




'''simple docstring'''







from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        TFSharedEmbeddings,
    )

    if _tf_gpu_memory_limit is not None:
        lowerCAmelCase_    	=							tf.config.list_physical_devices('''GPU''')
        for gpu in gpus:
            # Restrict TensorFlow to only allocate x GB of memory on the GPUs
            try:
                tf.config.set_logical_device_configuration(
                    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                )
                lowerCAmelCase_    	=							tf.config.list_logical_devices('''GPU''')
                print('''Logical GPUs''', logical_gpus)
            except RuntimeError as e:
                # Virtual devices must be set before GPUs have been initialized
                print(e)

@require_tf
class   _snake_case:
 __snake_case:       Any									=				None
 __snake_case:       str									=				()
 __snake_case:       List[Any]									=				()
 __snake_case:       List[Any]									=				True
 __snake_case:       Optional[int]									=				True
 __snake_case:       Any									=				True
 __snake_case:       List[str]									=				False






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     Union[str, Any]						,		a					:     Union[str, Any]						,		a					:     Dict=False						)					->      Tuple:






    """simple docstring"""


    A__         =    copy.deepcopy(a_						)

    if model_class in get_values(a_						):
       A__         =    {
           k: tf.tile(tf.expand_dims(a_						,		1						)						,		(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)						)
           if isinstance(a_						,		tf.Tensor						) and v.ndim > 0
           else v
           for k, v in inputs_dict.items()
       }

    if return_labels:
       if model_class in get_values(a_						):
          A__         =    tf.ones(self.model_tester.batch_size						,		dtype=tf.intaa						)
       elif model_class in get_values(a_						):
          A__         =    tf.zeros(self.model_tester.batch_size						,		dtype=tf.intaa						)
          A__         =    tf.zeros(self.model_tester.batch_size						,		dtype=tf.intaa						)
       elif model_class in [
           *get_values(a_						),
           *get_values(a_						),
       ]:
          A__         =    tf.zeros(self.model_tester.batch_size						,		dtype=tf.intaa						)
       elif model_class in get_values(a_						):
          A__         =    tf.zeros(self.model_tester.batch_size						,		dtype=tf.intaa						)
       elif model_class in [
           *get_values(a_						),
           *get_values(a_						),
           *get_values(a_						),
           *get_values(a_						),
           *get_values(a_						),
       ]:
          A__         =    tf.zeros(
              (self.model_tester.batch_size, self.model_tester.seq_length)						,		dtype=tf.intaa						)
    return inputs_dict






 @slow
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      Union[str, Any]:






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
       A__         =    self._prepare_for_class(a_						,		a_						)
       A__         =    model_class(a_						)

       @tf.function
       def run_in_graph_mode():
          return model(a_						)

       A__         =    run_in_graph_mode()
       self.assertIsNotNone(a_						)






 @slow
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      List[Any]:






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
       A__         =    self._prepare_for_class(a_						,		a_						)
       A__         =    model_class(a_						)

       @tf.function(experimental_compile=a_						)
       def run_in_graph_mode():
          return model(a_						)

       A__         =    run_in_graph_mode()
       self.assertIsNotNone(a_						)






 @slow
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      Optional[Any]:






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
       A__         =    model_class(a_						)
       if getattr(a_						,		'hf_compute_loss'						,		a_						):
          # Test that model correctly compute the loss with kwargs
          A__         =    self._prepare_for_class(inputs_dict.copy()						,		a_						,		return_labels=a_						)
          # Is there a better way to remove these decoder inputs?
          A__         =    {
              key: val
              for key, val in prepared_for_class.items()
              if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
          }

