



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFXLMForMultipleChoice,
			    TFXLMForQuestionAnsweringSimple,
			    TFXLMForSequenceClassification,
			    TFXLMForTokenClassification,
			    TFXLMModel,
			    TFXLMWithLMHeadModel,
			    XLMConfig,
			)
class       _lowerCAmelCase :






			"""simple docstring"""





			def __init__(					self						:							Tuple      ,  lowercase						:							str      ,  )    ->       str:
									"""simple docstring"""







									__lowercase											=					parent
									__lowercase											=					13
									__lowercase											=					7
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					True
									__lowercase											=					False
									__lowercase											=					False
									__lowercase											=					False
									__lowercase											=					2
									__lowercase											=					99
									__lowercase											=					0
									__lowercase											=					32
									__lowercase											=					2
									__lowercase											=					4
									__lowercase											=					0.1
									__lowercase											=					0.1
									__lowercase											=					512
									__lowercase											=					16
									__lowercase											=					2
									__lowercase											=					0.02
									__lowercase											=					3
									__lowercase											=					4
									__lowercase											=					"""last"""
									__lowercase											=					True
									__lowercase											=					None
									__lowercase											=					0





			def 		snake_case__      (					self						:							Dict )    ->       Tuple:
									"""simple docstring"""







									__lowercase											=					ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )
									__lowercase											=					random_attention_mask([self.batch_size, self.seq_length]      ,  dtype=tf.floataa )

									__lowercase											=					None
									if self.use_input_lengths:
															__lowercase											=					(
															    ids_tensor([self.batch_size]      ,  vocab_size=2 ) + self.seq_length - 2
															)  # small variation of seq_length

									__lowercase											=					None
									if self.use_token_type_ids:
															__lowercase											=					ids_tensor([self.batch_size, self.seq_length]      ,  self.n_langs )

									__lowercase											=					None
									__lowercase											=					None
									__lowercase											=					None
									if self.use_labels:
															__lowercase											=					ids_tensor([self.batch_size]      ,  self.type_sequence_label_size )
															__lowercase											=					ids_tensor([self.batch_size, self.seq_length]      ,  self.num_labels )
															__lowercase											=					ids_tensor([self.batch_size]      ,  2      ,  dtype=tf.floataa )
															__lowercase											=					ids_tensor([self.batch_size]      ,  self.num_choices )

									__lowercase											=					XLMConfig(
									    vocab_size=self.vocab_size      ,  n_special=self.n_special      ,  emb_dim=self.hidden_size      ,  n_layers=self.num_hidden_layers      ,  n_heads=self.num_attention_heads      ,  dropout=self.hidden_dropout_prob      ,  attention_dropout=self.attention_probs_dropout_prob      ,  gelu_activation=self.gelu_activation      ,  sinusoidal_embeddings=self.sinusoidal_embeddings      ,  asm=self.asm      ,  causal=self.causal      ,  n_langs=self.n_langs      ,  max_position_embeddings=self.max_position_embeddings      ,  initializer_range=self.initializer_range      ,  summary_type=self.summary_type      ,  use_proj=self.use_proj      ,  bos_token_id=self.bos_token_id      ,  )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)





			def 		snake_case__      (					self						:							List[str]      ,  lowercase						:							Tuple      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							int      ,  lowercase						:							List[str]      ,  )    ->       Dict:
									"""simple docstring"""







									__lowercase											=					TFXLMModel(config=lowercase )
									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase											=					model(lowercase )

									__lowercase											=					[input_ids, input_mask]
									__lowercase											=					model(lowercase )
									self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size) )





			def 		snake_case__      (					self						:							Dict      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Dict      ,  lowercase						:							Dict      ,  lowercase						:							int      ,  lowercase						:							Union[str, Any]      ,  )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					TFXLMWithLMHeadModel(lowercase )

									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
									__lowercase											=					model(lowercase )

									__lowercase											=					outputs

									self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.vocab_size) )





			def 		snake_case__      (					self						:							Optional[Any]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Any      ,  lowercase						:							int      ,  lowercase						:							List[Any]      ,  lowercase						:							Any      ,  lowercase						:							int      ,  lowercase						:							List[Any]      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[Any]      ,  )    ->       Dict:
									"""simple docstring"""







