

'''simple docstring'''
import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MarkupLMForQuestionAnswering,
        MarkupLMForSequenceClassification,
        MarkupLMForTokenClassification,
        MarkupLMModel,
    )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer


class      a__						:


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=13    ,       _UpperCamelCase=7    ,       _UpperCamelCase=True    ,       _UpperCamelCase=True    ,       _UpperCamelCase=True    ,       _UpperCamelCase=True    ,       _UpperCamelCase=99    ,       _UpperCamelCase=32    ,       _UpperCamelCase=5    ,       _UpperCamelCase=4    ,       _UpperCamelCase=37    ,       _UpperCamelCase="gelu"    ,       _UpperCamelCase=0.1    ,       _UpperCamelCase=0.1    ,       _UpperCamelCase=512    ,       _UpperCamelCase=16    ,       _UpperCamelCase=2    ,       _UpperCamelCase=0.0_2    ,       _UpperCamelCase=3    ,       _UpperCamelCase=None    ,       _UpperCamelCase=20    ,       _UpperCamelCase=30    ,       _UpperCamelCase=2    ,       _UpperCamelCase=2    ,       _UpperCamelCase=10    ,       )     ->   Tuple:





      """simple docstring"""





      _lowercase    :   int      =     parent
      _lowercase    :   Union[str, Any]      =     batch_size
      _lowercase    :   Union[str, Any]      =     seq_length
      _lowercase    :   Tuple      =     is_training
      _lowercase    :   Tuple      =     use_input_mask
      _lowercase    :   List[str]      =     use_token_type_ids
      _lowercase    :   Optional[int]      =     use_labels
      _lowercase    :   List[Any]      =     vocab_size
      _lowercase    :   List[str]      =     hidden_size
      _lowercase    :   Optional[Any]      =     num_hidden_layers
      _lowercase    :   Optional[int]      =     num_attention_heads
      _lowercase    :   List[Any]      =     intermediate_size
      _lowercase    :   Tuple      =     hidden_act
      _lowercase    :   Any      =     hidden_dropout_prob
      _lowercase    :   str      =     attention_probs_dropout_prob
      _lowercase    :   Optional[int]      =     max_position_embeddings
      _lowercase    :   Dict      =     type_vocab_size
      _lowercase    :   Optional[Any]      =     type_sequence_label_size
      _lowercase    :   str      =     initializer_range
      _lowercase    :   Dict      =     num_labels
      _lowercase    :   str      =     scope
      _lowercase    :   Optional[Any]      =     max_xpath_tag_unit_embeddings
      _lowercase    :   Tuple      =     max_xpath_subs_unit_embeddings
      _lowercase    :   Union[str, Any]      =     tag_pad_id
      _lowercase    :   Union[str, Any]      =     subs_pad_id
      _lowercase    :   Optional[Any]      =     max_depth


   def 		_lowerCamelCase     (				self						)     ->   Tuple:





      """simple docstring"""





      _lowercase    :   List[str]      =     ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size						)

      _lowercase    :   Tuple      =     ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]    ,       self.max_xpath_tag_unit_embeddings						)

      _lowercase    :   List[str]      =     ids_tensor(
          [self.batch_size, self.seq_length, self.max_depth]    ,       self.max_xpath_subs_unit_embeddings						)

      _lowercase    :   Optional[Any]      =     None
      if self.use_input_mask:
         _lowercase    :   str      =     ids_tensor([self.batch_size, self.seq_length]    ,       vocab_size=2						)

      _lowercase    :   int      =     None
      if self.use_token_type_ids:
         _lowercase    :   Optional[int]      =     ids_tensor([self.batch_size, self.seq_length]    ,       self.type_vocab_size						)

      _lowercase    :   str      =     None
      _lowercase    :   Dict      =     None
      if self.use_labels:
         _lowercase    :   Tuple      =     ids_tensor([self.batch_size]    ,       self.type_sequence_label_size						)
         _lowercase    :   List[str]      =     ids_tensor([self.batch_size, self.seq_length]    ,       self.num_labels						)

      _lowercase    :   Tuple      =     self.get_config()

      return (
          config,
          input_ids,
          xpath_tags_seq,
          xpath_subs_seq,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
      )


   def 		_lowerCamelCase     (				self						)     ->   str:





      """simple docstring"""





      return MarkupLMConfig(
          vocab_size=self.vocab_size    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       intermediate_size=self.intermediate_size    ,       hidden_act=self.hidden_act    ,       hidden_dropout_prob=self.hidden_dropout_prob    ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       type_vocab_size=self.type_vocab_size    ,       initializer_range=self.initializer_range    ,       max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings    ,       max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings    ,       tag_pad_id=self.tag_pad_id    ,       subs_pad_id=self.subs_pad_id    ,       max_depth=self.max_depth    ,       )


