



# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


SCREAMING_SNAKE_CASE_:List[Any]             =						"""pytorch_model.bin"""
SCREAMING_SNAKE_CASE_:Optional[int]             =						"""pytorch_model.bin.index.json"""
SCREAMING_SNAKE_CASE_:List[str]             =						"""adapter_config.json"""
SCREAMING_SNAKE_CASE_:Tuple             =						"""adapter_model.bin"""
SCREAMING_SNAKE_CASE_:List[Any]             =						"""adapter_model.safetensors"""
SCREAMING_SNAKE_CASE_:Any             =						"""tf_model.h5"""
SCREAMING_SNAKE_CASE_:Dict             =						"""tf_model.h5.index.json"""
SCREAMING_SNAKE_CASE_:Dict             =						"""model.ckpt"""
SCREAMING_SNAKE_CASE_:Tuple             =						"""flax_model.msgpack"""
SCREAMING_SNAKE_CASE_:Tuple             =						"""flax_model.msgpack.index.json"""
SCREAMING_SNAKE_CASE_:Tuple             =						"""model.safetensors"""
SCREAMING_SNAKE_CASE_:Optional[Any]             =						"""model.safetensors.index.json"""
SCREAMING_SNAKE_CASE_:str             =						"""config.json"""
SCREAMING_SNAKE_CASE_:Optional[int]             =						"""preprocessor_config.json"""
SCREAMING_SNAKE_CASE_:Tuple             =						FEATURE_EXTRACTOR_NAME
SCREAMING_SNAKE_CASE_:int             =						"""generation_config.json"""
SCREAMING_SNAKE_CASE_:Union[str, Any]             =						"""modelcard.json"""

SCREAMING_SNAKE_CASE_:int             =						"""▁"""
SCREAMING_SNAKE_CASE_:int             =						SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

SCREAMING_SNAKE_CASE_:Dict             =						[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
SCREAMING_SNAKE_CASE_:Dict             =						[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
SCREAMING_SNAKE_CASE_:Tuple             =						[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Any:



    """simple docstring"""






    if version.parse(_lowerCAmelCase		) < version.parse(_lowerCAmelCase		):
        if "dev" in min_version:
            A :       Any          =   (
                """This example requires a source install from HuggingFace Transformers (see """
                """`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
            )
        else:
            A :       Dict          =   f'''This example requires a minimum version of {min_version},'''
        error_message += f''' but the version found is {__version__}.\n'''
        raise ImportError(
            error_message
            + """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
            """versions of HuggingFace Transformers."""		)







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
     from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
     from tensorflow.keras.optimizers import Adam






class SCREAMING_SNAKE_CASE__     (		tf.keras.optimizers.schedules.LearningRateSchedule    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = None,		):
        super().__init__()
        A :       Union[str, Any]          =   initial_learning_rate
        A :       List[Any]          =   warmup_steps
        A :       int          =   power
        A :       Optional[int]          =   decay_schedule_fn
        A :       int          =   name






    def __call__(       self,		lowerCamelCase__		):
        with tf.name_scope(self.name or """WarmUp"""		) as name:
            # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
            # learning rate will be `global_step/num_warmup_steps * init_lr`.
            A :       str          =   tf.cast(lowerCamelCase__,		tf.floataa		)
            A :       List[Any]          =   tf.cast(self.warmup_steps,		tf.floataa		)
            A :       Dict          =   global_step_float / warmup_steps_float
            A :       Union[str, Any]          =   self.initial_learning_rate * tf.math.pow(lowerCamelCase__,		self.power		)
            return tf.cond(
                global_step_float < warmup_steps_float,		lambda: warmup_learning_rate,		lambda: self.decay_schedule_fn(step - self.warmup_steps		),		name=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 0.9      ,     _lowerCAmelCase = 0.999      ,     _lowerCAmelCase = 1e-8      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 1.0      ,     _lowerCAmelCase = None      ,     )       ->  Union[str, Any]:



    """simple docstring"""






