



'''simple docstring'''

import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


snake_case_			=     """https://openaipublic.azureedge.net/jukebox/models/"""
snake_case_			=     {
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				if key.endswith('''.model.1.bias'''       ) and len(key.split('''.'''       )       ) > 10:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= key.replace('''.model.1.bias'''    ,	'''.conv1d_1.bias'''       )
				elif key.endswith('''.model.1.weight'''       ) and len(key.split('''.'''       )       ) > 10:
								SCREAMING_SNAKE_CASE  :						List[str]      					= key.replace('''.model.1.weight'''    ,	'''.conv1d_1.weight'''       )
				elif key.endswith('''.model.3.bias'''       ) and len(key.split('''.'''       )       ) > 10:
								SCREAMING_SNAKE_CASE  :						List[str]      					= key.replace('''.model.3.bias'''    ,	'''.conv1d_2.bias'''       )
				elif key.endswith('''.model.3.weight'''       ) and len(key.split('''.'''       )       ) > 10:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= key.replace('''.model.3.weight'''    ,	'''.conv1d_2.weight'''       )

				if "conditioner_blocks.0." in key:
								SCREAMING_SNAKE_CASE  :						Any      					= key.replace('''conditioner_blocks.0'''    ,	'''conditioner_blocks'''       )

				if "prime_prior" in key:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= key.replace('''prime_prior'''    ,	'''encoder'''       )

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								SCREAMING_SNAKE_CASE  :						Tuple      					= key.replace('''.emb.'''    ,	'''.'''       )

				if key.endswith('''k'''       ):  # replace vqvae.X.k with vqvae.X.codebook
								return key.replace('''.k'''    ,	'''.codebook'''       )
				if "y_emb." in key:
								return key.replace('''y_emb.'''    ,	'''metadata_embedding.'''       )

				if "x_emb.emb." in key:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= key.replace('''0.x_emb.emb'''    ,	'''embed_tokens'''       )

				if "prime_state_ln" in key:
								return key.replace('''prime_state_ln'''    ,	'''encoder.final_layer_norm'''       )
				if ".ln" in key:
								return key.replace('''.ln'''    ,	'''.layer_norm'''       )
				if "_ln" in key:
								return key.replace('''_ln'''    ,	'''_layer_norm'''       )

				if "prime_state_proj" in key:
								return key.replace('''prime_state_proj'''    ,	'''encoder.proj_in'''       )
				if "prime_x_out" in key:
								return key.replace('''prime_x_out'''    ,	'''encoder.lm_head'''       )
				if "prior.x_out" in key:
								return key.replace('''x_out'''    ,	'''fc_proj_out'''       )
				if "x_emb" in key:
								return key.replace('''x_emb'''    ,	'''embed_tokens'''       )

				return key






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
				import re

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
				SCREAMING_SNAKE_CASE  :						int      					= re.compile(
				    R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

				SCREAMING_SNAKE_CASE  :						str      					= re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''       )
				SCREAMING_SNAKE_CASE  :						Dict      					= re.compile(
				    R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''       )

				SCREAMING_SNAKE_CASE  :						str      					= re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re.compile(
				    R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''       )
				SCREAMING_SNAKE_CASE  :						int      					= re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'''       )