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       )     ->   List[str]:





      """simple docstring"""





      _lowercase    :   Dict      =     MarkupLMModel(config=_UpperCamelCase						)
      model.to(_UpperCamelCase						)
      model.eval()
      print("Configs:"    ,       model.config.tag_pad_id    ,       model.config.subs_pad_id						)
      _lowercase    :   Optional[Any]      =     model(_UpperCamelCase    ,       attention_mask=_UpperCamelCase    ,       token_type_ids=_UpperCamelCase						)
      _lowercase    :   str      =     model(_UpperCamelCase    ,       token_type_ids=_UpperCamelCase						)
      _lowercase    :   Dict      =     model(_UpperCamelCase						)
      self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, self.seq_length, self.hidden_size)						)
      self.parent.assertEqual(result.pooler_output.shape    ,       (self.batch_size, self.hidden_size)						)


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       )     ->   Any:





      """simple docstring"""





      _lowercase    :   Any      =     self.num_labels
      _lowercase    :   str      =     MarkupLMForSequenceClassification(_UpperCamelCase						)
      model.to(_UpperCamelCase						)
      model.eval()
      _lowercase    :   str      =     model(
          _UpperCamelCase    ,       xpath_tags_seq=_UpperCamelCase    ,       xpath_subs_seq=_UpperCamelCase    ,       attention_mask=_UpperCamelCase    ,       token_type_ids=_UpperCamelCase    ,       labels=_UpperCamelCase    ,       )
      self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.num_labels)						)


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       )     ->   Optional[int]:





      """simple docstring"""





      _lowercase    :   Dict      =     self.num_labels
      _lowercase    :   Optional[Any]      =     MarkupLMForTokenClassification(config=_UpperCamelCase						)
      model.to(_UpperCamelCase						)
      model.eval()
      _lowercase    :   Optional[Any]      =     model(
          _UpperCamelCase    ,       xpath_tags_seq=_UpperCamelCase    ,       xpath_subs_seq=_UpperCamelCase    ,       attention_mask=_UpperCamelCase    ,       token_type_ids=_UpperCamelCase    ,       labels=_UpperCamelCase    ,       )
      self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.seq_length, self.num_labels)						)


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       )     ->   Optional[Any]:





      """simple docstring"""





      _lowercase    :   Tuple      =     MarkupLMForQuestionAnswering(config=_UpperCamelCase						)
      model.to(_UpperCamelCase						)
      model.eval()
      _lowercase    :   str      =     model(
          _UpperCamelCase    ,       xpath_tags_seq=_UpperCamelCase    ,       xpath_subs_seq=_UpperCamelCase    ,       attention_mask=_UpperCamelCase    ,       token_type_ids=_UpperCamelCase    ,       start_positions=_UpperCamelCase    ,       end_positions=_UpperCamelCase    ,       )
      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						)     ->   Optional[int]:





      """simple docstring"""





      _lowercase    :   Tuple      =     self.prepare_config_and_inputs()
      (
          _lowercase   
      )    :   str      =     config_and_inputs
      _lowercase    :   int      =     {
          "input_ids": input_ids,
          "xpath_tags_seq": xpath_tags_seq,
          "xpath_subs_seq": xpath_subs_seq,
          "token_type_ids": token_type_ids,
          "attention_mask": input_mask,
      }
      return config, inputs_dict




@require_torch
class      a__						(							lowerCamelCase_						,   lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							(
       (
           MarkupLMModel,
           MarkupLMForSequenceClassification,
           MarkupLMForTokenClassification,
           MarkupLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							(
       {
           'feature-extraction': MarkupLMModel,
           'question-answering': MarkupLMForQuestionAnswering,
           'text-classification': MarkupLMForSequenceClassification,
           'token-classification': MarkupLMForTokenClassification,
           'zero-shot': MarkupLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)     ->   List[str]:





      """simple docstring"""





      return True


   def 		_lowerCamelCase     (				self						)     ->   Dict:





      """simple docstring"""





      _lowercase    :   Any      =     MarkupLMModelTester(self						)
      _lowercase    :   int      =     ConfigTester(self    ,       config_class=_UpperCamelCase    ,       hidden_size=37						)


   def 		_lowerCamelCase     (				self						)     ->   Any:





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 		_lowerCamelCase     (				self						)     ->   Union[str, Any]:





      """simple docstring"""