    A :       Optional[int]          =   tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=_lowerCAmelCase      ,     decay_steps=num_train_steps - num_warmup_steps      ,     end_learning_rate=init_lr * min_lr_ratio      ,     power=_lowerCAmelCase      ,     )
    if num_warmup_steps:
        A :       Dict          =   WarmUp(
            initial_learning_rate=_lowerCAmelCase      ,     decay_schedule_fn=_lowerCAmelCase      ,     warmup_steps=_lowerCAmelCase      ,     )
    if weight_decay_rate > 0.0:
        A :       str          =   AdamWeightDecay(
            learning_rate=_lowerCAmelCase      ,     weight_decay_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]      ,     include_in_weight_decay=_lowerCAmelCase      ,     )
    else:
        A :       Optional[int]          =   tf.keras.optimizers.Adam(
            learning_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     )
    # We return the optimizer and the LR scheduler in order to better track the
    # evolution of the LR independently of the optimizer.
    return optimizer, lr_schedule






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 0.001,		lowerCamelCase__ = 0.9,		lowerCamelCase__ = 0.999,		lowerCamelCase__ = 1e-7,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = "AdamWeightDecay",		**lowerCamelCase__,		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)
        A :       int          =   weight_decay_rate
        A :       Any          =   include_in_weight_decay
        A :       Dict          =   exclude_from_weight_decay






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__		):
        A :       Tuple          =   {"""WarmUp""": WarmUp}
        return super(lowerCamelCase__,		cls		).from_config(lowerCamelCase__,		custom_objects=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        super(lowerCamelCase__,		self		)._prepare_local(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A :       List[str]          =   tf.constant(
            self.weight_decay_rate,		name="""adam_weight_decay_rate"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   self._do_use_weight_decay(var.name		)
        if do_decay:
            return var.assign_sub(
                learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["""weight_decay_rate"""],		use_locking=self._use_locking,		)
        return tf.no_op()






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A     ,							A :       Dict          =   list(zip(*lowerCamelCase__		)		)
        return super(lowerCamelCase__,		self		).apply_gradients(zip(lowerCamelCase__,		lowerCamelCase__		),		name=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

        A :       Union[str, Any]          =   apply_state or {}
        A :       Optional[int]          =   apply_state.get((var_device, var_dtype)		)
        if coefficients is None:
            A :       Dict          =   self._fallback_apply_state(lowerCamelCase__,		lowerCamelCase__		)
            A :       List[Any]          =   coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       str          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Any          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_dense(lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       Tuple          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Optional[Any]          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_sparse(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   super().get_config()
        config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
        return config






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        if self.weight_decay_rate == 0:
            return False

        if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return True

        if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                if re.search(lowerCamelCase__,		lowerCamelCase__		) is not None:
                    return False
        return True






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self		):
        A :       List[str]          =   []
        A :       List[str]          =   None






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._accum_steps is None:
            A :       str          =   tf.Variable(
                tf.constant(0,		dtype=tf.intaa		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)

        return self._accum_steps.value()






    @property
    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            raise ValueError("""The accumulator should be called first to initialize the gradients"""		)
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






    def __call__(       self,		lowerCamelCase__		):
        if not self._gradients:
            A :       int          =   self.step  # Create the step variable.
            self._gradients.extend(
                [
                    tf.Variable(
                        tf.zeros_like(lowerCamelCase__		),		trainable=lowerCamelCase__,		synchronization=tf.VariableSynchronization.ON_READ,		aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,		)
                    if gradient is not None
                    else gradient
                    for gradient in gradients
                ]		)
        if len(lowerCamelCase__		) != len(self._gradients		):
            raise ValueError(f'''Expected {len(self._gradients		)} gradients, but got {len(lowerCamelCase__		)}'''		)

        for accum_gradient, gradient in zip(self._gradients,		lowerCamelCase__		):
            if accum_gradient is not None and gradient is not None:
                accum_gradient.assign_add(lowerCamelCase__		)

        self._accum_steps.assign_add(1		)






    def 			_lowerCAmelCase					(       self		):
        if not self._gradients:
            return
        self._accum_steps.assign(0		)
        for gradient in self._gradients:
            if gradient is not None:
                gradient.assign(tf.zeros_like(lowerCamelCase__		)		)


