


'''simple docstring'''



import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__A	=logging.get_logger('transformers.models.speecht5')

__A	={
    'speech_encoder_prenet.layer_norm': 'speecht5.encoder.prenet.feature_projection.layer_norm',
    'speech_encoder_prenet.post_extract_proj': 'speecht5.encoder.prenet.feature_projection.projection',
    'speech_encoder_prenet.pos_conv.0': 'speecht5.encoder.prenet.pos_conv_embed.conv',
    'speech_encoder_prenet.mask_emb': 'speecht5.encoder.prenet.masked_spec_embed',
}
__A	={
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
__A	={
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0': 'speecht5.decoder.prenet.layers.0',
    'speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0': 'speecht5.decoder.prenet.layers.1',
    'speech_decoder_prenet.decoder_prenet.0.1': 'speecht5.decoder.prenet.final_layer',
    'speech_decoder_prenet.decoder_prenet.1.alpha': 'speecht5.decoder.prenet.encode_positions.alpha',
    'speech_decoder_prenet.spkembs_layer.0': 'speecht5.decoder.prenet.speaker_embeds_layer',
}
__A	={
    'speech_decoder_postnet.feat_out': 'speech_decoder_postnet.feat_out',
    'speech_decoder_postnet.prob_out': 'speech_decoder_postnet.prob_out',
    'speech_decoder_postnet.postnet.postnet.0.0': 'speech_decoder_postnet.layers.0.conv',
    'speech_decoder_postnet.postnet.postnet.0.1': 'speech_decoder_postnet.layers.0.batch_norm',
    'speech_decoder_postnet.postnet.postnet.1.0': 'speech_decoder_postnet.layers.1.conv',
    'speech_decoder_postnet.postnet.postnet.1.1': 'speech_decoder_postnet.layers.1.batch_norm',
    'speech_decoder_postnet.postnet.postnet.2.0': 'speech_decoder_postnet.layers.2.conv',
    'speech_decoder_postnet.postnet.postnet.2.1': 'speech_decoder_postnet.layers.2.batch_norm',
    'speech_decoder_postnet.postnet.postnet.3.0': 'speech_decoder_postnet.layers.3.conv',
    'speech_decoder_postnet.postnet.postnet.3.1': 'speech_decoder_postnet.layers.3.batch_norm',
    'speech_decoder_postnet.postnet.postnet.4.0': 'speech_decoder_postnet.layers.4.conv',
    'speech_decoder_postnet.postnet.postnet.4.1': 'speech_decoder_postnet.layers.4.batch_norm',
}
__A	={
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
__A	={
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
__A	={
    'encoder.layers.*.self_attn.k_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj',
    'encoder.layers.*.self_attn.v_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj',
    'encoder.layers.*.self_attn.q_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj',
    'encoder.layers.*.self_attn.out_proj': 'speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj',
    'encoder.layers.*.self_attn_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.layer_norm',
    'encoder.layers.*.fc1': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense',
    'encoder.layers.*.fc2': 'speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense',
    'encoder.layers.*.final_layer_norm': 'speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'speecht5.encoder.wrapped_encoder.layer_norm',
    'encoder.pos_emb.pe_k': 'speecht5.encoder.wrapped_encoder.embed_positions.pe_k',
}
__A	={
    'decoder.layers.*.self_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj',
    'decoder.layers.*.self_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj',
    'decoder.layers.*.self_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj',
    'decoder.layers.*.self_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj',
    'decoder.layers.*.self_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm',
    'decoder.layers.*.encoder_attn.k_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj',
    'decoder.layers.*.encoder_attn.v_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj',
    'decoder.layers.*.encoder_attn.q_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj',
    'decoder.layers.*.encoder_attn.out_proj': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj',
    'decoder.layers.*.encoder_attn_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm',
    'decoder.layers.*.fc1': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense',
    'decoder.layers.*.fc2': 'speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense',
    'decoder.layers.*.final_layer_norm': 'speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm',
}
__A	={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__A	={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A	={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__A	=[]
__A	=[
    'encoder.version',
    'encoder.layers.*.norm_k.weight',
    'encoder.layers.*.norm_k.bias',
    'decoder.version',
    'decoder.layers.*.norm_k.weight',
    'decoder.layers.*.norm_k.bias',
    'decoder.pos_emb.pe_k',
    'speech_encoder_prenet.embed_positions._float_tensor',
    'text_decoder_prenet.embed_positions._float_tensor',
]
__A	=IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
__A	=IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
__A	=IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			for attribute in key.split("""."""					):
						UpperCAmelCase__  :					Union[str, Any]        =     getattr(UpperCamelCase__ ,      UpperCamelCase__					)

