




'''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,
)


lowerCAmelCase__          =						{
    '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       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		UpperCamelCase__				:					Union[str, Any]					=					{}
		state_dict.pop('pixel_mean'     ,      lowerCamelCase_)
		state_dict.pop('pixel_std'     ,      lowerCamelCase_)

		UpperCamelCase__				:					str					=					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:
								UpperCamelCase__				:					int					=					key.replace(lowerCamelCase_     ,      lowerCamelCase_)

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

				UpperCamelCase__				:					Any					=					value

		UpperCamelCase__				:					int					=					model_state_dict[
		    'prompt_encoder.shared_embedding.positional_embedding'
		]

		return model_state_dict







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_="ybelkada/segment-anything")							->     Any:
		UpperCamelCase__				:					Dict					=					hf_hub_download(lowerCamelCase_     ,      f'checkpoints/{model_name}.pth')

		if "sam_vit_b" in model_name:
				UpperCamelCase__				:					Tuple					=					SamConfig()
		elif "sam_vit_l" in model_name:
				UpperCamelCase__				:					Union[str, Any]					=					SamVisionConfig(
				    hidden_size=1_024     ,      num_hidden_layers=24     ,      num_attention_heads=16     ,      global_attn_indexes=[5, 11, 17, 23]     ,      )

				UpperCamelCase__				:					Tuple					=					SamConfig(
				    vision_config=lowerCamelCase_     ,      )
		elif "sam_vit_h" in model_name:
				UpperCamelCase__				:					Optional[Any]					=					SamVisionConfig(
				    hidden_size=1_280     ,      num_hidden_layers=32     ,      num_attention_heads=16     ,      global_attn_indexes=[7, 15, 23, 31]     ,      )

				UpperCamelCase__				:					str					=					SamConfig(
				    vision_config=lowerCamelCase_     ,      )

		UpperCamelCase__				:					Optional[int]					=					torch.load(lowerCamelCase_     ,      map_location='cpu')
		UpperCamelCase__				:					Union[str, Any]					=					replace_keys(lowerCamelCase_)

		UpperCamelCase__				:					int					=					SamImageProcessor()

		UpperCamelCase__				:					Dict					=					SamProcessor(image_processor=lowerCamelCase_)
		UpperCamelCase__				:					Union[str, Any]					=					SamModel(lowerCamelCase_)

		hf_model.load_state_dict(lowerCamelCase_)
		UpperCamelCase__				:					Dict					=					hf_model.to('cuda')

		UpperCamelCase__				:					Dict					=					'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
		UpperCamelCase__				:					Any					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw).convert('RGB')

		UpperCamelCase__				:					Dict					=					[[[400, 650]]]
		UpperCamelCase__				:					List[str]					=					[[1]]

		UpperCamelCase__				:					Any					=					processor(images=np.array(lowerCamelCase_)     ,      return_tensors='pt').to('cuda')

		with torch.no_grad():
				UpperCamelCase__				:					Tuple					=					hf_model(**lowerCamelCase_)
		UpperCamelCase__				:					str					=					output.iou_scores.squeeze()

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.579_890_251_159_668

				UpperCamelCase__				:					Union[str, Any]					=					processor(
				    images=np.array(lowerCamelCase_)     ,      input_points=lowerCamelCase_     ,      input_labels=lowerCamelCase_     ,      return_tensors='pt').to('cuda')

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

				assert scores[-1].item() == 0.9_712_603_092_193_604

				UpperCamelCase__				:					Tuple					=					((75, 275, 1_725, 850),)

				UpperCamelCase__				:					str					=					processor(images=np.array(lowerCamelCase_)     ,      input_boxes=lowerCamelCase_     ,      return_tensors='pt').to('cuda')

				with torch.no_grad():
						UpperCamelCase__				:					Tuple					=					hf_model(**lowerCamelCase_)
				UpperCamelCase__				:					Union[str, Any]					=					output.iou_scores.squeeze()

				assert scores[-1].item() == 0.8_686_015_605_926_514

				# Test with 2 points and 1 image.
				UpperCamelCase__				:					str					=					[[[400, 650], [800, 650]]]
				UpperCamelCase__				:					Optional[int]					=					[[1, 1]]

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

				with torch.no_grad():
						UpperCamelCase__				:					List[str]					=					hf_model(**lowerCamelCase_)
				UpperCamelCase__				:					Optional[int]					=					output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		lowerCAmelCase__          =						['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',
		)

		lowerCAmelCase__          =						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 argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__          =						logging.get_logger(__name__)

lowerCAmelCase__          =						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
lowerCAmelCase__          =						[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     str:
		for attribute in key.split('.'):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								UpperCamelCase__				:					str					=					'lm_head'

				UpperCamelCase__				:					Optional[Any]					=					getattr(lowerCamelCase_     ,      lowerCamelCase_)

		if weight_type is not None:
				UpperCamelCase__				:					List[Any]					=					getattr(lowerCamelCase_     ,      lowerCamelCase_).shape
		else:
				UpperCamelCase__				:					List[str]					=					hf_pointer.shape

		assert hf_shape == value.shape, (
		    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
		    f' {value.shape} for {full_name}'
		)

		if weight_type == "weight":
				UpperCamelCase__				:					Optional[Any]					=					value
		elif weight_type == "weight_g":
				UpperCamelCase__				:					Union[str, Any]					=					value
		elif weight_type == "weight_v":
				UpperCamelCase__				:					List[Any]					=					value
		elif weight_type == "bias":
				UpperCamelCase__				:					Any					=					value
		else:
				UpperCamelCase__				:					Optional[int]					=					value

		logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.')