									__lowercase											=					TFXLMForQuestionAnsweringSimple(lowercase )

									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths}

									__lowercase											=					model(lowercase )

									self.parent.assertEqual(result.start_logits.shape      ,  (self.batch_size, self.seq_length) )
									self.parent.assertEqual(result.end_logits.shape      ,  (self.batch_size, self.seq_length) )





			def 		snake_case__      (					self						:							Optional[int]      ,  lowercase						:							Tuple      ,  lowercase						:							List[str]      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Any      ,  lowercase						:							Tuple      ,  lowercase						:							Any      ,  lowercase						:							Dict      ,  )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					TFXLMForSequenceClassification(lowercase )

									__lowercase											=					{"""input_ids""": input_ids, """lengths""": input_lengths}

									__lowercase											=					model(lowercase )

									self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size) )





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[int]      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							Tuple      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Tuple      ,  lowercase						:							List[Any]      ,  lowercase						:							Optional[Any]      ,  )    ->       int:
									"""simple docstring"""







									__lowercase											=					self.num_labels
									__lowercase											=					TFXLMForTokenClassification(config=lowercase )
									__lowercase											=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
									__lowercase											=					model(lowercase )
									self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.num_labels) )





			def 		snake_case__      (					self						:							int      ,  lowercase						:							Tuple      ,  lowercase						:							Optional[Any]      ,  lowercase						:							str      ,  lowercase						:							str      ,  lowercase						:							Dict      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							List[str]      ,  lowercase						:							List[str]      ,  )    ->       List[str]:
									"""simple docstring"""







									__lowercase											=					self.num_choices
									__lowercase											=					TFXLMForMultipleChoice(config=lowercase )
									__lowercase											=					tf.tile(tf.expand_dims(lowercase      ,  1 )      ,  (1, self.num_choices, 1) )
									__lowercase											=					tf.tile(tf.expand_dims(lowercase      ,  1 )      ,  (1, self.num_choices, 1) )
									__lowercase											=					tf.tile(tf.expand_dims(lowercase      ,  1 )      ,  (1, self.num_choices, 1) )
									__lowercase											=					{
									    """input_ids""": multiple_choice_inputs_ids,
									    """attention_mask""": multiple_choice_input_mask,
									    """token_type_ids""": multiple_choice_token_type_ids,
									}
									__lowercase											=					model(lowercase )
									self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_choices) )







			def 		snake_case__      (					self						:							str )    ->       Optional[int]:
									"""simple docstring"""







									__lowercase											=					self.prepare_config_and_inputs()
									(
									    (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      (
									    __lowercase
									)						,      
									)											=					config_and_inputs
									__lowercase											=					{
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """langs""": token_type_ids,
									    """lengths""": input_lengths,
									}
									return config, inputs_dict





@require_tf
class       _lowerCAmelCase (			_UpperCAmelCase     ,       _UpperCAmelCase     ,       unittest.TestCase      ):






			"""simple docstring"""





			lowercase__   :	Tuple  			=      (
			    (
			        TFXLMModel,
			        TFXLMWithLMHeadModel,
			        TFXLMForSequenceClassification,
			        TFXLMForQuestionAnsweringSimple,
			        TFXLMForTokenClassification,
			        TFXLMForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			lowercase__   :	List[str]  			=      (
			    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
			)  # TODO (PVP): Check other models whether language generation is also applicable
			lowercase__   :	Tuple  			=      (
			    {
			        """feature-extraction""": TFXLMModel,
			        """fill-mask""": TFXLMWithLMHeadModel,
			        """question-answering""": TFXLMForQuestionAnsweringSimple,
			        """text-classification""": TFXLMForSequenceClassification,
			        """text-generation""": TFXLMWithLMHeadModel,
			        """token-classification""": TFXLMForTokenClassification,
			        """zero-shot""": TFXLMForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowercase__   :	int  			=      False
			lowercase__   :	int  			=      False





			def 		snake_case__      (					self						:							Optional[int]      ,  lowercase						:							List[str]      ,  lowercase						:							Optional[Any]      ,  lowercase						:							Optional[int]      ,  lowercase						:							List[str]      ,  lowercase						:							Dict )    ->       str:
									"""simple docstring"""







									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith("""Fast""" )
									):
															# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
															# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
															# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
															return True

									return False





			def 		snake_case__      (					self						:							Tuple )    ->       List[str]:
									"""simple docstring"""