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

			if hf_shape != value.shape:
						raise ValueError(
						    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__  :					List[Any]        =     value
			elif weight_type == "weight_g":
						UpperCAmelCase__  :					int        =     value
			elif weight_type == "weight_v":
						UpperCAmelCase__  :					int        =     value
			elif weight_type == "bias":
						UpperCAmelCase__  :					Union[str, Any]        =     value
			elif weight_type == "running_mean":
						UpperCAmelCase__  :					List[Any]        =     value
			elif weight_type == "running_var":
						UpperCAmelCase__  :					List[str]        =     value
			elif weight_type == "num_batches_tracked":
						UpperCAmelCase__  :					Optional[Any]        =     value
			else:
						UpperCAmelCase__  :					Optional[Any]        =     value

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			for key in ignore_keys:
						if key.endswith(""".*"""					):
									if name.startswith(key[:-1]					):
												return True
						elif ".*." in key:
									UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     key.split(""".*."""					)
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Tuple        =     []

			if task == "s2t":
						UpperCAmelCase__  :					Union[str, Any]        =     hf_model.speechta.encoder.prenet.feature_encoder
						UpperCAmelCase__  :					str        =     MAPPING_S2T
						UpperCAmelCase__  :					Tuple        =     IGNORE_KEYS_S2T
			elif task == "t2s":
						UpperCAmelCase__  :					int        =     None
						UpperCAmelCase__  :					List[Any]        =     MAPPING_T2S
						UpperCAmelCase__  :					Dict        =     IGNORE_KEYS_T2S
			elif task == "s2s":
						UpperCAmelCase__  :					int        =     hf_model.speechta.encoder.prenet.feature_encoder
						UpperCAmelCase__  :					Optional[Any]        =     MAPPING_S2S
						UpperCAmelCase__  :					str        =     IGNORE_KEYS_S2S
			else:
						raise ValueError(f'''Unsupported task: {task}'''					)

			for name, value in fairseq_dict.items():
						if should_ignore(UpperCamelCase__ ,      UpperCamelCase__					):
									logger.info(f'''{name} was ignored'''					)
									continue

						UpperCAmelCase__  :					str        =     False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      hf_model.config.feat_extract_norm == """group""" ,      )
									UpperCAmelCase__  :					str        =     True
						else:
									for key, mapped_key in MAPPING.items():
												# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

												if "*" in key:
															UpperCAmelCase__   ,       UpperCAmelCase__  :					Dict        =     key.split(""".*."""					)
															if prefix in name and suffix in name:
																		UpperCAmelCase__  :					str        =     suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
												if key in name:
															UpperCAmelCase__  :					Optional[Any]        =     True
															if "*" in mapped_key:
																		UpperCAmelCase__  :					Union[str, Any]        =     name.split(UpperCamelCase__					)[0].split("""."""					)[-2]
																		UpperCAmelCase__  :					Any        =     mapped_key.replace("""*""" ,      UpperCamelCase__					)
															if "weight_g" in name:
																		UpperCAmelCase__  :					Dict        =     """weight_g"""
															elif "weight_v" in name:
																		UpperCAmelCase__  :					Optional[int]        =     """weight_v"""
															elif "bias" in name:
																		UpperCAmelCase__  :					Optional[int]        =     """bias"""
															elif "weight" in name:
																		UpperCAmelCase__  :					str        =     """weight"""
															elif "running_mean" in name:
																		UpperCAmelCase__  :					List[str]        =     """running_mean"""
															elif "running_var" in name:
																		UpperCAmelCase__  :					Any        =     """running_var"""
															elif "num_batches_tracked" in name:
																		UpperCAmelCase__  :					List[Any]        =     """num_batches_tracked"""
															else:
																		UpperCAmelCase__  :					List[str]        =     None
															set_recursively(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
												continue
						if not is_used:
									unused_weights.append(UpperCamelCase__					)

