
"""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:
								__UpperCAmelCase 					=    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)]
																)
																__UpperCAmelCase 					=    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      _SCREAMING_SNAKE_CASE   :
			UpperCAmelCase_    :int												=		None
			UpperCAmelCase_    :int												=		()
			UpperCAmelCase_    :int												=		()
			UpperCAmelCase_    :List[str]												=		True
			UpperCAmelCase_    :List[Any]												=		True
			UpperCAmelCase_    :Optional[Any]												=		True
			UpperCAmelCase_    :List[Any]												=		False






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A=False			)     ->	dict:
						lowerCAmelCase_       :int     	=    copy.deepcopy(__lowerCAmelCase			)

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

						if return_labels:
									if model_class in get_values(__lowerCAmelCase			):
												lowerCAmelCase_       :str     	=    tf.ones(self.model_tester.batch_size			,  dtype=tf.intaa			)
									elif model_class in get_values(__lowerCAmelCase			):
												lowerCAmelCase_       :str     	=    tf.zeros(self.model_tester.batch_size			,  dtype=tf.intaa			)
												lowerCAmelCase_       :List[Any]     	=    tf.zeros(self.model_tester.batch_size			,  dtype=tf.intaa			)
									elif model_class in [
									    *get_values(__lowerCAmelCase			),
									    *get_values(__lowerCAmelCase			),
									]:
												lowerCAmelCase_       :str     	=    tf.zeros(self.model_tester.batch_size			,  dtype=tf.intaa			)
									elif model_class in get_values(__lowerCAmelCase			):
												lowerCAmelCase_       :List[str]     	=    tf.zeros(self.model_tester.batch_size			,  dtype=tf.intaa			)
									elif model_class in [
									    *get_values(__lowerCAmelCase			),
									    *get_values(__lowerCAmelCase			),
									    *get_values(__lowerCAmelCase			),
									    *get_values(__lowerCAmelCase			),
									    *get_values(__lowerCAmelCase			),
									]:
												lowerCAmelCase_       :int     	=    tf.zeros(
												    (self.model_tester.batch_size, self.model_tester.seq_length)			,  dtype=tf.intaa			)
						return inputs_dict






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	str:
						lowerCAmelCase_	,			lowerCAmelCase_       :Tuple     	=    self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes[:2]:
									lowerCAmelCase_       :Optional[Any]     	=    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase			)
									lowerCAmelCase_       :Tuple     	=    model_class(__lowerCAmelCase			)

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

									lowerCAmelCase_       :Optional[Any]     	=    run_in_graph_mode()
									self.assertIsNotNone(__lowerCAmelCase			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_	,			lowerCAmelCase_       :str     	=    self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes[:2]:
									lowerCAmelCase_       :Union[str, Any]     	=    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase			)
									lowerCAmelCase_       :Union[str, Any]     	=    model_class(__lowerCAmelCase			)

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

									lowerCAmelCase_       :str     	=    run_in_graph_mode()
									self.assertIsNotNone(__lowerCAmelCase			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_	,			lowerCAmelCase_       :Tuple     	=    self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes[:2]:
									lowerCAmelCase_       :Optional[int]     	=    model_class(__lowerCAmelCase			)
									if getattr(__lowerCAmelCase			,  """hf_compute_loss"""			,  __lowerCAmelCase			):
												# Test that model correctly compute the loss with kwargs
												lowerCAmelCase_       :Union[str, Any]     	=    self._prepare_for_class(inputs_dict.copy()			,  __lowerCAmelCase			,  return_labels=__lowerCAmelCase			)
												# Is there a better way to remove these decoder inputs?
												lowerCAmelCase_       :Tuple     	=    {
												    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""")
												}

												lowerCAmelCase_       :Any     	=    {
												    """labels""",
												    """label""",
												    """label_ids""",
												    """start_positions""",
												    """start_position""",
												    """end_positions""",
												    """end_position""",
												    """next_sentence_label""",
												}
												lowerCAmelCase_       :List[Any]     	=    possible_label_cols.intersection(set(__lowerCAmelCase			)			)
												self.assertGreater(len(__lowerCAmelCase			)			,  0			,  msg="""No matching label names found!"""			)
												lowerCAmelCase_       :Union[str, Any]     	=    {key: val for key, val in prepared_for_class.items() if key in label_names}
												lowerCAmelCase_       :int     	=    {key: val for key, val in prepared_for_class.items() if key not in label_names}
												self.assertGreater(len(__lowerCAmelCase			)			,  0			)