      _lowercase    :   Dict      =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCamelCase						)


   def 		_lowerCamelCase     (				self						)     ->   Any:





      """simple docstring"""





      _lowercase    :   Tuple      =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase						)


   def 		_lowerCamelCase     (				self						)     ->   Dict:





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase						)






   def 		_lowerCamelCase     (				self						)     ->   Tuple:





      """simple docstring"""





      _lowercase    :   Optional[int]      =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase						)







def       _A	(			)    ->			Dict:
   _lowercase    :   int      =     "\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    "

   return html_string


@require_torch
class      a__						(							unittest.TestCase						):


   @cached_property
   def 		_lowerCamelCase     (				self						)     ->   str:





      """simple docstring"""





      _lowercase    :   Tuple      =     MarkupLMFeatureExtractor()
      _lowercase    :   Optional[int]      =     MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base"						)

      return MarkupLMProcessor(_UpperCamelCase    ,       _UpperCamelCase						)






   @slow
   def 		_lowerCamelCase     (				self						)     ->   str:





      """simple docstring"""





      _lowercase    :   str      =     MarkupLMModel.from_pretrained("microsoft/markuplm-base"						).to(_UpperCamelCase						)

      _lowercase    :   Dict      =     self.default_processor

      _lowercase    :   Optional[int]      =     processor(prepare_html_string()    ,       return_tensors="pt"						)
      _lowercase    :   Union[str, Any]      =     inputs.to(_UpperCamelCase						)

      # forward pass
      with torch.no_grad():
         _lowercase    :   Any      =     model(**_UpperCamelCase						)

      # verify the last hidden states
      _lowercase    :   Optional[Any]      =     torch.Size([1, 14, 768]						)
      self.assertEqual(outputs.last_hidden_state.shape    ,       _UpperCamelCase						)

      _lowercase    :   List[Any]      =     torch.tensor(
          [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]						).to(_UpperCamelCase						)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,       _UpperCamelCase    ,       atol=1E-4						)						)









'''simple docstring'''
import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor


class      a__						:


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=False    ,       _UpperCamelCase=13    ,       _UpperCamelCase=(32, 128)    ,       _UpperCamelCase=4    ,       _UpperCamelCase=3    ,       _UpperCamelCase=27    ,       _UpperCamelCase=38    ,       _UpperCamelCase=99    ,       _UpperCamelCase=99    ,       _UpperCamelCase=32    ,       _UpperCamelCase=5    ,       _UpperCamelCase=4    ,       _UpperCamelCase=4.0    ,       _UpperCamelCase=257    ,       _UpperCamelCase=None    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     parent
      _lowercase    :   Dict      =     is_training
      _lowercase    :   Optional[Any]      =     batch_size
      _lowercase    :   int      =     image_size
      _lowercase    :   Dict      =     patch_size
      _lowercase    :   Optional[int]      =     num_channels
      _lowercase    :   List[str]      =     max_token_length
      _lowercase    :   int      =     num_character_labels
      _lowercase    :   int      =     num_bpe_labels
      _lowercase    :   int      =     num_wordpiece_labels
      _lowercase    :   Union[str, Any]      =     hidden_size
      _lowercase    :   List[str]      =     num_hidden_layers
      _lowercase    :   int      =     num_attention_heads
      _lowercase    :   Any      =     mlp_ratio
      _lowercase    :   Optional[Any]      =     patch_embeds_hidden_size
      _lowercase    :   Optional[Any]      =     output_hidden_states


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
      _lowercase    :   int      =     self.get_config()
      return config, pixel_values


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return MgpstrConfig(
          image_size=self.image_size    ,       patch_size=self.patch_size    ,       num_channels=self.num_channels    ,       max_token_length=self.max_token_length    ,       num_character_labels=self.num_character_labels    ,       num_bpe_labels=self.num_bpe_labels    ,       num_wordpiece_labels=self.num_wordpiece_labels    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       num_attention_heads=self.num_attention_heads    ,       mlp_ratio=self.mlp_ratio    ,       output_hidden_states=self.output_hidden_states    ,       )


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   List[str]      =     MgpstrForSceneTextRecognition(_UpperCamelCase						)
      model.to(_UpperCamelCase						)
      model.eval()
      with torch.no_grad():
         _lowercase    :   Tuple      =     model(_UpperCamelCase						)
      self.parent.assertEqual(
          generated_ids[0][0].shape    ,       (self.batch_size, self.max_token_length, self.num_character_labels)						)






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     self.prepare_config_and_inputs()
      _lowercase  ,   _lowercase    :   Optional[int]      =     config_and_inputs
      _lowercase    :   Union[str, Any]      =     {"pixel_values": pixel_values}
      return config, inputs_dict




