






"""simple docstring"""



import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


__A						:			Dict    							=						{
    '''iou_prediction_head.layers.0''': '''iou_prediction_head.proj_in''',
    '''iou_prediction_head.layers.1''': '''iou_prediction_head.layers.0''',
    '''iou_prediction_head.layers.2''': '''iou_prediction_head.proj_out''',
    '''mask_decoder.output_upscaling.0''': '''mask_decoder.upscale_conv1''',
    '''mask_decoder.output_upscaling.1''': '''mask_decoder.upscale_layer_norm''',
    '''mask_decoder.output_upscaling.3''': '''mask_decoder.upscale_conv2''',
    '''mask_downscaling.0''': '''mask_embed.conv1''',
    '''mask_downscaling.1''': '''mask_embed.layer_norm1''',
    '''mask_downscaling.3''': '''mask_embed.conv2''',
    '''mask_downscaling.4''': '''mask_embed.layer_norm2''',
    '''mask_downscaling.6''': '''mask_embed.conv3''',
    '''point_embeddings''': '''point_embed''',
    '''pe_layer.positional_encoding_gaussian_matrix''': '''shared_embedding.positional_embedding''',
    '''image_encoder''': '''vision_encoder''',
    '''neck.0''': '''neck.conv1''',
    '''neck.1''': '''neck.layer_norm1''',
    '''neck.2''': '''neck.conv2''',
    '''neck.3''': '''neck.layer_norm2''',
    '''patch_embed.proj''': '''patch_embed.projection''',
    '''.norm''': '''.layer_norm''',
    '''blocks''': '''layers''',
}



def 		lowercase       (							__snake_case							:					List[Any]      ):
		lowercase_					:					List[str]							=  {}
		state_dict.pop('''pixel_mean'''						,   SCREAMING_SNAKE_CASE__      )
		state_dict.pop('''pixel_std'''						,   SCREAMING_SNAKE_CASE__      )

		lowercase_					:					int							=  R'''.*.output_hypernetworks_mlps.(\d+).layers.(\d+).*'''

		for key, value in state_dict.items():
				for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
						if key_to_modify in key:
								lowercase_					:					int							=  key.replace(SCREAMING_SNAKE_CASE__						,   SCREAMING_SNAKE_CASE__      )

				if re.match(SCREAMING_SNAKE_CASE__						,   SCREAMING_SNAKE_CASE__      ):
						lowercase_					:					Union[str, Any]							=  int(re.match(SCREAMING_SNAKE_CASE__						,   SCREAMING_SNAKE_CASE__      ).group(2      )      )
						if layer_nb == 0:
								lowercase_					:					Optional[int]							=  key.replace('''layers.0'''						,   '''proj_in'''      )
						elif layer_nb == 1:
								lowercase_					:					Optional[Any]							=  key.replace('''layers.1'''						,   '''layers.0'''      )
						elif layer_nb == 2:
								lowercase_					:					Any							=  key.replace('''layers.2'''						,   '''proj_out'''      )

				lowercase_					:					Tuple							=  value

		lowercase_					:					Union[str, Any]							=  model_state_dict[
		    '''prompt_encoder.shared_embedding.positional_embedding'''
		]

		return model_state_dict



def 		lowercase       (							__snake_case							:					Any						,   __snake_case							:					Optional[int]						,   __snake_case							:					int						,   __snake_case							:					Optional[int]="ybelkada/segment-anything"      ):
		lowercase_					:					Tuple							=  hf_hub_download(SCREAMING_SNAKE_CASE__						,   F'''checkpoints/{model_name}.pth'''      )

		if "sam_vit_b" in model_name:
				lowercase_					:					List[str]							=  SamConfig()
		elif "sam_vit_l" in model_name:
				lowercase_					:					Union[str, Any]							=  SamVisionConfig(
				    hidden_size=1_0_2_4						,   num_hidden_layers=2_4						,   num_attention_heads=1_6						,   global_attn_indexes=[5, 1_1, 1_7, 2_3]						,   )

				lowercase_					:					Tuple							=  SamConfig(
				    vision_config=SCREAMING_SNAKE_CASE__						,   )
		elif "sam_vit_h" in model_name:
				lowercase_					:					str							=  SamVisionConfig(
				    hidden_size=1_2_8_0						,   num_hidden_layers=3_2						,   num_attention_heads=1_6						,   global_attn_indexes=[7, 1_5, 2_3, 3_1]						,   )

				lowercase_					:					List[Any]							=  SamConfig(
				    vision_config=SCREAMING_SNAKE_CASE__						,   )

		lowercase_					:					Dict							=  torch.load(SCREAMING_SNAKE_CASE__						,   map_location='''cpu'''      )
		lowercase_					:					int							=  replace_keys(SCREAMING_SNAKE_CASE__      )

		lowercase_					:					Tuple							=  SamImageProcessor()