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

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

												lowerCAmelCase_       :int     	=    history.history["""loss"""][0]
												self.assertTrue(not isnan(__lowerCAmelCase			)			)
												lowerCAmelCase_       :str     	=    history.history["""val_loss"""][0]
												self.assertTrue(not isnan(__lowerCAmelCase			)			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						lowerCAmelCase_	,			lowerCAmelCase_       :List[Any]     	=    self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase_       :Union[str, Any]     	=    True
						lowerCAmelCase_       :Union[str, Any]     	=    True

						if hasattr(__lowerCAmelCase			,  """use_cache"""			):
									lowerCAmelCase_       :str     	=    True

						lowerCAmelCase_       :List[str]     	=    getattr(self.model_tester			,  """encoder_seq_length"""			,  self.model_tester.seq_length			)
						lowerCAmelCase_       :int     	=    getattr(self.model_tester			,  """key_length"""			,  __lowerCAmelCase			)

						for model_class in self.all_model_classes[:2]:
									lowerCAmelCase_       :Any     	=    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase			)
									lowerCAmelCase_       :Dict     	=    model_class(__lowerCAmelCase			)
									model.build()
									lowerCAmelCase_       :str     	=    len(model(__lowerCAmelCase			)			)

									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:
															lowerCAmelCase_       :List[str]     	=    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(__lowerCAmelCase			,  saved_model=__lowerCAmelCase			)
												lowerCAmelCase_       :List[Any]     	=    os.path.join(__lowerCAmelCase			,  """saved_model"""			,  """1"""			)
												lowerCAmelCase_       :str     	=    tf.keras.models.load_model(__lowerCAmelCase			)
												lowerCAmelCase_       :Optional[int]     	=    model(__lowerCAmelCase			)

												if self.is_encoder_decoder:
															lowerCAmelCase_       :int     	=    outputs["""encoder_hidden_states"""]
															lowerCAmelCase_       :Union[str, Any]     	=    outputs["""encoder_attentions"""]
												else:
															lowerCAmelCase_       :Tuple     	=    outputs["""hidden_states"""]
															lowerCAmelCase_       :Any     	=    outputs["""attentions"""]

												self.assertEqual(len(__lowerCAmelCase			)			,  __lowerCAmelCase			)

												lowerCAmelCase_       :Optional[int]     	=    getattr(
												    self.model_tester			,  """expected_num_hidden_layers"""			,  self.model_tester.num_hidden_layers + 1			)

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

												self.assertEqual(len(__lowerCAmelCase			)			,  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 					__lowerCAmelCase					(   self			)     ->	Tuple:
						tf.keras.mixed_precision.set_global_policy("""mixed_float16"""			)

						# try/finally block to ensure subsequent tests run in float32
						try:
									lowerCAmelCase_	,			lowerCAmelCase_       :List[str]     	=    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
												lowerCAmelCase_       :Optional[int]     	=    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase			)
												lowerCAmelCase_       :Optional[Any]     	=    model_class(__lowerCAmelCase			)
												lowerCAmelCase_       :Optional[int]     	=    model(__lowerCAmelCase			)

												self.assertIsNotNone(__lowerCAmelCase			)
						finally:
									tf.keras.mixed_precision.set_global_policy("""float32"""			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						lowerCAmelCase_	,			lowerCAmelCase_       :int     	=    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"]
						lowerCAmelCase_       :Optional[int]     	=    {
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__			),)
						    for module_member_name in dir(__lowerCAmelCase			)
						    if module_member_name.endswith("""MainLayer"""			)
						    for module_member in (getattr(__lowerCAmelCase			,  __lowerCAmelCase			),)
						    if isinstance(__lowerCAmelCase			,  __lowerCAmelCase			)
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(__lowerCAmelCase			,  """_keras_serializable"""			,  __lowerCAmelCase			)
						}