          A__         =    {
              'labels',
              'label',
              'label_ids',
              'start_positions',
              'start_position',
              'end_positions',
              'end_position',
              'next_sentence_label',
          }
          A__         =    possible_label_cols.intersection(set(a_						)						)
          self.assertGreater(len(a_						)						,		0						,		msg='No matching label names found!'						)
          A__         =    {key: val for key, val in prepared_for_class.items() if key in label_names}
          A__         =    {key: val for key, val in prepared_for_class.items() if key not in label_names}
          self.assertGreater(len(a_						)						,		0						)

          # Make sure it works with XLA!
          model.compile(optimizer=tf.keras.optimizers.SGD(0.0						)						,		jit_compile=a_						)
          # Make sure the model fits without crashing regardless of where we pass the labels
          A__         =    model.fit(
              a_						,		validation_data=a_						,		steps_per_epoch=1						,		validation_steps=1						,		shuffle=a_						,		verbose=0						,		)
          A__         =    history.history['loss'][0]
          self.assertTrue(not isnan(a_						)						)
          A__         =    history.history['val_loss'][0]
          self.assertTrue(not isnan(a_						)						)

          # Now test it with separate labels, to make sure that path works in XLA too.
          A__         =    model_class(a_						)
          model.compile(optimizer=tf.keras.optimizers.SGD(0.0						)						,		jit_compile=a_						)
          A__         =    model.fit(
              a_						,		a_						,		validation_data=(inputs_minus_labels, labels)						,		steps_per_epoch=1						,		validation_steps=1						,		shuffle=a_						,		verbose=0						,		)

          A__         =    history.history['loss'][0]
          self.assertTrue(not isnan(a_						)						)
          A__         =    history.history['val_loss'][0]
          self.assertTrue(not isnan(a_						)						)






 @slow
 def 	_UpperCamelCase   (self					:     Tuple						)					->      Dict:






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()
    A__         =    True
    A__         =    True

    if hasattr(a_						,		'use_cache'						):
       A__         =    True

    A__         =    getattr(self.model_tester						,		'encoder_seq_length'						,		self.model_tester.seq_length						)
    A__         =    getattr(self.model_tester						,		'key_length'						,		a_						)

    for model_class in self.all_model_classes[:2]:
       A__         =    self._prepare_for_class(a_						,		a_						)
       A__         =    model_class(a_						)
       model.build()
       A__         =    len(model(a_						)						)

       for key in list(class_inputs_dict.keys()						):
          # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
          if key not in model.input_signature:
             del class_inputs_dict[key]
          # Check it's a tensor, in case the inputs dict has some bools in it too
          elif isinstance(class_inputs_dict[key]						,		tf.Tensor						) and class_inputs_dict[key].dtype.is_integer:
             A__         =    tf.cast(class_inputs_dict[key]						,		tf.intaa						)

       if set(class_inputs_dict.keys()						) != set(model.input_signature.keys()						):
          continue  # Some models have inputs that the preparation functions don't create, we skip those

       with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(a_						,		saved_model=a_						)
          A__         =    os.path.join(a_						,		'saved_model'						,		'1'						)
          A__         =    tf.keras.models.load_model(a_						)
          A__         =    model(a_						)

          if self.is_encoder_decoder:
             A__         =    outputs['encoder_hidden_states']
             A__         =    outputs['encoder_attentions']
          else:
             A__         =    outputs['hidden_states']
             A__         =    outputs['attentions']

          self.assertEqual(len(a_						)						,		a_						)

          A__         =    getattr(
              self.model_tester						,		'expected_num_hidden_layers'						,		self.model_tester.num_hidden_layers + 1						)

          self.assertEqual(len(a_						)						,		a_						)
          self.assertListEqual(
              list(output_hidden_states[0].shape[-2:]						)						,		[self.model_tester.seq_length, self.model_tester.hidden_size]						,		)

          self.assertEqual(len(a_						)						,		self.model_tester.num_hidden_layers						)
          self.assertListEqual(
              list(output_attentions[0].shape[-3:]						)						,		[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						,		)






 @slow
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      Optional[Any]:






    """simple docstring"""


    tf.keras.mixed_precision.set_global_policy('mixed_float16'						)