		lowercase_					:					Optional[Any]							=  SamProcessor(image_processor=SCREAMING_SNAKE_CASE__      )
		lowercase_					:					Dict							=  SamModel(SCREAMING_SNAKE_CASE__      )

		hf_model.load_state_dict(SCREAMING_SNAKE_CASE__      )
		lowercase_					:					Any							=  hf_model.to('''cuda'''      )

		lowercase_					:					List[Any]							=  '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
		lowercase_					:					Any							=  Image.open(requests.get(SCREAMING_SNAKE_CASE__						,   stream=SCREAMING_SNAKE_CASE__      ).raw      ).convert('''RGB'''      )

		lowercase_					:					Any							=  [[[4_0_0, 6_5_0]]]
		lowercase_					:					Union[str, Any]							=  [[1]]

		lowercase_					:					Dict							=  processor(images=np.array(SCREAMING_SNAKE_CASE__      )						,   return_tensors='''pt'''      ).to('''cuda'''      )

		with torch.no_grad():
				lowercase_					:					int							=  hf_model(**SCREAMING_SNAKE_CASE__      )
		lowercase_					:					List[str]							=  output.iou_scores.squeeze()

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.579890251159668

				lowercase_					:					Any							=  processor(
				    images=np.array(SCREAMING_SNAKE_CASE__      )						,   input_points=SCREAMING_SNAKE_CASE__						,   input_labels=SCREAMING_SNAKE_CASE__						,   return_tensors='''pt'''      ).to('''cuda'''      )

				with torch.no_grad():
						lowercase_					:					str							=  hf_model(**SCREAMING_SNAKE_CASE__      )
				lowercase_					:					Optional[Any]							=  output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9712603092193604

				lowercase_					:					Optional[int]							=  ((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

				lowercase_					:					Union[str, Any]							=  processor(images=np.array(SCREAMING_SNAKE_CASE__      )						,   input_boxes=SCREAMING_SNAKE_CASE__						,   return_tensors='''pt'''      ).to('''cuda'''      )

				with torch.no_grad():
						lowercase_					:					str							=  hf_model(**SCREAMING_SNAKE_CASE__      )
				lowercase_					:					Optional[int]							=  output.iou_scores.squeeze()

				assert scores[-1].item() == 0.8686015605926514

				# Test with 2 points and 1 image.
				lowercase_					:					int							=  [[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
				lowercase_					:					str							=  [[1, 1]]

				lowercase_					:					str							=  processor(
				    images=np.array(SCREAMING_SNAKE_CASE__      )						,   input_points=SCREAMING_SNAKE_CASE__						,   input_labels=SCREAMING_SNAKE_CASE__						,   return_tensors='''pt'''      ).to('''cuda'''      )

				with torch.no_grad():
						lowercase_					:					List[str]							=  hf_model(**SCREAMING_SNAKE_CASE__      )
				lowercase_					:					List[Any]							=  output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
						__A						:			str    							=						argparse.ArgumentParser()
						__A						:			Any    							=						['''sam_vit_b_01ec64''', '''sam_vit_h_4b8939''', '''sam_vit_l_0b3195''']
						parser.add_argument(
						    '''--model_name''',
						    default='''sam_vit_h_4b8939''',
						    choices=choices,
						    type=str,
						    help='''Path to hf config.json of model to convert''',
						)
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						    help='''Whether to push the model and processor to the hub after converting''',
						)
						parser.add_argument(
						    '''--model_hub_id''',
						    default='''ybelkada/segment-anything''',
						    choices=choices,
						    type=str,
						    help='''Path to hf config.json of model to convert''',
						)

						__A						:			Optional[Any]    							=						parser.parse_args()

						convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)





'''simple docstring'''
import datasets

from .evaluate import evaluate


lowercase__						       =      "\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

lowercase__						       =      "\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

lowercase__						       =      "\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,					_KWARGS_DESCRIPTION					)
class 	snake_case__       (      datasets.Metric					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Tuple:






				"""simple docstring"""







				return datasets.MetricInfo(
				    description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
				        {
				            '''predictions''': {
				                '''id''': datasets.Value('''string'''      ),
				                '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''      )      ),
				            },
				            '''references''': {
				                '''id''': datasets.Value('''string'''      ),
				                '''answers''': datasets.features.Sequence(
				                    {
				                        '''text''': datasets.Value('''string'''      ),
				                        '''answer_start''': datasets.Value('''int32'''      ),
				                    }      ),
				            },
				        }      )  ,	codebase_urls=['''https://www.atticusprojectai.org/cuad''']  ,	reference_urls=['''https://www.atticusprojectai.org/cuad''']  ,	)



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
				snake_case  :       Dict     			=						[
				    {
				        '''paragraphs''': [
				            {
				                '''qas''': [
				                    {
				                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
				                        '''id''': ref['''id'''],
				                    }
				                    for ref in references
				                ]
				            }
				        ]
				    }
				]
				snake_case  :       int     			=						evaluate(dataset=UpperCamelCase__  ,	predictions=UpperCamelCase__      )
				return score