				for original_key, value in state_dict.items():
								# rename vqvae.encoder keys
								if re_encoder_block_conv_in.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						int      					= re_encoder_block_conv_in.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= int(groups[2]       ) * 2 + int(groups[3]       )
												SCREAMING_SNAKE_CASE  :						int      					= F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re_encoder_block_conv_in.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								elif re_encoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						int      					= re_encoder_block_resnet.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						List[str]      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						int      					= int(groups[2]       ) * 2 + int(groups[3]       )
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {'''1''': 1, '''3''': 2}[groups[-2]]
												SCREAMING_SNAKE_CASE  :						List[Any]      					= F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
												SCREAMING_SNAKE_CASE  :						Dict      					= F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= prefix + resnet_block
												SCREAMING_SNAKE_CASE  :						Dict      					= re_encoder_block_resnet.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								elif re_encoder_block_proj_out.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re_encoder_block_proj_out.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						List[str]      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						Tuple      					= F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						Dict      					= re_encoder_block_proj_out.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Dict      					= re_decoder_block_conv_out.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						List[str]      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						Dict      					= int(groups[2]       ) * 2 + int(groups[3]       ) - 2
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						Any      					= re_decoder_block_conv_out.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								elif re_decoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						List[Any]      					= re_decoder_block_resnet.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= int(groups[2]       ) * 2 + int(groups[3]       ) - 2
												SCREAMING_SNAKE_CASE  :						List[str]      					= {'''1''': 1, '''3''': 2}[groups[-2]]
												SCREAMING_SNAKE_CASE  :						Dict      					= F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						str      					= prefix + resnet_block
												SCREAMING_SNAKE_CASE  :						List[Any]      					= re_decoder_block_resnet.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						Tuple      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						str      					= F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						List[str]      					= re_decoder_block_proj_in.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								# rename prior cond.model to upsampler.upsample_block and resnet
								elif re_prior_cond_conv_out.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Dict      					= re_prior_cond_conv_out.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						int      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						str      					= int(groups[1]       ) * 2 + int(groups[2]       ) - 2
												SCREAMING_SNAKE_CASE  :						int      					= F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re_prior_cond_conv_out.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								elif re_prior_cond_resnet.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= re_prior_cond_resnet.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						Any      					= int(groups[1]       ) * 2 + int(groups[2]       ) - 2
												SCREAMING_SNAKE_CASE  :						Tuple      					= {'''1''': 1, '''3''': 2}[groups[-2]]
												SCREAMING_SNAKE_CASE  :						Dict      					= F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
												SCREAMING_SNAKE_CASE  :						int      					= F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						int      					= prefix + resnet_block
												SCREAMING_SNAKE_CASE  :						List[str]      					= re_prior_cond_resnet.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE       ):
												SCREAMING_SNAKE_CASE  :						List[Any]      					= re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= regex_match.groups()
												SCREAMING_SNAKE_CASE  :						Tuple      					= F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
												SCREAMING_SNAKE_CASE  :						Tuple      					= re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

								# keep original key
								else:
												SCREAMING_SNAKE_CASE  :						str      					= original_key

								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= replace_key(_SCREAMING_SNAKE_CASE       )

								if F'''{key_prefix}.{key}''' not in model_state_dict or key is None:
												print(F'''failed converting {original_key} to {key}, does not match'''       )

								# handle missmatched shape
								elif value.shape != model_state_dict[F'''{key_prefix}.{key}'''].shape:
												SCREAMING_SNAKE_CASE  :						Dict      					= model_state_dict[F'''{key_prefix}.{key}''']
												print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''       )
												SCREAMING_SNAKE_CASE  :						str      					= original_key

								SCREAMING_SNAKE_CASE  :						List[Any]      					= original_key
								SCREAMING_SNAKE_CASE  :						List[str]      					= value

				return new_dict






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]=None    ,	_SCREAMING_SNAKE_CASE      :str=None       ):
				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/"       )[-1]}'''       ):
												SCREAMING_SNAKE_CASE  :						Any      					= requests.get(F'''{PREFIX}{file}'''    ,	allow_redirects=_SCREAMING_SNAKE_CASE       )
												os.makedirs(F'''{pytorch_dump_folder_path}/'''    ,	exist_ok=_SCREAMING_SNAKE_CASE       )
												open(F'''{pytorch_dump_folder_path}/{file.split("/"       )[-1]}'''    ,	'''wb'''       ).write(r.content       )

				SCREAMING_SNAKE_CASE  :						Dict      					= MODEL_MAPPING[model_name.split('''/'''       )[-1]]

				SCREAMING_SNAKE_CASE  :						int      					= JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= JukeboxModel(_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Tuple      					= []
				SCREAMING_SNAKE_CASE  :						Dict      					= {}
				for i, dict_name in enumerate(_SCREAMING_SNAKE_CASE       ):
								SCREAMING_SNAKE_CASE  :						str      					= torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split("/"       )[-1]}'''       )['''model''']

