





"""simple docstring"""






from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


A	     =					logging.get_logger(__name__)




@add_end_docstrings(_UpperCamelCase							)
class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					def __init__(  self		,     **_UpperCAmelCase       ):
									super().__init__(**_UpperCAmelCase       )

									if self.framework != "pt":
													raise ValueError(f"""The {self.__class__} is only available in PyTorch."""       )
									# No specific FOR_XXX available yet







					def __call__(  self		,     _UpperCAmelCase		,     **_UpperCAmelCase       ):
									return super().__call__(_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									__a	:  List[str]         =					{}
									if "candidate_labels" in kwargs:
													__a	:  int         =					kwargs['''candidate_labels''']
									if "hypothesis_template" in kwargs:
													__a	:  Tuple         =					kwargs['''hypothesis_template''']

									return preprocess_params, {}, {}







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=None		,     _UpperCAmelCase="This is a sound of {}."       ):
									if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ):
													if audio.startswith('''http://'''       ) or audio.startswith('''https://'''       ):
																	# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																	# like http_huggingface_co.png
																	__a	:  Dict         =					requests.get(_UpperCAmelCase       ).content
													else:
																	with open(_UpperCAmelCase		,     '''rb'''       ) as f:
																					__a	:  Optional[Any]         =					f.read()

									if isinstance(_UpperCAmelCase		,     _UpperCAmelCase       ):
													__a	:  Any         =					ffmpeg_read(_UpperCAmelCase		,     self.feature_extractor.sampling_rate       )

									if not isinstance(_UpperCAmelCase		,     np.ndarray       ):
													raise ValueError('''We expect a numpy ndarray as input'''       )
									if len(audio.shape       ) != 1:
													raise ValueError('''We expect a single channel audio input for ZeroShotAudioClassificationPipeline'''       )

									__a	:  Dict         =					self.feature_extractor(
									    [audio]		,     sampling_rate=self.feature_extractor.sampling_rate		,     return_tensors='''pt'''       )
									__a	:  List[Any]         =					candidate_labels
									__a	:  Tuple         =					[hypothesis_template.format(_UpperCAmelCase       ) for x in candidate_labels]
									__a	:  str         =					self.tokenizer(_UpperCAmelCase		,     return_tensors=self.framework		,     padding=_UpperCAmelCase       )
									__a	:  List[Any]         =					[text_inputs]
									return inputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  Any         =					model_inputs.pop('''candidate_labels'''       )
									__a	:  Union[str, Any]         =					model_inputs.pop('''text_inputs'''       )
									if isinstance(text_inputs[0]		,     _UpperCAmelCase       ):
													__a	:  Tuple         =					text_inputs[0]
									else:
													# Batching case.
													__a	:  Optional[int]         =					text_inputs[0][0]

									__a	:  str         =					self.model(**_UpperCAmelCase		,     **_UpperCAmelCase       )

									__a	:  List[str]         =					{
									    '''candidate_labels''': candidate_labels,
									    '''logits''': outputs.logits_per_audio,
									}
									return model_outputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					model_outputs.pop('''candidate_labels'''       )
									__a	:  Optional[int]         =					model_outputs['''logits'''][0]

									if self.framework == "pt":
													__a	:  List[Any]         =					logits.softmax(dim=0       )
													__a	:  List[Any]         =					probs.tolist()
									else:
													raise ValueError('''`tf` framework not supported.'''       )

									__a	:  Optional[int]         =					[
									    {'''score''': score, '''label''': candidate_label}
									    for score, candidate_label in sorted(zip(_UpperCAmelCase		,     _UpperCAmelCase       )		,     key=lambda _UpperCAmelCase       : -x[0]       )
									]
									return result






"""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	     =					{
    '''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       __A (     a_  :List[Any])						->						List[Any]:
				__a	:  List[Any]         =					{}
				state_dict.pop('''pixel_mean'''				,    a_)
				state_dict.pop('''pixel_std'''				,    a_)

				__a	:  List[Any]         =					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:
																__a	:  int         =					key.replace(a_				,    a_)

								if re.match(a_				,    a_):
												__a	:  Optional[Any]         =					int(re.match(a_				,    a_).group(2))
												if layer_nb == 0:
																__a	:  Any         =					key.replace('''layers.0'''				,    '''proj_in''')
												elif layer_nb == 1:
																__a	:  Dict         =					key.replace('''layers.1'''				,    '''layers.0''')
												elif layer_nb == 2:
																__a	:  Optional[int]         =					key.replace('''layers.2'''				,    '''proj_out''')

								__a	:  int         =					value

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

				return model_state_dict

def       __A (     a_  :Optional[int]				,    a_  :Optional[Any]				,    a_  :Dict				,    a_  :Optional[int]="ybelkada/segment-anything")						->						Dict:
				__a	:  Dict         =					hf_hub_download(a_				,    F"""checkpoints/{model_name}.pth""")

				if "sam_vit_b" in model_name:
								__a	:  List[str]         =					SamConfig()
				elif "sam_vit_l" in model_name:
								__a	:  List[Any]         =					SamVisionConfig(
								    hidden_size=10_24				,    num_hidden_layers=24				,    num_attention_heads=16				,    global_attn_indexes=[5, 11, 17, 23]				,    )

								__a	:  List[Any]         =					SamConfig(
								    vision_config=a_				,    )
				elif "sam_vit_h" in model_name:
								__a	:  List[str]         =					SamVisionConfig(
								    hidden_size=12_80				,    num_hidden_layers=32				,    num_attention_heads=16				,    global_attn_indexes=[7, 15, 23, 31]				,    )

								__a	:  Optional[int]         =					SamConfig(
								    vision_config=a_				,    )

				__a	:  int         =					torch.load(a_				,    map_location='''cpu''')
				__a	:  Tuple         =					replace_keys(a_)

				__a	:  Optional[int]         =					SamImageProcessor()

				__a	:  Any         =					SamProcessor(image_processor=a_)
				__a	:  Any         =					SamModel(a_)

				hf_model.load_state_dict(a_)
				__a	:  Dict         =					hf_model.to('''cuda''')

				__a	:  Tuple         =					'''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
				__a	:  str         =					Image.open(requests.get(a_				,    stream=a_).raw).convert('''RGB''')

				__a	:  Tuple         =					[[[4_00, 6_50]]]
				__a	:  Tuple         =					[[1]]

				__a	:  Tuple         =					processor(images=np.array(a_)				,    return_tensors='''pt''').to('''cuda''')

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

				if model_name == "sam_vit_h_4b8939":
								assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

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

								with torch.no_grad():
												__a	:  Optional[int]         =					hf_model(**a_)
								__a	:  Optional[int]         =					output.iou_scores.squeeze()

								assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

								__a	:  str         =					((75, 2_75, 17_25, 8_50),)

								__a	:  List[str]         =					processor(images=np.array(a_)				,    input_boxes=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  Any         =					hf_model(**a_)
								__a	:  Any         =					output.iou_scores.squeeze()

								assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

								# Test with 2 points and 1 image.
								__a	:  int         =					[[[4_00, 6_50], [8_00, 6_50]]]
								__a	:  Dict         =					[[1, 1]]

								__a	:  Optional[Any]         =					processor(
								    images=np.array(a_)				,    input_points=a_				,    input_labels=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  int         =					hf_model(**a_)
								__a	:  Any         =					output.iou_scores.squeeze()

								assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()
		A	     =					['''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	     =					parser.parse_args()

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