




'''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    ,   ) ->	List[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    ) ->	Any:
      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    ) ->	Optional[int]:
      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__    ,   ) ->	List[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__    ,   ) ->	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__    ,   ) ->	int:
      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__    ,   ) ->	str:
      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__    ,   ) ->	List[Any]:
      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__    ,   ) ->	Optional[Any]:
      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    ) ->	Optional[int]:
      snake_case				=			self.prepare_config_and_inputs()
      (
          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__    ) ->	Optional[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    ) ->	Dict:
      snake_case				=			XLMModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   emb_dim=37    )







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







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_model(*A__    )







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







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







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







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_sequence_classif(*A__    )







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_xlm_token_classif(*A__    )







     def     UpperCamelCase			( self    ) ->	str:
      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    ) ->	Dict:
      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    ) ->	Dict:
      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    ) ->	Dict:





      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    ) ->	int:
      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'''







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					IFInpaintingSuperResolutionPipeline
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     _UpperCAmelCase										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
     _UpperCAmelCase										=					PipelineTesterMixin.required_optional_params - {'''latents'''}







     def     UpperCamelCase			( self    ) ->	int:
      return self._get_superresolution_dummy_components()







     def     UpperCamelCase			( self    ,   A__    ,   A__=0    ) ->	Union[str, Any]:
      if str(A__    ).startswith('''mps'''    ):
       snake_case				=			torch.manual_seed(A__    )
      else:
       snake_case				=			torch.Generator(device=A__    ).manual_seed(A__    )

      snake_case				=			floats_tensor((1, 3, 16, 16)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )
      snake_case				=			floats_tensor((1, 3, 32, 32)    ,   rng=random.Random(A__    )    ).to(A__    )

      snake_case				=			{
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': image,
          '''original_image''': original_image,
          '''mask_image''': mask_image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }

      return inputs







     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()    ,   reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,   )
     def     UpperCamelCase			( self    ) ->	List[Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_optional_components()







     @unittest.skipIf(torch_device != '''cuda'''    ,   reason='''float16 requires CUDA'''    )
     def     UpperCamelCase			( self    ) ->	List[str]:
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1e-1    )







     def     UpperCamelCase			( self    ) ->	int:
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      self._test_save_load_local()







     def     UpperCamelCase			( self    ) ->	Dict:
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2    ,   )

