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_					:






 '''simple docstring'''






 def __init__(     self   :    Optional[int]     ,       lowercase__   :    int     ,       lowercase__   :    Optional[Any]=False     ,       lowercase__   :    Optional[int]=13     ,       lowercase__   :    List[str]=(32, 128)     ,       lowercase__   :    List[str]=4     ,       lowercase__   :    Dict=3     ,       lowercase__   :    str=27     ,       lowercase__   :    Optional[int]=38     ,       lowercase__   :    Optional[Any]=99     ,       lowercase__   :    Dict=99     ,       lowercase__   :    Dict=32     ,       lowercase__   :    Optional[Any]=5     ,       lowercase__   :    Optional[Any]=4     ,       lowercase__   :    int=4.0     ,       lowercase__   :    Union[str, Any]=257     ,       lowercase__   :    Optional[Any]=None     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   parent
        lowerCAmelCase__	      =   is_training
        lowerCAmelCase__	      =   batch_size
        lowerCAmelCase__	      =   image_size
        lowerCAmelCase__	      =   patch_size
        lowerCAmelCase__	      =   num_channels
        lowerCAmelCase__	      =   max_token_length
        lowerCAmelCase__	      =   num_character_labels
        lowerCAmelCase__	      =   num_bpe_labels
        lowerCAmelCase__	      =   num_wordpiece_labels
        lowerCAmelCase__	      =   hidden_size
        lowerCAmelCase__	      =   num_hidden_layers
        lowerCAmelCase__	      =   num_attention_heads
        lowerCAmelCase__	      =   mlp_ratio
        lowerCAmelCase__	      =   patch_embeds_hidden_size
        lowerCAmelCase__	      =   output_hidden_states




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])
        lowerCAmelCase__	      =   self.get_config()
        return config, pixel_values




 def 						__snake_case		(     self   :    Tuple):







        '''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 						__snake_case		(     self   :    List[str]     ,       lowercase__   :    Any     ,       lowercase__   :    Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   MgpstrForSceneTextRecognition(lowercase__)
        model.to(lowercase__)
        model.eval()
        with torch.no_grad():
               lowerCAmelCase__	      =   model(lowercase__)
        self.parent.assertEqual(
            generated_ids[0][0].shape     ,       (self.batch_size, self.max_token_length, self.num_character_labels))




 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.prepare_config_and_inputs()
        lowerCAmelCase__      ,   lowerCAmelCase__	      =   config_and_inputs
        lowerCAmelCase__	      =   {'pixel_values': pixel_values}
        return config, inputs_dict






@require_torch
class 							a_					(		A_			,	A_			,	unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_      					=   (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
 UpperCAmelCase_      					=   {'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
 UpperCAmelCase_      					=   False

 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False
 UpperCAmelCase_      					=   False




 def 						__snake_case		(     self   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   MgpstrModelTester(self)
        lowerCAmelCase__	      =   ConfigTester(self     ,       config_class=lowercase__     ,       has_text_modality=lowercase__)




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        self.config_tester.run_common_tests()




 def 						__snake_case		(     self   :    List[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__)




 @unittest.skip(reason='MgpstrModel does not use inputs_embeds')
 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()

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




 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()

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

               lowerCAmelCase__	      =   ['pixel_values']
               self.assertListEqual(arg_names[:1]     ,       lowercase__)




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







        '''simple docstring'''



        pass




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   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

               lowerCAmelCase__	      =   True
               lowerCAmelCase__	      =   model_class(lowercase__)
               self.assertTrue(model.is_gradient_checkpointing)




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        def check_hidden_states_output(lowercase__   :    Optional[Any]     ,       lowercase__   :    Union[str, Any]     ,       lowercase__   :    List[Any]):
               lowerCAmelCase__	      =   model_class(lowercase__)
               model.to(lowercase__)
               model.eval()

               with torch.no_grad():
                      lowerCAmelCase__	      =   model(**self._prepare_for_class(lowercase__     ,       lowercase__))

               lowerCAmelCase__	      =   outputs.hidden_states

               lowerCAmelCase__	      =   getattr(
                   self.model_tester     ,       'expected_num_hidden_layers'     ,       self.model_tester.num_hidden_layers + 1)
               self.assertEqual(len(lowercase__)     ,       lowercase__)

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

        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowerCAmelCase__	      =   True
               check_hidden_states_output(lowercase__     ,       lowercase__     ,       lowercase__)

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

               check_hidden_states_output(lowercase__     ,       lowercase__     ,       lowercase__)




 def 						__snake_case		(     self   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase__	      =   _config_zero_init(lowercase__)
        for model_class in self.all_model_classes:
               lowerCAmelCase__	      =   model_class(config=lowercase__)
               for name, param in model.named_parameters():
                      if isinstance(lowercase__     ,       (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 						__snake_case		(     self   :    int):







        '''simple docstring'''



        pass






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



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






 '''simple docstring'''






 @slow
 def 						__snake_case		(     self   :    Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__	      =   'alibaba-damo/mgp-str-base'
        lowerCAmelCase__	      =   MgpstrForSceneTextRecognition.from_pretrained(lowercase__).to(lowercase__)
        lowerCAmelCase__	      =   MgpstrProcessor.from_pretrained(lowercase__)

        lowerCAmelCase__	      =   prepare_img()
        lowerCAmelCase__	      =   processor(images=lowercase__     ,       return_tensors='pt').pixel_values.to(lowercase__)

        # forward pass
        with torch.no_grad():
               lowerCAmelCase__	      =   model(lowercase__)

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

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

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

        lowerCAmelCase__	      =   torch.tensor(
            [[[-39.5_397, -44.4_024, -36.1_844], [-61.4_709, -63.8_639, -58.3_454], [-74.0_225, -68.5_494, -71.2_164]]]     ,       device=lowercase__     ,       )

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





import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					str				,					lowercase__						:					Union[str, Any]				,					lowercase__						:					Optional[int]				,					lowercase__						:					Optional[Any]	):



			'''simple docstring'''





			a__					=  BertConfig.from_pretrained(
			    """bert-large-cased"""				,					vocab_size=lowercase__				,					max_position_embeddings=512				,					is_decoder=lowercase__				,					add_cross_attention=lowercase__				,					)
			a__					=  bert_config.to_dict()
			del bert_config_dict["type_vocab_size"]
			a__					=  BertGenerationConfig(**lowercase__	)
			if is_encoder:
						a__					=  BertGenerationEncoder(lowercase__	)
			else:
						a__					=  BertGenerationDecoder(lowercase__	)
			print(f'Building PyTorch model from configuration: {config}'	)

			# Load weights from tf checkpoint
			load_tf_weights_in_bert_generation(
			    lowercase__				,					lowercase__				,					model_class="""bert"""				,					is_encoder_named_decoder=lowercase__				,					is_encoder=lowercase__				,					)

			# Save pytorch-model
			print(f'Save PyTorch model and config to {pytorch_dump_path}'	)
			model.save_pretrained(lowercase__	)


if __name__ == "__main__":
	_lowercase				:					str     					=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_hub_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--is_encoder_named_decoder""",
	    action="""store_true""",
	    help="""If decoder has to be renamed to encoder in PyTorch model.""",
	)
	parser.add_argument("""--is_encoder""", action="""store_true""", help="""If model is an encoder.""")
	parser.add_argument("""--vocab_size""", default=5_03_58, type=int, help="""Vocab size of model""")
	_lowercase				:					List[str]     					=parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_hub_path,
	    args.pytorch_dump_path,
	    args.is_encoder_named_decoder,
	    args.vocab_size,
	    is_encoder=args.is_encoder,
	)