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     full_name.split("""conv_layers."""					)[-1]
			UpperCAmelCase__  :					List[Any]        =     name.split("""."""					)
			UpperCAmelCase__  :					Dict        =     int(items[0]					)
			UpperCAmelCase__  :					List[Any]        =     int(items[1]					)

			if type_id == 0:
						if "bias" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''					)
									UpperCAmelCase__  :					Optional[int]        =     value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''					)
									UpperCAmelCase__  :					Union[str, Any]        =     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:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''					)
									UpperCAmelCase__  :					Any        =     value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
												raise ValueError(
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''					)
									UpperCAmelCase__  :					Optional[int]        =     value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
			else:
						unused_weights.append(UpperCamelCase__					)







@torch.no_grad()
def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=None ,      UpperCamelCase__=None ,      UpperCamelCase__=None ,      ):
			if config_path is not None:
						UpperCAmelCase__  :					List[str]        =     SpeechTaConfig.from_pretrained(UpperCamelCase__					)
			else:
						UpperCAmelCase__  :					Any        =     SpeechTaConfig()

			if task == "s2t":
						UpperCAmelCase__  :					Optional[Any]        =     config.max_text_positions
						UpperCAmelCase__  :					Any        =     SpeechTaForSpeechToText(UpperCamelCase__					)
			elif task == "t2s":
						UpperCAmelCase__  :					Dict        =     1_8_7_6
						UpperCAmelCase__  :					List[str]        =     6_0_0
						UpperCAmelCase__  :					Any        =     config.max_speech_positions
						UpperCAmelCase__  :					int        =     SpeechTaForTextToSpeech(UpperCamelCase__					)
			elif task == "s2s":
						UpperCAmelCase__  :					Dict        =     1_8_7_6
						UpperCAmelCase__  :					Optional[Any]        =     config.max_speech_positions
						UpperCAmelCase__  :					List[Any]        =     SpeechTaForSpeechToSpeech(UpperCamelCase__					)
			else:
						raise ValueError(f'''Unknown task name: {task}'''					)

			if vocab_path:
						UpperCAmelCase__  :					List[str]        =     SpeechTaTokenizer(UpperCamelCase__ ,      model_max_length=config.max_text_positions					)

						# Mask token behaves like a normal word, i.e. include the space before it
						UpperCAmelCase__  :					Tuple        =     AddedToken("""<mask>""" ,      lstrip=UpperCamelCase__ ,      rstrip=UpperCamelCase__					)
						UpperCAmelCase__  :					Any        =     mask_token
						tokenizer.add_special_tokens({"""mask_token""": mask_token}					)
						tokenizer.add_tokens(["""<ctc_blank>"""]					)

			UpperCAmelCase__  :					Dict        =     SpeechTaFeatureExtractor()
			UpperCAmelCase__  :					int        =     SpeechTaProcessor(tokenizer=UpperCamelCase__ ,      feature_extractor=UpperCamelCase__					)
			processor.save_pretrained(UpperCamelCase__					)

			UpperCAmelCase__  :					str        =     torch.load(UpperCamelCase__					)
			recursively_load_weights(fairseq_checkpoint["""model"""] ,      UpperCamelCase__ ,      UpperCamelCase__					)

			model.save_pretrained(UpperCamelCase__					)

			if repo_id:
						print("""Pushing to the hub..."""					)
						processor.push_to_hub(UpperCamelCase__					)
						model.push_to_hub(UpperCamelCase__					)


if __name__ == "__main__":
		__A	=argparse.ArgumentParser()
		parser.add_argument(
		    '--task',
		    default='s2t',
		    type=str,
		    help='Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.',
		)
		parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--vocab_path', default=None, type=str, help='Path to SentencePiece model')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
		)

		__A	=parser.parse_args()
		convert_speechta_checkpoint(
		    args.task,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.vocab_path,
		    args.push_to_hub,
		)



'''simple docstring'''