								SCREAMING_SNAKE_CASE  :						Tuple      					= {}
								for k in old_dic.keys():
												if k.endswith('''.b'''       ):
																SCREAMING_SNAKE_CASE  :						Tuple      					= old_dic[k]
												elif k.endswith('''.w'''       ):
																SCREAMING_SNAKE_CASE  :						List[Any]      					= old_dic[k]
												elif "level_2" not in dict_name and "cond.model." in k:
																SCREAMING_SNAKE_CASE  :						List[Any]      					= old_dic[k]
												else:
																SCREAMING_SNAKE_CASE  :						Tuple      					= old_dic[k]

								SCREAMING_SNAKE_CASE  :						str      					= '''vqvae''' if i == 0 else F'''priors.{3 - i}'''
								SCREAMING_SNAKE_CASE  :						Tuple      					= fix_jukebox_keys(_SCREAMING_SNAKE_CASE    ,	model.state_dict()    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								weight_dict.append(_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						List[str]      					= weight_dict.pop(0       )
				model.vqvae.load_state_dict(_SCREAMING_SNAKE_CASE       )
				for i in range(len(_SCREAMING_SNAKE_CASE       )       ):
								model.priors[i].load_state_dict(weight_dict[2 - i]       )

				Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
				with open(F'''{pytorch_dump_folder_path}/mapping.json'''    ,	'''w'''       ) as txtfile:
								json.dump(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
				model.save_pretrained(_SCREAMING_SNAKE_CASE       )

				return weight_dict


if __name__ == "__main__":
	snake_case_			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""jukebox-5b-lyrics""",
	    type=str,
	    help="""Name of the model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default="""jukebox-5b-lyrics-converted""",
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	snake_case_			=     parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= []
				for i in range(encoder_config.num_hidden_layers       ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (F'''encoder.deit.blocks.{i}.norm1.weight''', F'''encoder.encoder.layer.{i}.layernorm_before.weight''')       )
								rename_keys.append((F'''encoder.deit.blocks.{i}.norm1.bias''', F'''encoder.encoder.layer.{i}.layernorm_before.bias''')       )
								rename_keys.append(
								    (F'''encoder.deit.blocks.{i}.attn.proj.weight''', F'''encoder.encoder.layer.{i}.attention.output.dense.weight''')       )
								rename_keys.append(
								    (F'''encoder.deit.blocks.{i}.attn.proj.bias''', F'''encoder.encoder.layer.{i}.attention.output.dense.bias''')       )
								rename_keys.append(
								    (F'''encoder.deit.blocks.{i}.norm2.weight''', F'''encoder.encoder.layer.{i}.layernorm_after.weight''')       )
								rename_keys.append((F'''encoder.deit.blocks.{i}.norm2.bias''', F'''encoder.encoder.layer.{i}.layernorm_after.bias''')       )
								rename_keys.append(
								    (F'''encoder.deit.blocks.{i}.mlp.fc1.weight''', F'''encoder.encoder.layer.{i}.intermediate.dense.weight''')       )
								rename_keys.append(
								    (F'''encoder.deit.blocks.{i}.mlp.fc1.bias''', F'''encoder.encoder.layer.{i}.intermediate.dense.bias''')       )
								rename_keys.append(
								    (F'''encoder.deit.blocks.{i}.mlp.fc2.weight''', F'''encoder.encoder.layer.{i}.output.dense.weight''')       )
								rename_keys.append((F'''encoder.deit.blocks.{i}.mlp.fc2.bias''', F'''encoder.encoder.layer.{i}.output.dense.bias''')       )

				# cls token, position embeddings and patch embeddings of encoder
				rename_keys.extend(
				    [
				        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
				        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
				        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
				        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
				        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
				        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
				    ]       )

				return rename_keys






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				for i in range(encoder_config.num_hidden_layers       ):
								# queries, keys and values (only weights, no biases)
								SCREAMING_SNAKE_CASE  :						str      					= state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''       )

								SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight[
								    : encoder_config.hidden_size, :
								]
								SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_weight[
								    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
								]
								SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_weight[
								    -encoder_config.hidden_size :, :
								]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= dct.pop(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				if "handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Any      					= '''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
								# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
								# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
								# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
								# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
				elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
				SCREAMING_SNAKE_CASE  :						List[Any]      					= Image.open(requests.get(_SCREAMING_SNAKE_CASE    ,	stream=_SCREAMING_SNAKE_CASE       ).raw       ).convert('''RGB'''       )
				return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ViTConfig(image_size=3_84    ,	qkv_bias=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= 7_68
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 10_24
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 40_96
								SCREAMING_SNAKE_CASE  :						List[str]      					= 24
								SCREAMING_SNAKE_CASE  :						Any      					= 16
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 10_24
				else:
								raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''       )

				# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
				if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= False
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''relu'''
								SCREAMING_SNAKE_CASE  :						Tuple      					= 10_24
								SCREAMING_SNAKE_CASE  :						int      					= True
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
								SCREAMING_SNAKE_CASE  :						Any      					= False

				# load HuggingFace model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= ViTModel(_SCREAMING_SNAKE_CASE    ,	add_pooling_layer=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= TrOCRForCausalLM(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= VisionEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE    ,	decoder=_SCREAMING_SNAKE_CASE       )
				model.eval()

				# load state_dict of original model, rename some keys
				SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE    ,	map_location='''cpu'''    ,	check_hash=_SCREAMING_SNAKE_CASE       )['''model''']

				SCREAMING_SNAKE_CASE  :						int      					= create_rename_keys(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				for src, dest in rename_keys:
								rename_key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				read_in_q_k_v(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# remove parameters we don't need
				del state_dict["encoder.deit.head.weight"]
				del state_dict["encoder.deit.head.bias"]
				del state_dict["decoder.version"]

				# add prefix to decoder keys
				for key, val in state_dict.copy().items():
								SCREAMING_SNAKE_CASE  :						List[Any]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
								if key.startswith('''decoder'''       ) and "output_projection" not in key:
												SCREAMING_SNAKE_CASE  :						Tuple      					= val
								else:
												SCREAMING_SNAKE_CASE  :						int      					= val

    # load state dict
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

				# Check outputs on an image
				SCREAMING_SNAKE_CASE  :						List[Any]      					= ViTImageProcessor(size=encoder_config.image_size       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= RobertaTokenizer.from_pretrained('''roberta-large'''       )
				SCREAMING_SNAKE_CASE  :						Dict      					= TrOCRProcessor(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Tuple      					= processor(images=prepare_img(_SCREAMING_SNAKE_CASE       )    ,	return_tensors='''pt'''       ).pixel_values

				# verify logits
				SCREAMING_SNAKE_CASE  :						str      					= torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
				SCREAMING_SNAKE_CASE  :						Dict      					= model(pixel_values=_SCREAMING_SNAKE_CASE    ,	decoder_input_ids=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= outputs.logits

				SCREAMING_SNAKE_CASE  :						Any      					= torch.Size([1, 1, 5_02_65]       )
				if "trocr-base-handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.tensor(
								    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]       )
				elif "trocr-large-handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor(
								    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]       )
				elif "trocr-base-printed" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						str      					= torch.tensor(
								    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]       )
				elif "trocr-large-printed" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.tensor(
								    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]       )

				if "stage1" not in checkpoint_url:
								assert logits.shape == expected_shape, "Shape of logits not as expected"
								assert torch.allclose(logits[0, 0, :10]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1E-3       ), "First elements of logits not as expected"

				Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
				print(F'''Saving model to {pytorch_dump_folder_path}'''       )
				model.save_pretrained(_SCREAMING_SNAKE_CASE       )
				print(F'''Saving processor to {pytorch_dump_folder_path}'''       )
				processor.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	snake_case_			=     argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
	    type=str,
	    help="""URL to the original PyTorch checkpoint (.pth file).""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	snake_case_			=     parser.parse_args()
	convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




