



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   ,       lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  parent
				a__					=  13
				a__					=  7
				a__					=  True
				a__					=  True
				a__					=  True
				a__					=  True
				a__					=  True
				a__					=  False
				a__					=  False
				a__					=  False
				a__					=  2
				a__					=  99
				a__					=  0
				a__					=  32
				a__					=  2
				a__					=  4
				a__					=  0.1
				a__					=  0.1
				a__					=  512
				a__					=  16
				a__					=  2
				a__					=  0.0_2
				a__					=  3
				a__					=  4
				a__					=  """last"""
				a__					=  True
				a__					=  None
				a__					=  0

	def 				_A			(	self       ):



				'''simple docstring'''


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

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

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

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

				a__					=  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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       ):



				'''simple docstring'''


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

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

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  TFXLMWithLMHeadModel(lowerCamelCase       )

				a__					=  {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
				a__					=  model(lowerCamelCase       )

				a__					=  outputs

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

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  TFXLMForQuestionAnsweringSimple(lowerCamelCase       )

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

				a__					=  model(lowerCamelCase       )

				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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       ):



				'''simple docstring'''


				a__					=  TFXLMForSequenceClassification(lowerCamelCase       )

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

				a__					=  model(lowerCamelCase       )

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

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       ):



				'''simple docstring'''


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







	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.prepare_config_and_inputs()
				(
				    (
				    a__      
				)				,       (
				    a__      
				)				,       (
				    a__      
				)				,       (
				    a__      
				)				,       (
				    a__      
				)				,       (
				    a__      
				)				,       (
				    a__      
				)				,       (
				    a__      
				)				,       (
				    a__      
				)				,             
				)					=  config_and_inputs
				a__					=  {
				    """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'''




	A_       :     Tuple									=		(
	    (
	        TFXLMModel,
	        TFXLMWithLMHeadModel,
	        TFXLMForSequenceClassification,
	        TFXLMForQuestionAnsweringSimple,
	        TFXLMForTokenClassification,
	        TFXLMForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	A_       :     List[str]									=		(
	    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	A_       :     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 {}
	)
	A_       :     int									=		False
	A_       :     int									=		False

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''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 				_A			(	self       ):



				'''simple docstring'''


				a__					=  TFXLMModelTester(self       )
				a__					=  ConfigTester(self   ,       config_class=lowerCamelCase   ,       emb_dim=37       )

	def 				_A			(	self       ):



				'''simple docstring'''


				self.config_tester.run_common_tests()

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_model(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_lm_head(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_qa(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_sequence_classif(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_for_token_classification(*lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_xlm_for_multiple_choice(*lowerCamelCase       )







	@slow
	def 				_A			(	self       ):



				'''simple docstring'''


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


@require_tf
class lowerCAmelCase_       (   unittest.TestCase			):







	'''simple docstring'''




	@slow
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""       )
				a__					=  tf.convert_to_tensor([[14, 447]]   ,       dtype=tf.intaa       )  # the president
				a__					=  [
				    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
				a__					=  model.generate(lowerCamelCase   ,       do_sample=lowerCamelCase       )
				self.assertListEqual(output_ids[0].numpy().tolist()   ,       lowerCamelCase       )






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)