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     DPTConfig(embedding_type="""hybrid"""					)

			if "large" in checkpoint_url:
						UpperCAmelCase__  :					List[str]        =     1_0_2_4
						UpperCAmelCase__  :					List[str]        =     4_0_9_6
						UpperCAmelCase__  :					Optional[int]        =     2_4
						UpperCAmelCase__  :					str        =     1_6
						UpperCAmelCase__  :					Optional[Any]        =     [5, 1_1, 1_7, 2_3]
						UpperCAmelCase__  :					Any        =     [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						UpperCAmelCase__  :					Union[str, Any]        =     (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						UpperCAmelCase__  :					List[str]        =     7_6_8
						UpperCAmelCase__  :					Union[str, Any]        =     [1, 1, 1, 0.5]
						UpperCAmelCase__  :					Union[str, Any]        =     [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						UpperCAmelCase__  :					int        =     1_5_0
						UpperCAmelCase__  :					Any        =     1_6
						UpperCAmelCase__  :					Optional[Any]        =     (1, 3_8_4, 3_8_4)
						UpperCAmelCase__  :					Optional[int]        =     False
						UpperCAmelCase__  :					int        =     """project"""

			if "ade" in checkpoint_url:
						UpperCAmelCase__  :					Any        =     True
						UpperCAmelCase__  :					str        =     7_6_8
						UpperCAmelCase__  :					Optional[int]        =     [1, 1, 1, 0.5]
						UpperCAmelCase__  :					str        =     1_5_0
						UpperCAmelCase__  :					str        =     1_6
						UpperCAmelCase__  :					List[Any]        =     """huggingface/label-files"""
						UpperCAmelCase__  :					Optional[int]        =     """ade20k-id2label.json"""
						UpperCAmelCase__  :					Optional[int]        =     json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,      UpperCamelCase__ ,      repo_type="""dataset"""					)					) ,      """r"""					)					)
						UpperCAmelCase__  :					List[str]        =     {int(UpperCamelCase__					): v for k, v in idalabel.items()}
						UpperCAmelCase__  :					List[str]        =     idalabel
						UpperCAmelCase__  :					List[Any]        =     {v: k for k, v in idalabel.items()}
						UpperCAmelCase__  :					Optional[Any]        =     [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[int]        =     ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						UpperCAmelCase__  :					int        =     name.replace("""pretrained.model""" ,      """dpt.encoder"""					)
			if "pretrained.model" in name:
						UpperCAmelCase__  :					int        =     name.replace("""pretrained.model""" ,      """dpt.embeddings"""					)
			if "patch_embed" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""patch_embed""" ,      """"""					)
			if "pos_embed" in name:
						UpperCAmelCase__  :					Optional[int]        =     name.replace("""pos_embed""" ,      """position_embeddings"""					)
			if "attn.proj" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""attn.proj""" ,      """attention.output.dense"""					)
			if "proj" in name and "project" not in name:
						UpperCAmelCase__  :					str        =     name.replace("""proj""" ,      """projection"""					)
			if "blocks" in name:
						UpperCAmelCase__  :					str        =     name.replace("""blocks""" ,      """layer"""					)
			if "mlp.fc1" in name:
						UpperCAmelCase__  :					Tuple        =     name.replace("""mlp.fc1""" ,      """intermediate.dense"""					)
			if "mlp.fc2" in name:
						UpperCAmelCase__  :					str        =     name.replace("""mlp.fc2""" ,      """output.dense"""					)
			if "norm1" in name and "backbone" not in name:
						UpperCAmelCase__  :					Optional[Any]        =     name.replace("""norm1""" ,      """layernorm_before"""					)
			if "norm2" in name and "backbone" not in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""norm2""" ,      """layernorm_after"""					)
			if "scratch.output_conv" in name:
						UpperCAmelCase__  :					int        =     name.replace("""scratch.output_conv""" ,      """head"""					)
			if "scratch" in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""scratch""" ,      """neck"""					)
			if "layer1_rn" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""layer1_rn""" ,      """convs.0"""					)
			if "layer2_rn" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""layer2_rn""" ,      """convs.1"""					)
			if "layer3_rn" in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""layer3_rn""" ,      """convs.2"""					)
			if "layer4_rn" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""layer4_rn""" ,      """convs.3"""					)
			if "refinenet" in name:
						UpperCAmelCase__  :					List[Any]        =     int(name[len("""neck.refinenet"""					) : len("""neck.refinenet"""					) + 1]					)
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace(f'''refinenet{layer_idx}''' ,      f'''fusion_stage.layers.{abs(layer_idx-4					)}'''					)
			if "out_conv" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""out_conv""" ,      """projection"""					)
			if "resConfUnit1" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""resConfUnit1""" ,      """residual_layer1"""					)
			if "resConfUnit2" in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""resConfUnit2""" ,      """residual_layer2"""					)
			if "conv1" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""conv1""" ,      """convolution1"""					)
			if "conv2" in name:
						UpperCAmelCase__  :					Tuple        =     name.replace("""conv2""" ,      """convolution2"""					)
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						UpperCAmelCase__  :					int        =     name.replace("""pretrained.act_postprocess1.0.project.0""" ,      """neck.reassemble_stage.readout_projects.0.0"""					)
			if "pretrained.act_postprocess2.0.project.0" in name:
						UpperCAmelCase__  :					Optional[Any]        =     name.replace("""pretrained.act_postprocess2.0.project.0""" ,      """neck.reassemble_stage.readout_projects.1.0"""					)
			if "pretrained.act_postprocess3.0.project.0" in name:
						UpperCAmelCase__  :					Tuple        =     name.replace("""pretrained.act_postprocess3.0.project.0""" ,      """neck.reassemble_stage.readout_projects.2.0"""					)
			if "pretrained.act_postprocess4.0.project.0" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""pretrained.act_postprocess4.0.project.0""" ,      """neck.reassemble_stage.readout_projects.3.0"""					)