    # try/finally block to ensure subsequent tests run in float32
    try:
       A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
          A__         =    self._prepare_for_class(a_						,		a_						)
          A__         =    model_class(a_						)
          A__         =    model(a_						)

          self.assertIsNotNone(a_						)
    finally:
       tf.keras.mixed_precision.set_global_policy('float32'						)






 @slow
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Union[str, Any]:






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()
    # head_mask and decoder_head_mask has different shapes than other input args
    if "head_mask" in inputs_dict:
       del inputs_dict["head_mask"]
    if "decoder_head_mask" in inputs_dict:
       del inputs_dict["decoder_head_mask"]
    if "cross_attn_head_mask" in inputs_dict:
       del inputs_dict["cross_attn_head_mask"]
    A__         =    {
        module_member
        for model_class in self.all_model_classes
        for module in (import_module(model_class.__module__						),)
        for module_member_name in dir(a_						)
        if module_member_name.endswith('MainLayer'						)
        for module_member in (getattr(a_						,		a_						),)
        if isinstance(a_						,		a_						)
        and tf.keras.layers.Layer in module_member.__bases__
        and getattr(a_						,		'_keras_serializable'						,		a_						)
    }

    for main_layer_class in tf_main_layer_classes:
       # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
       if "T5" in main_layer_class.__name__:
          # Take the same values than in TFT5ModelTester for this shared layer
          A__         =    TFSharedEmbeddings(self.model_tester.vocab_size						,		self.model_tester.hidden_size						,		name='shared'						)
          A__         =    False
          A__         =    main_layer_class(a_						,		embed_tokens=a_						)
       else:
          A__         =    main_layer_class(a_						)

       A__         =    {
           name: tf.keras.Input(tensor.shape[1:]						,		dtype=tensor.dtype						) for name, tensor in inputs_dict.items()
       }

       if hasattr(self.model_tester						,		'num_labels'						):
          A__         =    self.model_tester.num_labels
       else:
          A__         =    2

       A__         =    tf.data.Dataset.from_tensor_slices(
           (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)						))						).batch(1						)

       A__         =    main_layer(a_						)[0]
       A__         =    tf.keras.layers.Dense(a_						,		activation='softmax'						,		name='outputs'						)(a_						)
       A__         =    tf.keras.models.Model(inputs=a_						,		outputs=[outputs]						)

       model.compile(loss='binary_crossentropy'						,		optimizer='adam'						,		metrics=['binary_accuracy']						)
       model.fit(a_						,		epochs=1						)

       with tempfile.TemporaryDirectory() as tmpdirname:
          A__         =    os.path.join(a_						,		'keras_model.h5'						)
          model.save(a_						)
          if "T5" in main_layer_class.__name__:
             A__         =    tf.keras.models.load_model(
                 a_						,		custom_objects={
                     main_layer_class.__name__: main_layer_class,
                     'TFSharedEmbeddings': TFSharedEmbeddings,
                 }						,		)
          else:
             A__         =    tf.keras.models.load_model(
                 a_						,		custom_objects={main_layer_class.__name__: main_layer_class}						)
          assert isinstance(a_						,		tf.keras.Model						)
          model(a_						)






 @slow
 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      List[Any]:






    """simple docstring"""


    A__     , A__         =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes[:2]:
       A__         =    model_class(a_						)

       A__         =    copy.deepcopy(a_						)

       if not self.is_encoder_decoder:
          A__         =    inputs['input_ids']
          del inputs["input_ids"]
       else:
          A__         =    inputs['input_ids']
          A__         =    inputs.get('decoder_input_ids'						,		a_						)
          del inputs["input_ids"]
          inputs.pop('decoder_input_ids'						,		a_						)

       if not self.is_encoder_decoder:
          A__         =    model.get_input_embeddings()(a_						)
       else:
          A__         =    model.get_input_embeddings()(a_						)
          A__         =    model.get_input_embeddings()(a_						)

