
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 UpperCAmelCase     :





   '''simple docstring'''


   def __init__(      self  :							Optional[Any]    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							int=False    ,    lowerCAmelCase_  :							Any=1_3    ,    lowerCAmelCase_  :							List[str]=(3_2, 1_2_8)    ,    lowerCAmelCase_  :							Optional[Any]=4    ,    lowerCAmelCase_  :							Optional[int]=3    ,    lowerCAmelCase_  :							List[str]=2_7    ,    lowerCAmelCase_  :							Optional[Any]=3_8    ,    lowerCAmelCase_  :							Tuple=9_9    ,    lowerCAmelCase_  :							List[Any]=9_9    ,    lowerCAmelCase_  :							Any=3_2    ,    lowerCAmelCase_  :							List[str]=5    ,    lowerCAmelCase_  :							Union[str, Any]=4    ,    lowerCAmelCase_  :							int=4.0    ,    lowerCAmelCase_  :							Tuple=2_5_7    ,    lowerCAmelCase_  :							Optional[int]=None    ,    ):

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


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      _A:						Dict	    = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]  )
      _A:						int	    = self.get_config()
      return config, pixel_values


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """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 				__magic_name__       (      self  :							Tuple    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = MgpstrForSceneTextRecognition(lowerCAmelCase_  )
      model.to(lowerCAmelCase_  )
      model.eval()
      with torch.no_grad():
         _A:						int	    = model(lowerCAmelCase_  )
      self.parent.assertEqual(
          generated_ids[0][0].shape    ,    (self.batch_size, self.max_token_length, self.num_character_labels)  )


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						int	    = self.prepare_config_and_inputs()
      _A    ,						_A:						Any	    = config_and_inputs
      _A:						Any	    = {'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





   '''simple docstring'''
   __UpperCamelCase	:					Optional[int]				       =					(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
   __UpperCamelCase	:					Any				       =					{'''feature-extraction''': MgpstrForSceneTextRecognition} if is_torch_available() else {}
   __UpperCamelCase	:					List[str]				       =					False

   __UpperCamelCase	:					Optional[Any]				       =					False
   __UpperCamelCase	:					List[Any]				       =					False
   __UpperCamelCase	:					Tuple				       =					False
   __UpperCamelCase	:					List[Any]				       =					False


   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = MgpstrModelTester(self  )
      _A:						List[str]	    = ConfigTester(self    ,    config_class=lowerCAmelCase_    ,    has_text_modality=lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      self.config_tester.run_common_tests()


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      _A:						str	    = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase_  )


   @unittest.skip(reason='''MgpstrModel does not use inputs_embeds'''  )
   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      pass


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A    ,						_A:						str	    = self.model_tester.prepare_config_and_inputs_for_common()

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


   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      _A    ,						_A:						Optional[int]	    = self.model_tester.prepare_config_and_inputs_for_common()

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

         _A:						Optional[int]	    = ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,    lowerCAmelCase_  )


   @unittest.skip(reason='''MgpstrModel does not support feedforward chunking'''  )
   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      pass


   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      _A    ,						_A:						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

         _A:						Union[str, Any]	    = True
         _A:						Union[str, Any]	    = model_class(lowerCAmelCase_  )
         self.assertTrue(model.is_gradient_checkpointing  )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      def check_hidden_states_output(lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Union[str, Any]    ,    lowerCAmelCase_  :							Dict  ):
         _A:						List[Any]	    = model_class(lowerCAmelCase_  )
         model.to(lowerCAmelCase_  )
         model.eval()

         with torch.no_grad():
            _A:						Optional[int]	    = model(**self._prepare_for_class(lowerCAmelCase_    ,    lowerCAmelCase_  )  )

         _A:						Tuple	    = outputs.hidden_states

         _A:						Optional[Any]	    = getattr(
             self.model_tester    ,    '''expected_num_hidden_layers'''    ,    self.model_tester.num_hidden_layers + 1  )
         self.assertEqual(len(lowerCAmelCase_  )    ,    lowerCAmelCase_  )

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

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

      for model_class in self.all_model_classes:
         _A:						int	    = True
         check_hidden_states_output(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )

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

         check_hidden_states_output(lowerCAmelCase_    ,    lowerCAmelCase_    ,    lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A    ,						_A:						Optional[Any]	    = self.model_tester.prepare_config_and_inputs_for_common()

      _A:						List[str]	    = _config_zero_init(lowerCAmelCase_  )
      for model_class in self.all_model_classes:
         _A:						Dict	    = model_class(config=lowerCAmelCase_  )
         for name, param in model.named_parameters():
            if isinstance(lowerCAmelCase_    ,    (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 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      pass






def 						lowerCamelCase__							(      )   ->     Any:
   _A:						Tuple	    = '''https://i.postimg.cc/ZKwLg2Gw/367-14.png'''
   _A:						Tuple	    = Image.open(requests.get(a	,	stream=a   ).raw   ).convert('''RGB'''   )
   return im






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





   '''simple docstring'''


   @slow
   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      _A:						Any	    = '''alibaba-damo/mgp-str-base'''
      _A:						Union[str, Any]	    = MgpstrForSceneTextRecognition.from_pretrained(lowerCAmelCase_  ).to(lowerCAmelCase_  )
      _A:						List[Any]	    = MgpstrProcessor.from_pretrained(lowerCAmelCase_  )

      _A:						List[Any]	    = prepare_img()
      _A:						List[str]	    = processor(images=lowerCAmelCase_    ,    return_tensors='''pt'''  ).pixel_values.to(lowerCAmelCase_  )

      # forward pass
      with torch.no_grad():
         _A:						Union[str, Any]	    = model(lowerCAmelCase_  )

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

      _A:						Union[str, Any]	    = processor.batch_decode(outputs.logits  )
      _A:						int	    = '''ticket'''

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

      _A:						int	    = torch.tensor(
          [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]    ,    device=lowerCAmelCase_    ,    )

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





import pytest

from datasets.utils.version import Version






@pytest.mark.parametrize(
    '''other, expected_equality'''	,	[
        (Version('''1.0.0'''   ), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''   ), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]	,	)
def 						lowerCamelCase__							(      a	,	a   )   ->     str:
   _A:						Tuple	    = Version('''1.0.0'''   )
   assert (version == other) is expected_equality
   assert (version != other) is not expected_equality
   assert (hash(a   ) == hash(a   )) is expected_equality



