





'''simple docstring'''







import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       XLMForMultipleChoice,
       XLMForQuestionAnswering,
       XLMForQuestionAnsweringSimple,
       XLMForSequenceClassification,
       XLMForTokenClassification,
       XLMModel,
       XLMWithLMHeadModel,
   )
   from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class 			_lowercase						:







     def __init__( self    ,   A__    ,   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__=5    ,   A__=4    ,   A__=0.1    ,   A__=0.1    ,   A__=5_12    ,   A__=2    ,   A__=0.0_2    ,   A__=2    ,   A__=4    ,   A__="last"    ,   A__=True    ,   A__=None    ,   A__=0    ,   ) ->	Optional[Any]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			seq_length
      snake_case				=			is_training
      snake_case				=			use_input_lengths
      snake_case				=			use_token_type_ids
      snake_case				=			use_labels
      snake_case				=			gelu_activation
      snake_case				=			sinusoidal_embeddings
      snake_case				=			causal
      snake_case				=			asm
      snake_case				=			n_langs
      snake_case				=			vocab_size
      snake_case				=			n_special
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			max_position_embeddings
      snake_case				=			type_sequence_label_size
      snake_case				=			initializer_range
      snake_case				=			num_labels
      snake_case				=			num_choices
      snake_case				=			summary_type
      snake_case				=			use_proj
      snake_case				=			scope
      snake_case				=			bos_token_id







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size    )
      snake_case				=			random_attention_mask([self.batch_size, self.seq_length]    )

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

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

      snake_case				=			None
      snake_case				=			None
      snake_case				=			None
      if self.use_labels:
       snake_case				=			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size    )
       snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.num_labels    )
       snake_case				=			ids_tensor([self.batch_size]    ,   2    ).float()
       snake_case				=			ids_tensor([self.batch_size]    ,   self.num_choices    )

      snake_case				=			self.get_config()

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







     def     UpperCamelCase			( self    ) ->	Any:
      return 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    ,   num_labels=self.num_labels    ,   bos_token_id=self.bos_token_id    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	str:
      snake_case				=			XLMModel(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			model(A__    ,   lengths=A__    ,   langs=A__    )
      snake_case				=			model(A__    ,   langs=A__    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Union[str, Any]:
      snake_case				=			XLMWithLMHeadModel(A__    )
      model.to(A__    )
      model.eval()

      snake_case				=			model(A__    ,   token_type_ids=A__    ,   labels=A__    )
      self.parent.assertEqual(result.loss.shape    ,   ()    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.vocab_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	List[str]:
      snake_case				=			XLMForQuestionAnsweringSimple(A__    )
      model.to(A__    )
      model.eval()

      snake_case				=			model(A__    )

      snake_case				=			model(A__    ,   start_positions=A__    ,   end_positions=A__    )
      snake_case				=			outputs
      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     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Union[str, Any]:
      snake_case				=			XLMForQuestionAnswering(A__    )
      model.to(A__    )
      model.eval()

      snake_case				=			model(A__    )

      snake_case				=			model(
          A__    ,   start_positions=A__    ,   end_positions=A__    ,   cls_index=A__    ,   is_impossible=A__    ,   p_mask=A__    ,   )

      snake_case				=			model(
          A__    ,   start_positions=A__    ,   end_positions=A__    ,   cls_index=A__    ,   is_impossible=A__    ,   )

      ((snake_case)				,		)				=			result_with_labels.to_tuple()

      snake_case				=			model(A__    ,   start_positions=A__    ,   end_positions=A__    )

      ((snake_case)				,		)				=			result_with_labels.to_tuple()

      self.parent.assertEqual(result_with_labels.loss.shape    ,   ()    )
      self.parent.assertEqual(result.start_top_log_probs.shape    ,   (self.batch_size, model.config.start_n_top)    )
      self.parent.assertEqual(result.start_top_index.shape    ,   (self.batch_size, model.config.start_n_top)    )
      self.parent.assertEqual(
          result.end_top_log_probs.shape    ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)    )
      self.parent.assertEqual(
          result.end_top_index.shape    ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)    )
      self.parent.assertEqual(result.cls_logits.shape    ,   (self.batch_size,)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	int:
      snake_case				=			XLMForSequenceClassification(A__    )
      model.to(A__    )
      model.eval()

      snake_case				=			model(A__    )
      snake_case				=			model(A__    ,   labels=A__    )
      self.parent.assertEqual(result.loss.shape    ,   ()    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Dict:
      snake_case				=			self.num_labels
      snake_case				=			XLMForTokenClassification(A__    )
      model.to(A__    )
      model.eval()

