


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   ,  A_   ,  A_=False   ,  A_=13   ,  A_=(32, 128)   ,  A_=4   ,  A_=3   ,  A_=27   ,  A_=38   ,  A_=99   ,  A_=99   ,  A_=32   ,  A_=5   ,  A_=4   ,  A_=4.0   ,  A_=257   ,  A_=None   ,  )    ->			Dict:
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=is_training
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=patch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=max_token_length
         __UpperCamelCase       				=num_character_labels
         __UpperCamelCase       				=num_bpe_labels
         __UpperCamelCase       				=num_wordpiece_labels
         __UpperCamelCase       				=hidden_size
         __UpperCamelCase       				=num_hidden_layers
         __UpperCamelCase       				=num_attention_heads
         __UpperCamelCase       				=mlp_ratio
         __UpperCamelCase       				=patch_embeds_hidden_size
         __UpperCamelCase       				=output_hidden_states






    def _a   (     self       )    ->			Optional[Any]:
         __UpperCamelCase       				=floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]       )
         __UpperCamelCase       				=self.get_config()
         return config, pixel_values






    def _a   (     self       )    ->			str:
         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   ,  A_   ,  A_       )    ->			Optional[Any]:
         __UpperCamelCase       				=MgpstrForSceneTextRecognition(A_       )
         model.to(A_       )
         model.eval()
         with torch.no_grad():
              __UpperCamelCase       				=model(A_       )
         self.parent.assertEqual(
             generated_ids[0][0].shape   ,  (self.batch_size, self.max_token_length, self.num_character_labels)       )






    def _a   (     self       )    ->			Optional[int]:
         __UpperCamelCase       				=self.prepare_config_and_inputs()
         __UpperCamelCase				, __UpperCamelCase       				=config_and_inputs
         __UpperCamelCase       				={'pixel_values': pixel_values}
         return config, inputs_dict






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



    """simple docstring"""






    UpperCAmelCase__		:						Union[str, Any]    =    (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
    UpperCAmelCase__		:						Optional[int]    =    {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
    UpperCAmelCase__		:						Optional[int]    =    False

    UpperCAmelCase__		:						Dict    =    False
    UpperCAmelCase__		:						str    =    False
    UpperCAmelCase__		:						List[str]    =    False
    UpperCAmelCase__		:						Union[str, Any]    =    False






    def _a   (     self       )    ->			Any:
         __UpperCamelCase       				=MgpstrModelTester(self       )
         __UpperCamelCase       				=ConfigTester(self   ,  config_class=A_   ,  has_text_modality=A_       )






    def _a   (     self       )    ->			Tuple:
         self.config_tester.run_common_tests()






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_       )






    @unittest.skip(reason='MgpstrModel does not use inputs_embeds'       )
    def _a   (     self       )    ->			Any:
         pass






    def _a   (     self       )    ->			str:
         __UpperCamelCase				, __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs_for_common()

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






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

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

              __UpperCamelCase       				=['pixel_values']
              self.assertListEqual(arg_names[:1]   ,  A_       )






    @unittest.skip(reason='MgpstrModel does not support feedforward chunking'       )
    def _a   (     self       )    ->			Tuple:
         pass






    def _a   (     self       )    ->			Tuple:
         __UpperCamelCase				, __UpperCamelCase       				=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

              __UpperCamelCase       				=True
              __UpperCamelCase       				=model_class(A_       )
              self.assertTrue(model.is_gradient_checkpointing       )






    def _a   (     self       )    ->			List[str]:
         def check_hidden_states_output(A_   ,  A_   ,  A_       ):
              __UpperCamelCase       				=model_class(A_       )
              model.to(A_       )
              model.eval()

              with torch.no_grad():
                   __UpperCamelCase       				=model(**self._prepare_for_class(A_   ,  A_       )       )

              __UpperCamelCase       				=outputs.hidden_states

              __UpperCamelCase       				=getattr(
                  self.model_tester   ,  'expected_num_hidden_layers'   ,  self.model_tester.num_hidden_layers + 1       )
              self.assertEqual(len(A_       )   ,  A_       )

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

         __UpperCamelCase				, __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __UpperCamelCase       				=True
              check_hidden_states_output(A_   ,  A_   ,  A_       )

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

              check_hidden_states_output(A_   ,  A_   ,  A_       )






    def _a   (     self       )    ->			Any:
         __UpperCamelCase				, __UpperCamelCase       				=self.model_tester.prepare_config_and_inputs_for_common()

         __UpperCamelCase       				=_config_zero_init(A_       )
         for model_class in self.all_model_classes:
              __UpperCamelCase       				=model_class(config=A_       )
              for name, param in model.named_parameters():
                   if isinstance(A_   ,  (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       )    ->			List[str]:
         pass

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






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



    """simple docstring"""






    @slow
    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				='alibaba-damo/mgp-str-base'
         __UpperCamelCase       				=MgpstrForSceneTextRecognition.from_pretrained(A_       ).to(A_       )
         __UpperCamelCase       				=MgpstrProcessor.from_pretrained(A_       )

         __UpperCamelCase       				=prepare_img()
         __UpperCamelCase       				=processor(images=A_   ,  return_tensors='pt'       ).pixel_values.to(A_       )

         # forward pass
         with torch.no_grad():
              __UpperCamelCase       				=model(A_       )

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

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

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

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

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









import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





