

'''simple docstring'''
import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class      a__						(							unittest.TestCase						):


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





      """simple docstring"""





      _lowercase    :   int      =     {
          "model_details": {
              "Organization": "testing",
              "Model date": "today",
              "Model version": "v2.1, Developed by Test Corp in 2019.",
              "Architecture": "Convolutional Neural Network.",
          },
          "metrics": "BLEU and ROUGE-1",
          "evaluation_data": {
              "Datasets": {"BLEU": "My-great-dataset-v1", "ROUGE-1": "My-short-dataset-v2.1"},
              "Preprocessing": "See details on https://arxiv.org/pdf/1810.03993.pdf",
          },
          "training_data": {
              "Dataset": "English Wikipedia dump dated 2018-12-01",
              "Preprocessing": (
                  "Using SentencePiece vocabulary of size 52k tokens. See details on"
                  " https://arxiv.org/pdf/1810.03993.pdf"
              ),
          },
          "quantitative_analyses": {"BLEU": 55.1, "ROUGE-1": 76},
      }


   def 		_lowerCamelCase     (				self						)     ->   Tuple:





      """simple docstring"""





      _lowercase    :   List[Any]      =     ModelCard.from_dict(self.inputs_dict						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "model_details"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "intended_use"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "factors"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "metrics"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "evaluation_data"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "training_data"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "quantitative_analyses"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "ethical_considerations"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "caveats_and_recommendations"						)						)


   def 		_lowerCamelCase     (				self						)     ->   Tuple:





      """simple docstring"""





      _lowercase    :   Optional[Any]      =     ModelCard.from_dict(self.inputs_dict						)
      _lowercase    :   Any      =     json.loads(modelcard.to_json_string()						)
      for key, value in self.inputs_dict.items():
         self.assertEqual(obj[key]    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						)     ->   int:





      """simple docstring"""





      _lowercase    :   List[Any]      =     ModelCard.from_dict(self.inputs_dict						)

      with tempfile.TemporaryDirectory() as tmpdirname:
         _lowercase    :   List[str]      =     os.path.join(_UpperCamelCase    ,       "modelcard.json"						)
         model_card_first.to_json_file(_UpperCamelCase						)
         _lowercase    :   Optional[Any]      =     ModelCard.from_json_file(_UpperCamelCase						)

      self.assertEqual(model_card_second.to_dict()    ,       model_card_first.to_dict()						)






   def 		_lowerCamelCase     (				self						)     ->   Any:





      """simple docstring"""





      _lowercase    :   List[Any]      =     ModelCard.from_dict(self.inputs_dict						)

      with tempfile.TemporaryDirectory() as tmpdirname:
         model_card_first.save_pretrained(_UpperCamelCase						)
         _lowercase    :   List[Any]      =     ModelCard.from_pretrained(_UpperCamelCase						)

      self.assertEqual(model_card_second.to_dict()    ,       model_card_first.to_dict()						)









'''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						)						)