						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
												lowerCAmelCase_       :Optional[Any]     	=    TFSharedEmbeddings(self.model_tester.vocab_size			,  self.model_tester.hidden_size			,  name="""shared"""			)
												lowerCAmelCase_       :Union[str, Any]     	=    False
												lowerCAmelCase_       :List[str]     	=    main_layer_class(__lowerCAmelCase			,  embed_tokens=__lowerCAmelCase			)
									else:
												lowerCAmelCase_       :Tuple     	=    main_layer_class(__lowerCAmelCase			)

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

									if hasattr(self.model_tester			,  """num_labels"""			):
												lowerCAmelCase_       :Tuple     	=    self.model_tester.num_labels
									else:
												lowerCAmelCase_       :Tuple     	=    2

									lowerCAmelCase_       :int     	=    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			)

									lowerCAmelCase_       :List[Any]     	=    main_layer(__lowerCAmelCase			)[0]
									lowerCAmelCase_       :List[Any]     	=    tf.keras.layers.Dense(__lowerCAmelCase			,  activation="""softmax"""			,  name="""outputs"""			)(__lowerCAmelCase			)
									lowerCAmelCase_       :str     	=    tf.keras.models.Model(inputs=__lowerCAmelCase			,  outputs=[outputs]			)

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

									with tempfile.TemporaryDirectory() as tmpdirname:
												lowerCAmelCase_       :List[str]     	=    os.path.join(__lowerCAmelCase			,  """keras_model.h5"""			)
												model.save(__lowerCAmelCase			)
												if "T5" in main_layer_class.__name__:
															lowerCAmelCase_       :int     	=    tf.keras.models.load_model(
															    __lowerCAmelCase			,  custom_objects={
															        main_layer_class.__name__: main_layer_class,
															        """TFSharedEmbeddings""": TFSharedEmbeddings,
															    }			,  )
												else:
															lowerCAmelCase_       :Union[str, Any]     	=    tf.keras.models.load_model(
															    __lowerCAmelCase			,  custom_objects={main_layer_class.__name__: main_layer_class}			)
												assert isinstance(__lowerCAmelCase			,  tf.keras.Model			)
												model(__lowerCAmelCase			)






			@slow
			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_	,			lowerCAmelCase_       :Tuple     	=    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes[:2]:
									lowerCAmelCase_       :int     	=    model_class(__lowerCAmelCase			)

									lowerCAmelCase_       :Optional[int]     	=    copy.deepcopy(__lowerCAmelCase			)

									if not self.is_encoder_decoder:
												lowerCAmelCase_       :int     	=    inputs["""input_ids"""]
												del inputs["input_ids"]
									else:
												lowerCAmelCase_       :Union[str, Any]     	=    inputs["""input_ids"""]
												lowerCAmelCase_       :Any     	=    inputs.get("""decoder_input_ids"""			,  __lowerCAmelCase			)
												del inputs["input_ids"]
												inputs.pop("""decoder_input_ids"""			,  __lowerCAmelCase			)

									if not self.is_encoder_decoder:
												lowerCAmelCase_       :Any     	=    model.get_input_embeddings()(__lowerCAmelCase			)
									else:
												lowerCAmelCase_       :Any     	=    model.get_input_embeddings()(__lowerCAmelCase			)
												lowerCAmelCase_       :int     	=    model.get_input_embeddings()(__lowerCAmelCase			)

									lowerCAmelCase_       :Optional[Any]     	=    self._prepare_for_class(__lowerCAmelCase			,  __lowerCAmelCase			)

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

									lowerCAmelCase_       :List[str]     	=    run_in_graph_mode()
									self.assertIsNotNone(__lowerCAmelCase			)






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	List[str]:
						lowerCAmelCase_       :int     	=    []
						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
						lowerCAmelCase_       :Optional[Any]     	=    []
						while len(__lowerCAmelCase			) < num_bad_tokens:
									lowerCAmelCase_       :Dict     	=    tf.squeeze(ids_tensor((1, 1)			,  self.model_tester.vocab_size			)			,  0			).numpy()[0]
									if token not in special_tokens:
												bad_tokens.append(__lowerCAmelCase			)
						return bad_tokens