       A__         =    self._prepare_for_class(a_						,		a_						)

       @tf.function
       def run_in_graph_mode():
          return model(a_						)

       A__         =    run_in_graph_mode()
       self.assertIsNotNone(a_						)






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     str						,		a					:     Optional[Any]						)					->      Any:






    """simple docstring"""


    A__         =    []
    if model.config.bos_token_id is not None:
       special_tokens.append(model.config.bos_token_id						)
    if model.config.pad_token_id is not None:
       special_tokens.append(model.config.pad_token_id						)
    if model.config.eos_token_id is not None:
       special_tokens.append(model.config.eos_token_id						)

    # create random bad tokens that are not special tokens
    A__         =    []
    while len(a_						) < num_bad_tokens:
       A__         =    tf.squeeze(ids_tensor((1, 1)						,		self.model_tester.vocab_size						)						,		0						).numpy()[0]
       if token not in special_tokens:
          bad_tokens.append(a_						)
    return bad_tokens






 def 	_UpperCamelCase   (self					:     List[Any]						,		a					:     List[Any]						)					->      Optional[int]:






    """simple docstring"""


    for token_id in output_ids[0].numpy().tolist():
       self.assertGreaterEqual(a_						,		0						)
       self.assertLess(a_						,		self.model_tester.vocab_size						)






 def 	_UpperCamelCase   (self					:     Optional[Any]						,		a					:     List[Any]						,		a					:     int						)					->      int:






    """simple docstring"""


    for bad_word_ids in bad_words_ids:
       # for all slices in batch
       for generated_ids_slice in generated_ids:
          # for all word idx
          for i in range(len(a_						)						,		len(a_						)						):
             # if tokens match
             if generated_ids_slice[i - len(a_						) : i] == bad_word_ids:
                return True
    return False








'''simple docstring'''







import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowerCAmelCase_    	=							logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowerCAmelCase_    	=							direct_transformers_import(Path(__file__).parent)


lowerCAmelCase_    	=							{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}

class   _snake_case( UpperCAmelCase			):
 __snake_case:       str									=				['''feature_extractor''', '''tokenizer''']
 # Names need to be attr_class for attr in attributes
 __snake_case:       Optional[Any]									=				None
 __snake_case:       List[str]									=				None
 __snake_case:       Any									=				None






 def __init__(self					:     List[Any]						,		*a					:     Union[str, Any]						,		**a					:     Optional[int]						)					->      Any:






    """simple docstring"""


    for key in kwargs:
       if key not in self.attributes:
          raise TypeError(f"""Unexpected keyword argument {key}."""						)
    for arg, attribute_name in zip(a						,		self.attributes						):
       if attribute_name in kwargs:
          raise TypeError(f"""Got multiple values for argument {attribute_name}."""						)
       else:
          A__         =    arg

    if len(a						) != len(self.attributes						):
       raise ValueError(
           f"""This processor requires {len(self.attributes						)} arguments: {", ".join(self.attributes						)}. Got """
           f"""{len(a						)} arguments instead."""						)

    # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
    for attribute_name, arg in kwargs.items():
       A__         =    getattr(self						,		f"""{attribute_name}_class"""						)
       # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
       A__         =    AUTO_TO_BASE_CLASS_MAPPING.get(a						,		a						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) for n in class_name if n is not None						)
       else:
          A__         =    getattr(a						,		a						)

       if not isinstance(a						,		a						):
          raise ValueError(
              f"""Received a {type(a						).__name__} for argument {attribute_name}, but a {class_name} was expected."""						)

       setattr(self						,		a						,		a						)






 def __repr__(self					:     Optional[int]						)					->      Any:






    """simple docstring"""


    A__         =    [f"""- {name}: {repr(getattr(self						,		a						)						)}""" for name in self.attributes]
    A__         =    '\n'.join(a						)
    return f"""{self.__class__.__name__}:\n{attributes_repr}"""