@require_torch
class      a__						(							lowerCamelCase_						,   lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  str			 =							(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							{'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False

   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							False
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							False
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     MgpstrModelTester(self						)
      _lowercase    :   Optional[Any]      =     ConfigTester(self    ,       config_class=_UpperCamelCase    ,       has_text_modality=_UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      self.config_tester.run_common_tests()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCamelCase						)


   @unittest.skip(reason="MgpstrModel does not use inputs_embeds"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   str      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   Optional[int]      =     model_class(_UpperCamelCase						)
         self.assertIsInstance(model.get_input_embeddings()    ,       (nn.Module)						)
         _lowercase    :   Any      =     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_UpperCamelCase    ,       nn.Linear						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   Any      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   int      =     model_class(_UpperCamelCase						)
         _lowercase    :   Union[str, Any]      =     inspect.signature(model.forward						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowercase    :   Optional[int]      =     [*signature.parameters.keys()]

         _lowercase    :   Optional[Any]      =     ["pixel_values"]
         self.assertListEqual(arg_names[:1]    ,       _UpperCamelCase						)


   @unittest.skip(reason="MgpstrModel does not support feedforward chunking"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   List[str]      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         if not model_class.supports_gradient_checkpointing:
            continue

         _lowercase    :   List[Any]      =     True
         _lowercase    :   int      =     model_class(_UpperCamelCase						)
         self.assertTrue(model.is_gradient_checkpointing						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      def check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						):
         _lowercase    :   Dict      =     model_class(_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()

         with torch.no_grad():
            _lowercase    :   Dict      =     model(**self._prepare_for_class(_UpperCamelCase    ,       _UpperCamelCase						)						)

         _lowercase    :   int      =     outputs.hidden_states

         _lowercase    :   Any      =     getattr(
             self.model_tester    ,       "expected_num_hidden_layers"    ,       self.model_tester.num_hidden_layers + 1						)
         self.assertEqual(len(_UpperCamelCase						)    ,       _UpperCamelCase						)

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]						)    ,       [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]    ,       )

      _lowercase  ,   _lowercase    :   Optional[int]      =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _lowercase    :   Dict      =     True
         check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowercase    :   Dict      =     True

         check_hidden_states_output(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase  ,   _lowercase    :   Union[str, Any]      =     self.model_tester.prepare_config_and_inputs_for_common()

      _lowercase    :   str      =     _config_zero_init(_UpperCamelCase						)
      for model_class in self.all_model_classes:
         _lowercase    :   Union[str, Any]      =     model_class(config=_UpperCamelCase						)
         for name, param in model.named_parameters():
            if isinstance(_UpperCamelCase    ,       (nn.Linear, nn.Convad, nn.LayerNorm)						):
               if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1E9).round() / 1E9).item()    ,       [0.0, 1.0]    ,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,       )






   @unittest.skip(reason="Retain_grad is tested in individual model tests"						)
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass







def       _A	(			)    ->			str:
   _lowercase    :   Any      =     "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
   _lowercase    :   Any      =     Image.open(requests.get(snake_case	,							stream=snake_case  ).raw  ).convert("RGB"  )
   return im


@require_vision
@require_torch
class      a__						(							unittest.TestCase						):


   @slow
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     "alibaba-damo/mgp-str-base"
      _lowercase    :   Any      =     MgpstrForSceneTextRecognition.from_pretrained(_UpperCamelCase						).to(_UpperCamelCase						)
      _lowercase    :   List[Any]      =     MgpstrProcessor.from_pretrained(_UpperCamelCase						)

      _lowercase    :   Tuple      =     prepare_img()
      _lowercase    :   Any      =     processor(images=_UpperCamelCase    ,       return_tensors="pt"						).pixel_values.to(_UpperCamelCase						)

      # forward pass
      with torch.no_grad():
         _lowercase    :   Tuple      =     model(_UpperCamelCase						)

      # verify the logits
      self.assertEqual(outputs.logits[0].shape    ,       torch.Size((1, 27, 38)						)						)

      _lowercase    :   Tuple      =     processor.batch_decode(outputs.logits						)
      _lowercase    :   Optional[Any]      =     "ticket"

      self.assertEqual(out_strs["generated_text"][0]    ,       _UpperCamelCase						)

      _lowercase    :   Tuple      =     torch.tensor(
          [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]    ,       device=_UpperCamelCase    ,       )

      self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]    ,       _UpperCamelCase    ,       atol=1E-4						)						)






