
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 				A__   (       *A__     ,**A__):
																			pass



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		hashlib.mda(image.tobytes()	)
					return m.hexdigest()[:10]



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		np.array(lowerCAmelCase__	)
					lowercase					=		npimg.shape
					return {"hash": hashimage(lowerCAmelCase__	), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class        lowercase	(      unittest.TestCase	):
							lowercase_ : Tuple       =dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if MODEL_FOR_MASK_GENERATION_MAPPING else [])	)
							lowercase_ : List[str]       =dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])	)




							def 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		MaskGenerationPipeline(model=A__     ,image_processor=A__)
												return image_segmenter, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]




							def 				A__   (       self     ,A__     ,A__):
												pass




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




							@slow
							@require_torch
							def 				A__   (       self):
												lowercase					=		pipeline('''mask-generation'''     ,model='''facebook/sam-vit-huge''')

												lowercase					=		image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''     ,points_per_batch=2_5_6)

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

												# fmt: off
												self.assertEqual(
												    nested_simplify(A__     ,decimals=4)     ,[
												        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0444},
												        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.021},
												        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0167},
												        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0132},
												        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0053},
												        {'''mask''': {'''hash''': '''e2d0b7a0b7''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9967},
												        {'''mask''': {'''hash''': '''453c7844bd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.993},
												        {'''mask''': {'''hash''': '''3d44f2926d''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9909},
												        {'''mask''': {'''hash''': '''64033ddc3f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9879},
												        {'''mask''': {'''hash''': '''801064ff79''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9834},
												        {'''mask''': {'''hash''': '''6172f276ef''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9716},
												        {'''mask''': {'''hash''': '''b49e60e084''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9612},
												        {'''mask''': {'''hash''': '''a811e775fd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9599},
												        {'''mask''': {'''hash''': '''a6a8ebcf4b''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9552},
												        {'''mask''': {'''hash''': '''9d8257e080''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9532},
												        {'''mask''': {'''hash''': '''32de6454a8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9516},
												        {'''mask''': {'''hash''': '''af3d4af2c8''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9499},
												        {'''mask''': {'''hash''': '''3c6db475fb''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9483},
												        {'''mask''': {'''hash''': '''c290813fb9''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9464},
												        {'''mask''': {'''hash''': '''b6f0b8f606''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.943},
												        {'''mask''': {'''hash''': '''92ce16bfdf''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.943},
												        {'''mask''': {'''hash''': '''c749b25868''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9408},
												        {'''mask''': {'''hash''': '''efb6cab859''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9335},
												        {'''mask''': {'''hash''': '''1ff2eafb30''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9326},
												        {'''mask''': {'''hash''': '''788b798e24''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.9262},
												        {'''mask''': {'''hash''': '''abea804f0e''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8999},
												        {'''mask''': {'''hash''': '''7b9e8ddb73''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8986},
												        {'''mask''': {'''hash''': '''cd24047c8a''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8984},
												        {'''mask''': {'''hash''': '''6943e6bcbd''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8873},
												        {'''mask''': {'''hash''': '''b5f47c9191''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 0.8871}
												    ]     ,)
												# fmt: on





							@require_torch
							@slow
							def 				A__   (       self):
												lowercase					=		'''facebook/sam-vit-huge'''
												lowercase					=		pipeline('''mask-generation'''     ,model=A__)

												lowercase					=		image_segmenter(
												    '''http://images.cocodataset.org/val2017/000000039769.jpg'''     ,pred_iou_thresh=1     ,points_per_batch=2_5_6)

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

												self.assertEqual(
												    nested_simplify(A__     ,decimals=4)     ,[
												        {'''mask''': {'''hash''': '''115ad19f5f''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0444},
												        {'''mask''': {'''hash''': '''6affa964c6''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0210},
												        {'''mask''': {'''hash''': '''dfe28a0388''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0167},
												        {'''mask''': {'''hash''': '''c0a5f4a318''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0132},
												        {'''mask''': {'''hash''': '''fe8065c197''', '''shape''': (4_8_0, 6_4_0)}, '''scores''': 1.0053},
												    ]     ,)






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		MaMaaaConfig(
					    vocab_size=lowerCAmelCase__				,    max_position_embeddings=1024				,    encoder_layers=args.encoder_layers				,    decoder_layers=args.decoder_layers				,    encoder_attention_heads=args.encoder_attention_heads				,    decoder_attention_heads=args.decoder_attention_heads				,    encoder_ffn_dim=args.encoder_ffn_embed_dim				,    decoder_ffn_dim=args.decoder_ffn_embed_dim				,    d_model=args.encoder_embed_dim				,    encoder_layerdrop=args.encoder_layerdrop				,    decoder_layerdrop=args.decoder_layerdrop				,    dropout=args.dropout				,    attention_dropout=args.attention_dropout				,    activation_dropout=args.activation_dropout				,    activation_function='''relu'''				,    )

					lowercase					=		state_dict['''decoder.embed_tokens.weight''']
					lowercase					=		MaMaaaForConditionalGeneration(lowerCAmelCase__	)
					model.model.load_state_dict(lowerCAmelCase__				,    strict=lowerCAmelCase__	)
					lowercase					=		make_linear_from_emb(model.model.shared	)

					return model


if __name__ == "__main__":
							lowercase__	:Optional[int]										=					argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("fairseq_path", type=str, help="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.")
							lowercase__	:Tuple										=					parser.parse_args()
							lowercase__	:int										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)