      snake_case				=			model(A__    ,   attention_mask=A__    ,   labels=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   ) ->	Optional[Any]:
      snake_case				=			self.num_choices
      snake_case				=			XLMForMultipleChoice(config=A__    )
      model.to(A__    )
      model.eval()
      snake_case				=			input_ids.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
      snake_case				=			token_type_ids.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
      snake_case				=			input_mask.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
      snake_case				=			model(
          A__    ,   attention_mask=A__    ,   token_type_ids=A__    ,   labels=A__    ,   )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_choices)    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.prepare_config_and_inputs()
      (
          (
          snake_case
      )				,		(
          snake_case
      )				,		(
          snake_case
      )				,		(
          snake_case
      )				,		(
          snake_case
      )				,		(
          snake_case
      )				,		(
          snake_case
      )				,		(
          snake_case
      )				,		(
          snake_case
      )				,		
      )				=			config_and_inputs
      snake_case				=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
      return config, inputs_dict




@require_torch
class 			_lowercase						(       __a				,		__a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(
         (
             XLMModel,
             XLMWithLMHeadModel,
             XLMForQuestionAnswering,
             XLMForSequenceClassification,
             XLMForQuestionAnsweringSimple,
             XLMForTokenClassification,
             XLMForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     _UpperCAmelCase										=					(
         (XLMWithLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     _UpperCAmelCase										=					(
         {
             '''feature-extraction''': XLMModel,
             '''fill-mask''': XLMWithLMHeadModel,
             '''question-answering''': XLMForQuestionAnsweringSimple,
             '''text-classification''': XLMForSequenceClassification,
             '''text-generation''': XLMWithLMHeadModel,
             '''token-classification''': XLMForTokenClassification,
             '''zero-shot''': XLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Any:
      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     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__=False    ) ->	Optional[int]:
      snake_case				=			super()._prepare_for_class(A__    ,   A__    ,   return_labels=A__    )

      if return_labels:
       if model_class.__name__ == "XLMForQuestionAnswering":
        snake_case				=			torch.zeros(
            self.model_tester.batch_size    ,   dtype=torch.long    ,   device=A__    )
        snake_case				=			torch.zeros(
            self.model_tester.batch_size    ,   dtype=torch.long    ,   device=A__    )

      return inputs_dict







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			XLMModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   emb_dim=37    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      self.config_tester.run_common_tests()







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_model(*A__    )







     def     UpperCamelCase			( self    ) ->	str:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_lm_head(*A__    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_simple_qa(*A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_qa(*A__    )







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_sequence_classif(*A__    )







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_token_classif(*A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_for_multiple_choice(*A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__=False    ,   A__=1    ) ->	Any:
      self.assertIsInstance(A__    ,   A__    )
      self.assertListEqual(
          [isinstance(A__    ,   A__    ) for iter_attentions in attentions]    ,   [True] * len(A__    )    )
      self.assertEqual(len(A__    )    ,   (max_length - min_length) * num_beam_groups    )

      for idx, iter_attentions in enumerate(A__    ):
       # adds PAD dummy token
       snake_case				=			min_length + idx + 1
       snake_case				=			min_length + idx + 1

       snake_case				=			(
           batch_size * num_beam_groups,
           config.num_attention_heads,
           tgt_len,
           src_len,
       )
       # check attn size
       self.assertListEqual(
           [layer_attention.shape for layer_attention in iter_attentions]    ,   [expected_shape] * len(A__    )    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__=False    ,   A__=1    ) ->	Tuple:
      self.assertIsInstance(A__    ,   A__    )
      self.assertListEqual(
          [isinstance(A__    ,   A__    ) for iter_hidden_states in hidden_states]    ,   [True] * len(A__    )    ,   )
      self.assertEqual(len(A__    )    ,   (max_length - min_length) * num_beam_groups    )

      for idx, iter_hidden_states in enumerate(A__    ):
       # adds PAD dummy token
       snake_case				=			min_length + idx + 1
       snake_case				=			(batch_size * num_beam_groups, seq_len, config.hidden_size)
       # check hidden size
       self.assertListEqual(
           [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]    ,   [expected_shape] * len(A__    )    ,   )
      pass







     @slow
     def     UpperCamelCase			( self    ) ->	Tuple:
      for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       snake_case				=			XLMModel.from_pretrained(A__    )
       self.assertIsNotNone(A__    )




@require_torch
class 			_lowercase						(       unittest.TestCase					):







     @slow
     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''    )
      model.to(A__    )
      snake_case				=			torch.tensor([[14, 4_47]]    ,   dtype=torch.long    ,   device=A__    )  # the president
      snake_case				=			[
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
          14,
          4_47,
      ]  # 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
      snake_case				=			model.generate(A__    ,   do_sample=A__    )
      self.assertListEqual(output_ids[0].cpu().numpy().tolist()    ,   A__    )








'''simple docstring'''







_lowercase       						=			{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