			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""pretrained.act_postprocess1.3""" ,      """neck.reassemble_stage.layers.0.projection"""					)
			if "pretrained.act_postprocess1.4" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""pretrained.act_postprocess1.4""" ,      """neck.reassemble_stage.layers.0.resize"""					)
			if "pretrained.act_postprocess2.3" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""pretrained.act_postprocess2.3""" ,      """neck.reassemble_stage.layers.1.projection"""					)
			if "pretrained.act_postprocess2.4" in name:
						UpperCAmelCase__  :					Optional[int]        =     name.replace("""pretrained.act_postprocess2.4""" ,      """neck.reassemble_stage.layers.1.resize"""					)
			if "pretrained.act_postprocess3.3" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""pretrained.act_postprocess3.3""" ,      """neck.reassemble_stage.layers.2.projection"""					)
			if "pretrained.act_postprocess4.3" in name:
						UpperCAmelCase__  :					str        =     name.replace("""pretrained.act_postprocess4.3""" ,      """neck.reassemble_stage.layers.3.projection"""					)
			if "pretrained.act_postprocess4.4" in name:
						UpperCAmelCase__  :					Optional[int]        =     name.replace("""pretrained.act_postprocess4.4""" ,      """neck.reassemble_stage.layers.3.resize"""					)
			if "pretrained" in name:
						UpperCAmelCase__  :					Optional[int]        =     name.replace("""pretrained""" ,      """dpt"""					)
			if "bn" in name:
						UpperCAmelCase__  :					Optional[Any]        =     name.replace("""bn""" ,      """batch_norm"""					)
			if "head" in name:
						UpperCAmelCase__  :					str        =     name.replace("""head""" ,      """head.head"""					)
			if "encoder.norm" in name:
						UpperCAmelCase__  :					Tuple        =     name.replace("""encoder.norm""" ,      """layernorm"""					)
			if "auxlayer" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""auxlayer""" ,      """auxiliary_head.head"""					)
			if "backbone" in name:
						UpperCAmelCase__  :					Any        =     name.replace("""backbone""" ,      """backbone.bit.encoder"""					)

			if ".." in name:
						UpperCAmelCase__  :					Dict        =     name.replace("""..""" ,      """."""					)

			if "stem.conv" in name:
						UpperCAmelCase__  :					int        =     name.replace("""stem.conv""" ,      """bit.embedder.convolution"""					)
			if "blocks" in name:
						UpperCAmelCase__  :					Union[str, Any]        =     name.replace("""blocks""" ,      """layers"""					)
			if "convolution" in name and "backbone" in name:
						UpperCAmelCase__  :					Optional[int]        =     name.replace("""convolution""" ,      """conv"""					)
			if "layer" in name and "backbone" in name:
						UpperCAmelCase__  :					int        =     name.replace("""layer""" ,      """layers"""					)
			if "backbone.bit.encoder.bit" in name:
						UpperCAmelCase__  :					Optional[Any]        =     name.replace("""backbone.bit.encoder.bit""" ,      """backbone.bit"""					)
			if "embedder.conv" in name:
						UpperCAmelCase__  :					List[Any]        =     name.replace("""embedder.conv""" ,      """embedder.convolution"""					)
			if "backbone.bit.encoder.stem.norm" in name:
						UpperCAmelCase__  :					Optional[Any]        =     name.replace("""backbone.bit.encoder.stem.norm""" ,      """backbone.bit.embedder.norm"""					)
			return name







