
import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
				from .testing_utils import (
				    floats_tensor,
				    load_hf_numpy,
				    load_image,
				    load_numpy,
				    load_pt,
				    nightly,
				    parse_flag_from_env,
				    print_tensor_test,
				    require_torch_a,
				    require_torch_gpu,
				    skip_mps,
				    slow,
				    torch_all_close,
				    torch_device,
				)
				from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase__				:				Optional[int]						=      get_logger(__name__)




def 						A_			(				snake_case				: Tuple			)   ->     Tuple:



						'''simple docstring'''

						if version.parse(snake_case			) < version.parse(snake_case			):
												if "dev" in min_version:
																		__UpperCamelCase  	=      (
																		    '''This example requires a source install from HuggingFace diffusers (see '''
																		    '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
																		)
												else:
																		__UpperCamelCase  	=      f"This example requires a minimum version of {min_version},"
												error_message += f" but the version found is {__version__}.\n"
												raise ImportError(snake_case			)





'''simple docstring'''
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       __lowercase		:







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						str		     =	parent
         __UpperCamelCase						:						Tuple		     =	1_3
         __UpperCamelCase						:						str		     =	7
         __UpperCamelCase						:						List[str]		     =	True
         __UpperCamelCase						:						Optional[Any]		     =	True
         __UpperCamelCase						:						Tuple		     =	True
         __UpperCamelCase						:						Tuple		     =	True
         __UpperCamelCase						:						int		     =	True
         __UpperCamelCase						:						int		     =	False
         __UpperCamelCase						:						Optional[Any]		     =	False
         __UpperCamelCase						:						Any		     =	False
         __UpperCamelCase						:						Any		     =	2
         __UpperCamelCase						:						str		     =	9_9
         __UpperCamelCase						:						int		     =	0
         __UpperCamelCase						:						Any		     =	3_2
         __UpperCamelCase						:						Tuple		     =	2
         __UpperCamelCase						:						Optional[int]		     =	4
         __UpperCamelCase						:						List[str]		     =	0.1
         __UpperCamelCase						:						List[Any]		     =	0.1
         __UpperCamelCase						:						List[str]		     =	5_1_2
         __UpperCamelCase						:						int		     =	1_6
         __UpperCamelCase						:						Any		     =	2
         __UpperCamelCase						:						Dict		     =	0.0_2
         __UpperCamelCase						:						Any		     =	3
         __UpperCamelCase						:						str		     =	4
         __UpperCamelCase						:						Any		     =	'last'
         __UpperCamelCase						:						int		     =	True
         __UpperCamelCase						:						List[str]		     =	None
         __UpperCamelCase						:						Union[str, Any]		     =	0







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size     )
         __UpperCamelCase						:						str		     =	random_attention_mask([self.batch_size, self.seq_length]  ,      dtype=tf.floataa     )

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

         __UpperCamelCase						:						int		     =	None
         if self.use_token_type_ids:
             __UpperCamelCase						:						Union[str, Any]		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.n_langs     )

         __UpperCamelCase						:						Optional[Any]		     =	None
         __UpperCamelCase						:						Optional[Any]		     =	None
         __UpperCamelCase						:						Tuple		     =	None
         if self.use_labels:
             __UpperCamelCase						:						Tuple		     =	ids_tensor([self.batch_size]  ,      self.type_sequence_label_size     )
             __UpperCamelCase						:						Dict		     =	ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels     )
             __UpperCamelCase						:						Optional[int]		     =	ids_tensor([self.batch_size]  ,      2  ,      dtype=tf.floataa     )
             __UpperCamelCase						:						int		     =	ids_tensor([self.batch_size]  ,      self.num_choices     )

         __UpperCamelCase						:						int		     =	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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						str		     =	TFXLMModel(config=_lowerCamelCase     )
         __UpperCamelCase						:						Optional[Any]		     =	{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
         __UpperCamelCase						:						str		     =	model(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	[input_ids, input_mask]
         __UpperCamelCase						:						Tuple		     =	model(_lowerCamelCase     )
         self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						int		     =	TFXLMWithLMHeadModel(_lowerCamelCase     )

         __UpperCamelCase						:						List[str]		     =	{'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
         __UpperCamelCase						:						List[str]		     =	model(_lowerCamelCase     )