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Tuple						,		a					:     bool = False						,		**a					:     int						)					->      List[str]:






    """simple docstring"""


    os.makedirs(a						,		exist_ok=a						)

    if push_to_hub:
       A__         =    kwargs.pop('commit_message'						,		a						)
       A__         =    kwargs.pop('repo_id'						,		save_directory.split(os.path.sep						)[-1]						)
       A__         =    self._create_repo(a						,		**a						)
       A__         =    self._get_files_timestamps(a						)
    # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
    # loaded from the Hub.
    if self._auto_class is not None:
       A__         =    [getattr(self						,		a						) for attribute_name in self.attributes]
       A__         =    [(a.init_kwargs if isinstance(a						,		a						) else a) for a in attrs]
       custom_object_save(self						,		a						,		config=a						)

    for attribute_name in self.attributes:
       A__         =    getattr(self						,		a						)
       # Include the processor class in the attribute config so this processor can then be reloaded with the
       # `AutoProcessor` API.
       if hasattr(a						,		'_set_processor_class'						):
          attribute._set_processor_class(self.__class__.__name__						)
       attribute.save_pretrained(a						)

    if self._auto_class is not None:
       # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
       for attribute_name in self.attributes:
          A__         =    getattr(self						,		a						)
          if isinstance(a						,		a						):
             del attribute.init_kwargs["auto_map"]

    if push_to_hub:
       self._upload_modified_files(
           a						,		a						,		a						,		commit_message=a						,		token=kwargs.get('use_auth_token'						)						,		)






 @classmethod
 def 	_UpperCamelCase   (cls					:     int						,		a					:     Union[str, os.PathLike]						,		a					:     Optional[Union[str, os.PathLike]] = None						,		a					:     bool = False						,		a					:     bool = False						,		a					:     Optional[Union[str, bool]] = None						,		a					:     str = "main"						,		**a					:     Union[str, Any]						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    cache_dir
    A__         =    force_download
    A__         =    local_files_only
    A__         =    revision

    A__         =    kwargs.pop('use_auth_token'						,		a						)
    if use_auth_token is not None:
       warnings.warn(
           'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'						,		a						)
       if token is not None:
          raise ValueError(
              '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'						)
       A__         =    use_auth_token

    if token is not None:
       # change to `token` in a follow-up PR
       A__         =    token

    A__         =    cls._get_arguments_from_pretrained(a						,		**a						)
    return cls(*a						)






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[int]						,		a					:     Union[str, Any]="AutoProcessor"						)					->      Any:






    """simple docstring"""


    if not isinstance(a						,		a						):
       A__         =    auto_class.__name__

    import transformers.models.auto as auto_module

    if not hasattr(a						,		a						):
       raise ValueError(f"""{auto_class} is not a valid auto class."""						)

    A__         =    auto_class






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[Any]						,		a					:     Optional[Any]						,		**a					:     Tuple						)					->      int:






    """simple docstring"""


    A__         =    []
    for attribute_name in cls.attributes:
       A__         =    getattr(cls						,		f"""{attribute_name}_class"""						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) if n is not None else None for n in class_name						)
          A__         =    kwargs.get('use_fast'						,		a						)
          if use_fast and classes[1] is not None:
             A__         =    classes[1]
          else:
             A__         =    classes[0]
       else:
          A__         =    getattr(a						,		a						)

       args.append(attribute_class.from_pretrained(a						,		**a						)						)
    return args






 @property
 def 	_UpperCamelCase   (self					:     str						)					->      List[str]:






    """simple docstring"""


    A__         =    getattr(self						,		self.attributes[0]						)
    return getattr(a						,		'model_input_names'						,		a						)




lowerCAmelCase_    	=							copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    lowerCAmelCase_    	=							ProcessorMixin.push_to_hub.__doc__.format(
        object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
    )