									__lowercase											=					TFXLMModelTester(self )
									__lowercase											=					ConfigTester(self      ,  config_class=lowercase      ,  emb_dim=37 )





			def 		snake_case__      (					self						:							List[str] )    ->       List[str]:
									"""simple docstring"""







									self.config_tester.run_common_tests()





			def 		snake_case__      (					self						:							List[Any] )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_model(*lowercase )





			def 		snake_case__      (					self						:							str )    ->       str:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_lm_head(*lowercase )





			def 		snake_case__      (					self						:							Optional[int] )    ->       Optional[int]:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_qa(*lowercase )





			def 		snake_case__      (					self						:							Optional[Any] )    ->       str:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_sequence_classif(*lowercase )





			def 		snake_case__      (					self						:							Union[str, Any] )    ->       int:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_token_classification(*lowercase )





			def 		snake_case__      (					self						:							Optional[Any] )    ->       Tuple:
									"""simple docstring"""







									__lowercase											=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase )







			@slow
			def 		snake_case__      (					self						:							Optional[int] )    ->       Optional[int]:
									"""simple docstring"""







									for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__lowercase											=					TFXLMModel.from_pretrained(lowercase )
															self.assertIsNotNone(lowercase )





@require_tf
class       _lowerCAmelCase (			unittest.TestCase      ):






			"""simple docstring"""





			@slow
			def 		snake_case__      (					self						:							int )    ->       List[Any]:
									"""simple docstring"""







									__lowercase											=					TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""" )
									__lowercase											=					tf.convert_to_tensor([[14, 447]]      ,  dtype=tf.intaa )  # the president
									__lowercase											=					[
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									]  # the president the president the president the president the president the president the president the president the president the president
									# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
									__lowercase											=					model.generate(lowercase      ,  do_sample=lowercase )
									self.assertListEqual(output_ids[0].numpy().tolist()      ,  lowercase )










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
UpperCamelCase__			=    "src/transformers"

# Matches is_xxx_available()
UpperCamelCase__			=    re.compile(R"is\_([a-z_]*)_available()")
# Matches from xxx import bla
UpperCamelCase__			=    re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
UpperCamelCase__			=    re.compile(R"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


UpperCamelCase__			=    "\n{0} = None\n"

UpperCamelCase__			=    "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


UpperCamelCase__			=    "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"






def 			UpperCAmelCase__     (     lowercase__ )			->   List[Any]:
						if _re_test_backend.search(lowercase__ ) is None:
												return None
						__lowercase											=					[b[0] for b in _re_backend.findall(lowercase__ )]
						backends.sort()
						return "_and_".join(lowercase__ )






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

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

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

																		__lowercase											=					[]
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
																								__lowercase											=					lines[line_index]
																								__lowercase											=					_re_single_line_import.search(lowercase__ )
																								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

																		__lowercase											=					objects
												else:
																		line_index += 1

						return backend_specific_objects






def 			UpperCAmelCase__     (     lowercase__    ,							lowercase__ )			->   int:
						if name.isupper():
												return DUMMY_CONSTANT.format(lowercase__ )
						elif name.islower():
												return DUMMY_FUNCTION.format(lowercase__    ,							lowercase__ )
						else:
												return DUMMY_CLASS.format(lowercase__    ,							lowercase__ )






def 			UpperCAmelCase__     (     lowercase__=None )			->   List[str]:
						if backend_specific_objects is None:
												__lowercase											=					read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						__lowercase											=					{}

						for backend, objects in backend_specific_objects.items():
												__lowercase											=					"""[""" + """, """.join(F"\"{b}\"" for b in backend.split("""_and_""" ) ) + """]"""
												__lowercase											=					"""# 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(lowercase__    ,							lowercase__ ) for o in objects] )
												__lowercase											=					dummy_file

						return dummy_files






def 			UpperCAmelCase__     (     lowercase__=False )			->   Optional[Any]:
						__lowercase											=					create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						__lowercase											=					{"""torch""": """pt"""}

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

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

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F"Updating transformers.utils.dummy_{short_names.get(lowercase__    ,							lowercase__ )}_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(lowercase__    ,							lowercase__ )}_objects.py. Run `make fix-copies` "
																								    """to fix this.""" )


if __name__ == "__main__":
			UpperCamelCase__			=    argparse.ArgumentParser()
			parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
			UpperCamelCase__			=    parser.parse_args()

			check_dummies(args.fix_and_overwrite)





