTF_FORCE_GPU_ALLOW_GROWTHzkeras.config.backendzkeras.backend.backendctS)aPublicly accessible method for determining the current backend. Returns: String, the name of the backend Keras is currently using. One of `"tensorflow"`, `"torch"`, or `"jax"`. Example: >>> keras.config.backend() 'tensorflow' )_BACKENDr r r r;r;Cs & Or zkeras.config.set_max_epochsc|ay)aLimit the maximum number of epochs for any call to fit. This will cap the number of epochs for any training run using `model.fit()`. This is purely for debugging, and can also be set via the `KERAS_MAX_EPOCHS` environment variable to quickly run a script without modifying its source. Args: max_epochs: The integer limit on the number of epochs or `None`. If `None`, no limit is applied. N _MAX_EPOCHS) max_epochss r set_max_epochsrJYs Kr z$keras.config.set_max_steps_per_epochc|ay)aLimit the maximum number of steps for any call to fit/evaluate/predict. This will cap the number of steps for single epoch of a call to `fit()`, `evaluate()`, or `predict()`. This is purely for debugging, and can also be set via the `KERAS_MAX_STEPS_PER_EPOCH` environment variable to quickly run a scrip without modifying its source. Args: max_epochs: The integer limit on the number of epochs or `None`. If `None`, no limit is applied. N_MAX_STEPS_PER_EPOCH)max_steps_per_epochs r set_max_steps_per_epochrOis /r zkeras.config.max_epochsctS)a5Get the maximum number of epochs for any call to fit. Retrieves the limit on the number of epochs set by `keras.config.set_max_epochs` or the `KERAS_MAX_EPOCHS` environment variable. Returns: The integer limit on the number of epochs or `None`, if no limit has been set. rGr r r rIrIzs  r z keras.config.max_steps_per_epochctS)ajGet the maximum number of steps for any call to fit/evaluate/predict. Retrieves the limit on the number of epochs set by `keras.config.set_max_steps_per_epoch` or the `KERAS_MAX_STEPS_PER_EPOCH` environment variable. Args: max_epochs: The integer limit on the number of epochs or `None`. If `None`, no limit is applied. rLr r r rNrNs  r )6jsonoskeras.src.api_exportrrrrrErHrMr rrrrr%r-r/r3r5environgetr9path expanduser_keras_base_diraccessW_OKjoinr: _config_pathexistsopenfload_configr_floatx_epsilon isinstancefloat_backend_image_data_formatmakedirsOSErrorwritedumpsIOErrorintr;rJrOrIrNr r r ros -  %  $&<=>?"(*DEF!G!H%'>?@ A )+FGHI,()   ,- %  %<34?5?(45 @6 @78N9N* 2:: -Jgg((-O 299_bgg . ox8J ww!!"'',,z<"HI 77>>, ,  #1diilG #kk(FH-G 7 77 7{{9gi0H h && &{{9h/H %8:K:MN !D DD Dw,-H ww~~j!  J  ww~~l#(9.0 G  , $ 3 GGJDJJwq1 2 3bjj zz/*H#bjj!345K"**,rzz*EFG |/5BJJ*+    ,-. / 567 /8 / ()* + 123  4  A # # &     3 3    sf<NNNN N7$N+4N7N NNNN('N(+N40N77O?O