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			for i in range(config.num_hidden_layers					):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						UpperCAmelCase__  :					Dict        =     state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''					)
						UpperCAmelCase__  :					Tuple        =     state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''					)
						# next, add query, keys and values (in that order) to the state dict
						UpperCAmelCase__  :					Any        =     in_proj_weight[: config.hidden_size, :]
						UpperCAmelCase__  :					Optional[Any]        =     in_proj_bias[: config.hidden_size]
						UpperCAmelCase__  :					Optional[int]        =     in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						UpperCAmelCase__  :					List[str]        =     in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						UpperCAmelCase__  :					Optional[int]        =     in_proj_weight[
						    -config.hidden_size :, :
						]
						UpperCAmelCase__  :					Optional[Any]        =     in_proj_bias[-config.hidden_size :]







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					str        =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase__  :					int        =     Image.open(requests.get(UpperCamelCase__ ,      stream=UpperCamelCase__					).raw					)
			return im







@torch.no_grad()
def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__   ,       UpperCAmelCase__  :					List[str]        =     get_dpt_config(UpperCamelCase__					)
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			UpperCAmelCase__  :					Dict        =     torch.load(UpperCamelCase__ ,      map_location="""cpu"""					)
			# remove certain keys
			remove_ignore_keys_(UpperCamelCase__					)
			# rename keys
			for key in state_dict.copy().keys():
						UpperCAmelCase__  :					Optional[int]        =     state_dict.pop(UpperCamelCase__					)
						UpperCAmelCase__  :					List[str]        =     val
			# read in qkv matrices
			read_in_q_k_v(UpperCamelCase__ ,      UpperCamelCase__					)

			# load HuggingFace model
			UpperCAmelCase__  :					Tuple        =     DPTForSemanticSegmentation(UpperCamelCase__					) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__					)
			model.load_state_dict(UpperCamelCase__					)
			model.eval()

			# Check outputs on an image
			UpperCAmelCase__  :					Tuple        =     4_8_0 if """ade""" in checkpoint_url else 3_8_4
			UpperCAmelCase__  :					Tuple        =     DPTImageProcessor(size=UpperCamelCase__					)

			UpperCAmelCase__  :					Union[str, Any]        =     prepare_img()
			UpperCAmelCase__  :					Any        =     image_processor(UpperCamelCase__ ,      return_tensors="""pt"""					)

			# forward pass
			UpperCAmelCase__  :					Dict        =     model(**UpperCamelCase__					).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__					).predicted_depth

			if show_prediction:
						UpperCAmelCase__  :					str        =     (
						    torch.nn.functional.interpolate(
						        outputs.unsqueeze(1					) ,      size=(image.size[1], image.size[0]) ,      mode="""bicubic""" ,      align_corners=UpperCamelCase__ ,      )
						    .squeeze()
						    .cpu()
						    .numpy()
						)

						Image.fromarray((prediction / prediction.max()) * 2_5_5					).show()

			if pytorch_dump_folder_path is not None:
						Path(UpperCamelCase__					).mkdir(exist_ok=UpperCamelCase__					)
						print(f'''Saving model to {pytorch_dump_folder_path}'''					)
						model.save_pretrained(UpperCamelCase__					)
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
						image_processor.save_pretrained(UpperCamelCase__					)

			if push_to_hub:
						model.push_to_hub("""ybelkada/dpt-hybrid-midas"""					)
						image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""					)


if __name__ == "__main__":
		__A	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
		    type=str,
		    help='URL of the original DPT checkpoint you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    type=str,
		    required=False,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		)
		parser.add_argument(
		    '--model_name',
		    default='dpt-large',
		    type=str,
		    help='Name of the model, in case you\'re pushing to the hub.',
		)
		parser.add_argument(
		    '--show_prediction',
		    action='store_true',
		)

		__A	=parser.parse_args()
		convert_dpt_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
		)