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	int:






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






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	int:






						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(__lowerCAmelCase			)			,  len(__lowerCAmelCase			)			):
															# if tokens match
															if generated_ids_slice[i - len(__lowerCAmelCase			) : i] == bad_word_ids:
																		return True
						return False











import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A_		:  int										=  '''src/transformers'''

# Matches is_xxx_available()
A_		:  Union[str, Any]										=  re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
A_		:  int										=  re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
A_		:  Tuple										=  re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


A_		:  str										=  '''
{0} = None
'''

A_		:  Tuple										=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


A_		:  Any										=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]       ):






 '''simple docstring'''

 if _re_test_backend.search(UpperCAmelCase__       ) is None:
  return None
 a							=		[b[0] for b in _re_backend.findall(UpperCAmelCase__       )]
 backends.sort()
 return "_and_".join(UpperCAmelCase__       )


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 with open(os.path.join(UpperCAmelCase__			,       "__init__.py"       )			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
  a							=		f.readlines()

 # Get to the point we do the actual imports for type checking
 a							=		0
 while not lines[line_index].startswith("if TYPE_CHECKING"       ):
  line_index += 1

 a							=		{}
 # Go through the end of the file
 while line_index < len(UpperCAmelCase__       ):
  # If the line is an if is_backend_available, we grab all objects associated.
  a							=		find_backend(lines[line_index]       )
  if backend is not None:
   while not lines[line_index].startswith("    else:"       ):
    line_index += 1
   line_index += 1

   a							=		[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]       ) <= 1 or lines[line_index].startswith(" " * 8       ):
    a							=		lines[line_index]
    a							=		_re_single_line_import.search(UpperCAmelCase__       )
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(", "       )       )
    elif line.startswith(" " * 12       ):
     objects.append(line[12:-2]       )
    line_index += 1

   a							=		objects
  else:
   line_index += 1

 return backend_specific_objects


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 if name.isupper():
  return DUMMY_CONSTANT.format(UpperCAmelCase__       )
 elif name.islower():
  return DUMMY_FUNCTION.format(UpperCAmelCase__			,       UpperCAmelCase__       )
 else:
  return DUMMY_CLASS.format(UpperCAmelCase__			,       UpperCAmelCase__       )


def 						UpperCAmelCase__   (   UpperCAmelCase__			:List[str]=None       ):






 '''simple docstring'''

 if backend_specific_objects is None:
  a							=		read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 a							=		{}

 for backend, objects in backend_specific_objects.items():
  a							=		"[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"       )       ) + "]"
  a							=		"# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(UpperCAmelCase__			,       UpperCAmelCase__       ) for o in objects]       )
  a							=		dummy_file

 return dummy_files


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int=False       ):






 '''simple docstring'''

 a							=		create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 a							=		{"torch": "pt"}

 # Locate actual dummy modules and read their content.
 a							=		os.path.join(UpperCAmelCase__			,       "utils"       )
 a							=		{
     backend: os.path.join(UpperCAmelCase__			,       F"""dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py"""       )
     for backend in dummy_files.keys()
 }

 a							=		{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(UpperCAmelCase__       ):
   with open(UpperCAmelCase__			,       "r"			,       encoding="utf-8"			,       newline="\n"       ) as f:
    a							=		f.read()
  else:
   a							=		""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F"""Updating transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py as the main """
        "__init__ has new objects."       )
    with open(dummy_file_paths[backend]			,       "w"			,       encoding="utf-8"			,       newline="\n"       ) as f:
     f.write(dummy_files[backend]       )
   else:
    raise ValueError(
        "The main __init__ has objects that are not present in "
        F"""transformers.utils.dummy_{short_names.get(UpperCAmelCase__			,       UpperCAmelCase__       )}_objects.py. Run `make fix-copies` """
        "to fix this."       )


if __name__ == "__main__":
     A_		:  Dict										=  argparse.ArgumentParser()
     parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
     A_		:  List[str]										=  parser.parse_args()

     check_dummies(args.fix_and_overwrite)