         __UpperCamelCase						:						Tuple		     =	outputs

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







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						Tuple		     =	TFXLMForQuestionAnsweringSimple(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	{'input_ids': input_ids, 'lengths': input_lengths}

         __UpperCamelCase						:						Optional[Any]		     =	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 lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						Optional[Any]		     =	TFXLMForSequenceClassification(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	{'input_ids': input_ids, 'lengths': input_lengths}

         __UpperCamelCase						:						Dict		     =	model(_lowerCamelCase     )

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







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						int		     =	self.num_labels
         __UpperCamelCase						:						int		     =	TFXLMForTokenClassification(config=_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         __UpperCamelCase						:						List[str]		     =	model(_lowerCamelCase     )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)     )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      ):
         __UpperCamelCase						:						Any		     =	self.num_choices
         __UpperCamelCase						:						List[Any]		     =	TFXLMForMultipleChoice(config=_lowerCamelCase     )
         __UpperCamelCase						:						Tuple		     =	tf.tile(tf.expand_dims(_lowerCamelCase  ,      1     )  ,      (1, self.num_choices, 1)     )
         __UpperCamelCase						:						Optional[int]		     =	tf.tile(tf.expand_dims(_lowerCamelCase  ,      1     )  ,      (1, self.num_choices, 1)     )
         __UpperCamelCase						:						Union[str, Any]		     =	tf.tile(tf.expand_dims(_lowerCamelCase  ,      1     )  ,      (1, self.num_choices, 1)     )
         __UpperCamelCase						:						str		     =	{
             'input_ids': multiple_choice_inputs_ids,
             'attention_mask': multiple_choice_input_mask,
             'token_type_ids': multiple_choice_token_type_ids,
         }
         __UpperCamelCase						:						Dict		     =	model(_lowerCamelCase     )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_choices)     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[str]		     =	self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       (
             __UpperCamelCase
         )     ,       
         )						:						Any		     =	config_and_inputs
         __UpperCamelCase						:						Union[str, Any]		     =	{
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'langs': token_type_ids,
             'lengths': input_lengths,
         }
         return config, inputs_dict






@require_tf
class       __lowercase		(    _lowerCamelCase      ,				_lowerCamelCase      ,				unittest.TestCase    ):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		 =    (
         (
             TFXLMModel,
             TFXLMWithLMHeadModel,
             TFXLMForSequenceClassification,
             TFXLMForQuestionAnsweringSimple,
             TFXLMForTokenClassification,
             TFXLMForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__		 =    (
         (TFXLMWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     SCREAMING_SNAKE_CASE__		 =    (
         {
             '''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 {}
     )
     SCREAMING_SNAKE_CASE__		 =    False
     SCREAMING_SNAKE_CASE__		 =    False







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase     ):
         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 lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	TFXLMModelTester(self     )
         __UpperCamelCase						:						Union[str, Any]		     =	ConfigTester(self  ,      config_class=_lowerCamelCase  ,      emb_dim=3_7     )







     def lowerCAmelCase  (		self     ):
         self.config_tester.run_common_tests()







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_model(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[int]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_lm_head(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_qa(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Optional[Any]		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_sequence_classif(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Any		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_token_classification(*_lowerCamelCase     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_xlm_for_multiple_choice(*_lowerCamelCase     )







     @slow
     def lowerCAmelCase  (		self     ):
         for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __UpperCamelCase						:						List[Any]		     =	TFXLMModel.from_pretrained(_lowerCamelCase     )
             self.assertIsNotNone(_lowerCamelCase     )






@require_tf
class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     @slow
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Union[str, Any]		     =	TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'     )
         __UpperCamelCase						:						Union[str, Any]		     =	tf.convert_to_tensor([[1_4, 4_4_7]]  ,      dtype=tf.intaa     )  # the president
         __UpperCamelCase						:						List[Any]		     =	[
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
             1_4,
             4_4_7,
         ]  # 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
         __UpperCamelCase						:						str		     =	model.generate(_lowerCamelCase  ,      do_sample=_lowerCamelCase     )
         self.assertListEqual(output_ids[0].numpy().tolist()  ,      _lowerCamelCase     )



