


import hashlib
import unittest
from typing import Dict

import numpy as np

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


if is_vision_available():
    from PIL import Image
else:

    class 							lowercase  :







         @staticmethod
         def 							lowercase__       (						*_lowercase :						List[Any]   ,   **_lowercase :						List[str]      ):
               pass




def    a   (							A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		hashlib.mda(image.tobytes()							)
      return m.hexdigest()[:1_0]




def    a   (							A__							)	-> Dict:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		np.array(A__							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		npimg.shape
      return {"hash": hashimage(A__							), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class 							lowercase  (						unittest.TestCase			):
     lowerCamelCase			:			Dict      = dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
     lowerCamelCase			:			Union[str, Any]      = dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)







     def 							lowercase__       (						self :						List[str]   ,   _lowercase :						Any   ,   _lowercase :						str   ,   _lowercase :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		MaskGenerationPipeline(model=_lowercase   ,   image_processor=_lowercase      )
           return image_segmenter, [
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
               "./tests/fixtures/tests_samples/COCO/000000039769.png",
           ]







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						int   ,   _lowercase :						Dict      ):
           pass







     @require_tf
     @unittest.skip('''Image segmentation not implemented in TF'''      )
     def 							lowercase__       (						self :						Tuple      ):
           pass







     @slow
     @require_torch
     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		pipeline('''mask-generation'''   ,   model='''facebook/sam-vit-huge'''      )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''   ,   points_per_batch=2_56      )

           # Shortening by hashing
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[]
           for i, o in enumerate(outputs['''masks''']      ):
                 new_outupt += [{"mask": mask_to_test_readable(_lowercase      ), "scores": outputs["scores"][i]}]

           # fmt: off
           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
                   {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.021},
                   {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
                   {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
                   {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
                   {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9967},
                   {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.993},
                   {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9909},
                   {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9879},
                   {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9834},
                   {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9716},
                   {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9612},
                   {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9599},
                   {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9552},
                   {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9532},
                   {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9516},
                   {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9499},
                   {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9483},
                   {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9464},
                   {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
                   {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_80, 6_40)}, '''scores''': 0.943},
                   {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9408},
                   {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9335},
                   {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9326},
                   {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_80, 6_40)}, '''scores''': 0.9262},
                   {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8999},
                   {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8986},
                   {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8984},
                   {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8873},
                   {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_80, 6_40)}, '''scores''': 0.8871}
               ]   ,   )
           # fmt: on







     @require_torch
     @slow
     def 							lowercase__       (						self :						Tuple      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		'''facebook/sam-vit-huge'''
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		pipeline('''mask-generation'''   ,   model=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		image_segmenter(
               '''http://images.cocodataset.org/val2017/000000039769.jpg'''   ,   pred_iou_thresh=1   ,   points_per_batch=2_56      )

           # Shortening by hashing
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[]
           for i, o in enumerate(outputs['''masks''']      ):
                 new_outupt += [{"mask": mask_to_test_readable(_lowercase      ), "scores": outputs["scores"][i]}]

           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0444},
                   {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0210},
                   {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0167},
                   {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0132},
                   {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_80, 6_40)}, '''scores''': 1.0053},
               ]   ,   )










import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						[
		    """encoder.version""",
		    """decoder.version""",
		    """model.encoder.version""",
		    """model.decoder.version""",
		    """_float_tensor""",
		    """decoder.output_projection.weight""",
		]
		for k in ignore_keys:
				state_dict.pop(__magic_name__       ,       __magic_name__	)






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__     ,      lowercase__	     =						emb.weight.shape
		lowercase__	     =						nn.Linear(__magic_name__       ,       __magic_name__       ,       bias=__magic_name__	)
		lowercase__	     =						emb.weight.data
		return lin_layer






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str="facebook/mbart-large-en-ro"       ,       __magic_name__	:				Any=False       ,       __magic_name__	:				int=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)["""model"""]
		remove_ignore_keys_(__magic_name__	)
		lowercase__	     =						state_dict["""encoder.embed_tokens.weight"""].shape[0]

		lowercase__	     =						MBartConfig.from_pretrained(__magic_name__       ,       vocab_size=__magic_name__	)
		if mbart_aa and finetuned:
				lowercase__	     =						"""relu"""

		lowercase__	     =						state_dict["""decoder.embed_tokens.weight"""]
		lowercase__	     =						MBartForConditionalGeneration(__magic_name__	)
		model.model.load_state_dict(__magic_name__	)

		if finetuned:
				lowercase__	     =						make_linear_from_emb(model.model.shared	)

		return model


if __name__ == "__main__":
	A			:						Optional[Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
	)
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument(
	    '--hf_config',
	    default='facebook/mbart-large-cc25',
	    type=str,
	    help='Which huggingface architecture to use: mbart-large',
	)
	parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
	parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
	A			:						Any        =  parser.parse_args()
	A			:						str        =  convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)

