




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      [
      '''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 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__				,      lowerCAmelCase__										=      emb.weight.shape
  lowerCAmelCase__										=      nn.Linear(__lowerCAmelCase   , __lowerCAmelCase   , bias=__lowerCAmelCase				)
  lowerCAmelCase__										=      emb.weight.data
  return lin_layer
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      torch.load(__lowerCAmelCase   , map_location='''cpu'''				)
  lowerCAmelCase__										=      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
  lowerCAmelCase__										=      mam_aaa['''model''']
  remove_ignore_keys_(__lowerCAmelCase				)
  lowerCAmelCase__										=      state_dict['''encoder.embed_tokens.weight'''].shape[0]

  lowerCAmelCase__										=      MaMaaaConfig(
      vocab_size=__lowerCAmelCase   , max_position_embeddings=10_24   , 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'''   , )

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

  return model


if __name__ == "__main__":
      __magic_name__  :  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.""")
      __magic_name__  :  Optional[Any]					=       parser.parse_args()
      __magic_name__  :  Optional[Any]					=       convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
      model.save_pretrained(args.pytorch_dump_folder_path)







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     SCREAMING_SNAKE_CASE__		:
           @staticmethod
           def        A__      (						*__lowerCamelCase	:		List[str]       ,						**__lowerCamelCase	:		Optional[int]						):



             """simple docstring"""
             pass
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      hashlib.mda(image.tobytes()				)
  return m.hexdigest()[:10]
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      np.array(__lowerCAmelCase				)
  lowerCAmelCase__										=      npimg.shape
  return {"hash": hashimage(__lowerCAmelCase				), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     lowercase_ :      Any				= dict(
         (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
     lowercase_ :      str				= dict(
         (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      MaskGenerationPipeline(model=__lowerCamelCase       ,						image_processor=__lowerCamelCase						)
       return image_segmenter, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Optional[int]						):



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



       """simple docstring"""
       pass
     @slow
     @require_torch
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      pipeline('''mask-generation'''       ,						model='''facebook/sam-vit-huge'''						)

       lowerCAmelCase__										=      image_segmenter('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,						points_per_batch=2_56						)

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

       # fmt: off
       self.assertEqual(
           nested_simplify(__lowerCamelCase       ,						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        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      '''facebook/sam-vit-huge'''
       lowerCAmelCase__										=      pipeline('''mask-generation'''       ,						model=__lowerCamelCase						)

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

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

       self.assertEqual(
           nested_simplify(__lowerCamelCase       ,						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},
           ]       ,						)