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					List[Any]					=					[]
		UpperCamelCase__				:					int					=					fairseq_model.state_dict()

		UpperCamelCase__				:					int					=					hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase__				:					Union[str, Any]					=					False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      hf_model.config.feat_extract_norm == 'group'     ,      )
						UpperCamelCase__				:					List[Any]					=					True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase__				:					List[Any]					=					'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('w2v_model.')[-1] == name.split('.')[0]:
										UpperCamelCase__				:					Any					=					True
										if "*" in mapped_key:
												UpperCamelCase__				:					Any					=					name.split(lowerCamelCase_)[0].split('.')[-2]
												UpperCamelCase__				:					Union[str, Any]					=					mapped_key.replace('*'     ,      lowerCamelCase_)
										if "weight_g" in name:
												UpperCamelCase__				:					int					=					'weight_g'
										elif "weight_v" in name:
												UpperCamelCase__				:					Any					=					'weight_v'
										elif "bias" in name:
												UpperCamelCase__				:					Union[str, Any]					=					'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												UpperCamelCase__				:					Any					=					'weight'
										else:
												UpperCamelCase__				:					Tuple					=					None
										set_recursively(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
								continue
				if not is_used:
						unused_weights.append(lowerCamelCase_)

		logger.warning(f'Unused weights: {unused_weights}')







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Dict					=					full_name.split('conv_layers.')[-1]
		UpperCamelCase__				:					List[Any]					=					name.split('.')
		UpperCamelCase__				:					Any					=					int(items[0])
		UpperCamelCase__				:					int					=					int(items[1])

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    f'{full_name} has size {value.shape}, but'
						    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
						)
						UpperCamelCase__				:					Tuple					=					value
						logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.')
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    f'{full_name} has size {value.shape}, but'
						    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
						)
						UpperCamelCase__				:					int					=					value
						logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.')
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
						    " found."
						)
						UpperCamelCase__				:					Optional[Any]					=					value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    f'{full_name} has size {value.shape}, but'
						    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
						)
						UpperCamelCase__				:					List[Any]					=					value
						logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
		else:
				unused_weights.append(lowerCamelCase_)







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=None     ,      lowerCamelCase_=None     ,      lowerCamelCase_=True)							->     Tuple:
		if config_path is not None:
				UpperCamelCase__				:					Optional[Any]					=					UniSpeechConfig.from_pretrained(lowerCamelCase_)
		else:
				UpperCamelCase__				:					int					=					UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						UpperCamelCase__				:					Union[str, Any]					=					Dictionary.load_from_json(lowerCamelCase_)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						UpperCamelCase__				:					List[Any]					=					target_dict.pad_index
						UpperCamelCase__				:					Dict					=					target_dict.bos_index
						UpperCamelCase__				:					Union[str, Any]					=					target_dict.eos_index
						UpperCamelCase__				:					Tuple					=					len(target_dict.symbols)
						UpperCamelCase__				:					Dict					=					os.path.join(lowerCamelCase_     ,      'vocab.json')
						if not os.path.isdir(lowerCamelCase_):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCamelCase_))
								return
						os.makedirs(lowerCamelCase_     ,      exist_ok=lowerCamelCase_)
						UpperCamelCase__				:					Optional[int]					=					target_dict.indices

						# fairseq has the <pad> and <s> switched
						UpperCamelCase__				:					Any					=					42
						UpperCamelCase__				:					List[str]					=					43
						with open(lowerCamelCase_     ,      'w'     ,      encoding='utf-8') as vocab_handle:
								json.dump(lowerCamelCase_     ,      lowerCamelCase_)
						UpperCamelCase__				:					Optional[int]					=					WavaVecaPhonemeCTCTokenizer(
						    lowerCamelCase_     ,      unk_token=target_dict.unk_word     ,      pad_token=target_dict.pad_word     ,      bos_token=target_dict.bos_word     ,      eos_token=target_dict.eos_word     ,      word_delimiter_token='|'     ,      do_lower_case=lowerCamelCase_     ,      )
						UpperCamelCase__				:					Optional[Any]					=					True if config.feat_extract_norm == 'layer' else False
						UpperCamelCase__				:					Union[str, Any]					=					WavaVecaFeatureExtractor(
						    feature_size=1     ,      sampling_rate=16_000     ,      padding_value=0     ,      do_normalize=lowerCamelCase_     ,      return_attention_mask=lowerCamelCase_     ,      )
						UpperCamelCase__				:					Tuple					=					WavaVecaProcessor(feature_extractor=lowerCamelCase_     ,      tokenizer=lowerCamelCase_)
						processor.save_pretrained(lowerCamelCase_)

				UpperCamelCase__				:					Dict					=					UniSpeechForCTC(lowerCamelCase_)
		else:
				UpperCamelCase__				:					List[Any]					=					UniSpeechForPreTraining(lowerCamelCase_)

		if is_finetuned:
				UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					int					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]     ,      arg_overrides={'data': '/'.join(dict_path.split('/')[:-1]), 'w2v_path': checkpoint_path})
		else:
				UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					str					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

		UpperCamelCase__				:					int					=					model[0].eval()

		recursively_load_weights(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

		hf_unispeech.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		lowerCAmelCase__          =						parser.parse_args()
		convert_unispeech_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)




