'''simple docstring'''





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def       lowerCAmelCase_							(   snake_case_			:					Tuple     )       -> List[Any]:


     '''simple docstring'''




     if "model" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("model."   ,       ""     )
     if "norm1" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("norm1"   ,       "attention.output.LayerNorm"     )
     if "norm2" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("norm2"   ,       "output.LayerNorm"     )
     if "norm" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("norm"   ,       "LayerNorm"     )
     if "transformer" in orig_key:
          UpperCAmelCase_			       =  orig_key.split("."     )[0].split("_"     )[-1]
          UpperCAmelCase_			       =  orig_key.replace(f"""transformer_{layer_num}"""   ,       f"""encoder.layer.{layer_num}"""     )
     if "mha.attn" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("mha.attn"   ,       "attention.self"     )
     if "mha" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("mha"   ,       "attention"     )
     if "W_q" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("W_q"   ,       "self.query"     )
     if "W_k" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("W_k"   ,       "self.key"     )
     if "W_v" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("W_v"   ,       "self.value"     )
     if "ff1" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("ff1"   ,       "intermediate.dense"     )
     if "ff2" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("ff2"   ,       "output.dense"     )
     if "ff" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("ff"   ,       "output.dense"     )
     if "mlm_class" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("mlm.mlm_class"   ,       "cls.predictions.decoder"     )
     if "mlm" in orig_key:
          UpperCAmelCase_			       =  orig_key.replace("mlm"   ,       "cls.predictions.transform"     )
     if "cls" not in orig_key:
          UpperCAmelCase_			       =  "yoso." + orig_key

     return orig_key





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					int     )       -> Any:


     '''simple docstring'''




     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_			       =  orig_state_dict.pop(snake_case_     )

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               UpperCAmelCase_			       =  val

     UpperCAmelCase_			       =  orig_state_dict["cls.predictions.decoder.bias"]
     UpperCAmelCase_			       =  torch.arange(snake_case_     ).expand((1, -1)     ) + 2

     return orig_state_dict





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					int     )       -> Any:


     '''simple docstring'''




     UpperCAmelCase_			       =  torch.load(snake_case_   ,       map_location="cpu"     )["model_state_dict"]
     UpperCAmelCase_			       =  YosoConfig.from_json_file(snake_case_     )
     UpperCAmelCase_			       =  YosoForMaskedLM(snake_case_     )

     UpperCAmelCase_			       =  convert_checkpoint_helper(config.max_position_embeddings   ,       snake_case_     )

     print(model.load_state_dict(snake_case_     )     )
     model.eval()
     model.save_pretrained(snake_case_     )

     print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	int    =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for YOSO model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    SCREAMING_SNAKE_CASE_:	Optional[int]    =parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:


			class 						lowercase    :
									'''simple docstring'''


									@staticmethod
									def       lowerCamelCase_				(  *snake_case    :				Dict       ,	**snake_case    :				List[Any]	):



														'''simple docstring'''


														pass


@is_pipeline_test
@require_torch
@require_vision
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Any		=		MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING







						def       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				List[str]       ,	snake_case    :				int       ,	snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipeline('visual-question-answering'       ,	model='hf-internal-testing/tiny-vilt-random-vqa'	)
											SCREAMING_SNAKE_CASE							:     Any						=				[
											    {
											        'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
											        'question': 'How many cats are there?',
											    },
											    {
											        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
											        'question': 'How many cats are there?',
											    },
											]
											return vqa_pipeline, examples







						def       lowerCamelCase_				(  self    :				int       ,	snake_case    :				int       ,	snake_case    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				vqa_pipeline(snake_case       ,	top_k=1	)
											self.assertEqual(
											    snake_case       ,	[
											        [{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}],
											        [{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}],
											    ]       ,	)







						@require_torch
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline('visual-question-answering'       ,	model='hf-internal-testing/tiny-vilt-random-vqa'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
											SCREAMING_SNAKE_CASE							:     Any						=				'How many cats are there?'

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				vqa_pipeline(image=snake_case       ,	question='How many cats are there?'       ,	top_k=2	)
											self.assertEqual(
											    snake_case       ,	[{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}, {'score': ANY(snake_case	), 'answer': ANY(snake_case	)}]	)

											SCREAMING_SNAKE_CASE							:     int						=				vqa_pipeline({'image': image, 'question': question}       ,	top_k=2	)
											self.assertEqual(
											    snake_case       ,	[{'score': ANY(snake_case	), 'answer': ANY(snake_case	)}, {'score': ANY(snake_case	), 'answer': ANY(snake_case	)}]	)







						@slow
						@require_torch
						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				pipeline('visual-question-answering'       ,	model='dandelin/vilt-b32-finetuned-vqa'	)
											SCREAMING_SNAKE_CASE							:     int						=				'./tests/fixtures/tests_samples/COCO/000000039769.png'
											SCREAMING_SNAKE_CASE							:     Dict						=				'How many cats are there?'

											SCREAMING_SNAKE_CASE							:     int						=				vqa_pipeline(image=snake_case       ,	question=snake_case       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				vqa_pipeline({'image': image, 'question': question}       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]	)

											SCREAMING_SNAKE_CASE							:     Any						=				vqa_pipeline(
											    [{'image': image, 'question': question}, {'image': image, 'question': question}]       ,	top_k=2	)
											self.assertEqual(
											    nested_simplify(snake_case       ,	decimals=4	)       ,	[[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2       ,	)







						@require_tf
						@unittest.skip('Visual question answering not implemented in TF'	)
						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											pass