


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  lowerCamelCase__  :





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase=False						,	UpperCAmelCase=1_3						,	UpperCAmelCase=(3_2, 1_2_8)						,	UpperCAmelCase=4						,	UpperCAmelCase=3						,	UpperCAmelCase=2_7						,	UpperCAmelCase=3_8						,	UpperCAmelCase=9_9						,	UpperCAmelCase=9_9						,	UpperCAmelCase=3_2						,	UpperCAmelCase=5						,	UpperCAmelCase=4						,	UpperCAmelCase=4.0						,	UpperCAmelCase=2_5_7						,	UpperCAmelCase=None						,	)	->  Optional[int]:
      _lowercase				 =parent
      _lowercase				 =is_training
      _lowercase				 =batch_size
      _lowercase				 =image_size
      _lowercase				 =patch_size
      _lowercase				 =num_channels
      _lowercase				 =max_token_length
      _lowercase				 =num_character_labels
      _lowercase				 =num_bpe_labels
      _lowercase				 =num_wordpiece_labels
      _lowercase				 =hidden_size
      _lowercase				 =num_hidden_layers
      _lowercase				 =num_attention_heads
      _lowercase				 =mlp_ratio
      _lowercase				 =patch_embeds_hidden_size
      _lowercase				 =output_hidden_states





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]			)
      _lowercase				 =self.get_config()
      return config, pixel_values





     def 	__A   (self			)	->  List[Any]:
      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 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase			)	->  int:
      _lowercase				 =MgpstrForSceneTextRecognition(UpperCAmelCase			)
      model.to(UpperCAmelCase			)
      model.eval()
      with torch.no_grad():
       _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(
          generated_ids[0][0].shape						,	(self.batch_size, self.max_token_length, self.num_character_labels)			)





     def 	__A   (self			)	->  str:
      _lowercase				 =self.prepare_config_and_inputs()
      _lowercase ,       _lowercase				 =config_and_inputs
      _lowercase				 ={'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_torch
class  lowerCamelCase__  (			lowerCAmelCase  ,				lowerCAmelCase  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__      =     {'''feature-extraction''': MgpstrForSceneTextRecognition} if is_torch_available() else {}
     SCREAMING_SNAKE_CASE__      =     False

     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False





     def 	__A   (self			)	->  str:
      _lowercase				 =MgpstrModelTester(self			)
      _lowercase				 =ConfigTester(self						,	config_class=UpperCAmelCase						,	has_text_modality=UpperCAmelCase			)





     def 	__A   (self			)	->  Dict:
      self.config_tester.run_common_tests()





     def 	__A   (self			)	->  str:
      _lowercase				 =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 	__A   (self			)	->  str:
      pass





     def 	__A   (self			)	->  Optional[Any]:
      _lowercase ,       _lowercase				 =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase				 =model_class(UpperCAmelCase			)
       self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)			)
       _lowercase				 =model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCAmelCase						,	nn.Linear			)			)





     def 	__A   (self			)	->  Tuple:
      _lowercase ,       _lowercase				 =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase				 =model_class(UpperCAmelCase			)
       _lowercase				 =inspect.signature(model.forward			)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _lowercase				 =[*signature.parameters.keys()]

       _lowercase				 =['''pixel_values''']
       self.assertListEqual(arg_names[:1]						,	UpperCAmelCase			)





     @unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''			)
     def 	__A   (self			)	->  List[str]:
      pass





     def 	__A   (self			)	->  Optional[int]:
      _lowercase ,       _lowercase				 =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				 =True
       _lowercase				 =model_class(UpperCAmelCase			)
       self.assertTrue(model.is_gradient_checkpointing			)





     def 	__A   (self			)	->  List[Any]:
      def check_hidden_states_output(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			):
       _lowercase				 =model_class(UpperCAmelCase			)
       model.to(UpperCAmelCase			)
       model.eval()

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

       _lowercase				 =outputs.hidden_states

       _lowercase				 =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				 =self.model_tester.prepare_config_and_inputs_for_common()

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

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

       check_hidden_states_output(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)





     def 	__A   (self			)	->  str:
      _lowercase ,       _lowercase				 =self.model_tester.prepare_config_and_inputs_for_common()

      _lowercase				 =_config_zero_init(UpperCAmelCase			)
      for model_class in self.all_model_classes:
       _lowercase				 =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 	__A   (self			)	->  Optional[int]:
      pass



def 							UpperCAmelCase_   (				)	->     Tuple:







 """simple docstring"""


 _lowercase				 ='''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
 _lowercase				 =Image.open(requests.get(__snake_case	,		stream=__snake_case		).raw		).convert('''RGB'''		)
 return im







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





     @slow
     def 	__A   (self			)	->  Tuple:
      _lowercase				 ='''alibaba-damo/mgp-str-base'''
      _lowercase				 =MgpstrForSceneTextRecognition.from_pretrained(UpperCAmelCase			).to(UpperCAmelCase			)
      _lowercase				 =MgpstrProcessor.from_pretrained(UpperCAmelCase			)

      _lowercase				 =prepare_img()
      _lowercase				 =processor(images=UpperCAmelCase						,	return_tensors='''pt'''			).pixel_values.to(UpperCAmelCase			)

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

      # verify the logits
      self.assertEqual(outputs.logits[0].shape						,	torch.Size((1, 2_7, 3_8)			)			)

      _lowercase				 =processor.batch_decode(outputs.logits			)
      _lowercase				 ='''ticket'''

      self.assertEqual(out_strs['''generated_text'''][0]						,	UpperCAmelCase			)

      _lowercase				 =torch.tensor(
          [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]						,	device=UpperCAmelCase						,	)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__        =				{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaubertForMultipleChoice''',
      '''FlaubertForQuestionAnswering''',
      '''FlaubertForQuestionAnsweringSimple''',
      '''FlaubertForSequenceClassification''',
      '''FlaubertForTokenClassification''',
      '''FlaubertModel''',
      '''FlaubertWithLMHeadModel''',
      '''FlaubertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFlaubertForMultipleChoice''',
      '''TFFlaubertForQuestionAnsweringSimple''',
      '''TFFlaubertForSequenceClassification''',
      '''TFFlaubertForTokenClassification''',
      '''TFFlaubertModel''',
      '''TFFlaubertPreTrainedModel''',
      '''TFFlaubertWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

  UpperCAmelCase__        =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

