




'''simple docstring'''





import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)




def     __magic_name__       (				A	:      Tuple     ):






			'''simple docstring'''







			a					=    model.config

			a					=    DonutSwinConfig(
			    image_size=original_config.input_size,						patch_size=4,						depths=original_config.encoder_layer,						num_heads=[4, 8, 16, 32],						window_size=original_config.window_size,						embed_dim=128,						)
			a					=    MBartConfig(
			    is_decoder=lowercase_,						is_encoder_decoder=lowercase_,						add_cross_attention=lowercase_,						decoder_layers=original_config.decoder_layer,						max_position_embeddings=original_config.max_position_embeddings,						vocab_size=len(
			        model.decoder.tokenizer     ),						scale_embedding=lowercase_,						add_final_layer_norm=lowercase_,						)

			return encoder_config, decoder_config




def     __magic_name__       (				A	:      Optional[Any]     ):






			'''simple docstring'''







			if "encoder.model" in name:
						a					=    name.replace("encoder.model",						"encoder"     )
			if "decoder.model" in name:
						a					=    name.replace("decoder.model",						"decoder"     )
			if "patch_embed.proj" in name:
						a					=    name.replace("patch_embed.proj",						"embeddings.patch_embeddings.projection"     )
			if "patch_embed.norm" in name:
						a					=    name.replace("patch_embed.norm",						"embeddings.norm"     )
			if name.startswith("encoder"     ):
						if "layers" in name:
									a					=    "encoder." + name
						if "attn.proj" in name:
									a					=    name.replace("attn.proj",						"attention.output.dense"     )
						if "attn" in name and "mask" not in name:
									a					=    name.replace("attn",						"attention.self"     )
						if "norm1" in name:
									a					=    name.replace("norm1",						"layernorm_before"     )
						if "norm2" in name:
									a					=    name.replace("norm2",						"layernorm_after"     )
						if "mlp.fc1" in name:
									a					=    name.replace("mlp.fc1",						"intermediate.dense"     )
						if "mlp.fc2" in name:
									a					=    name.replace("mlp.fc2",						"output.dense"     )

						if name == "encoder.norm.weight":
									a					=    "encoder.layernorm.weight"
						if name == "encoder.norm.bias":
									a					=    "encoder.layernorm.bias"

			return name




def     __magic_name__       (				A	:      str,						A	:      Tuple     ):






			'''simple docstring'''







			for key in orig_state_dict.copy().keys():
						a					=    orig_state_dict.pop(lowercase_     )

						if "qkv" in key:
									a					=    key.split("."     )
									a					=    int(key_split[3]     )
									a					=    int(key_split[5]     )
									a					=    model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

									if "weight" in key:
												a					=    val[:dim, :]
												a					=    val[dim : dim * 2, :]
												a					=    val[-dim:, :]
									else:
												a					=    val[:dim]
												a					=    val[dim : dim * 2]
												a					=    val[-dim:]
						elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
									# HuggingFace implementation doesn't use attn_mask buffer
									# and model doesn't use final LayerNorms for the encoder
									pass
						else:
									a					=    val

			return orig_state_dict




def     __magic_name__       (				A	:      List[Any],						A	:      Union[str, Any]=None,						A	:      Tuple=False     ):






			'''simple docstring'''







			a					=    DonutModel.from_pretrained(lowercase_     ).eval()

			# load HuggingFace model
			a					=    get_configs(lowercase_     )
			a					=    DonutSwinModel(lowercase_     )
			a					=    MBartForCausalLM(lowercase_     )
			a					=    VisionEncoderDecoderModel(encoder=lowercase_,						decoder=lowercase_     )
			model.eval()

			a					=    original_model.state_dict()
			a					=    convert_state_dict(lowercase_,						lowercase_     )
			model.load_state_dict(lowercase_     )

			# verify results on scanned document
			a					=    load_dataset("hf-internal-testing/example-documents"     )
			a					=    dataset["test"][0]["image"].convert("RGB"     )

			a					=    XLMRobertaTokenizerFast.from_pretrained(lowercase_,						from_slow=lowercase_     )
			a					=    DonutImageProcessor(
			    do_align_long_axis=original_model.config.align_long_axis,						size=original_model.config.input_size[::-1]     )
			a					=    DonutProcessor(lowercase_,						lowercase_     )
			a					=    processor(lowercase_,						return_tensors="pt"     ).pixel_values

			if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
						a					=    "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
						a					=    "When is the coffee break?"
						a					=    task_prompt.replace("{user_input}",						lowercase_     )
			elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
						a					=    "<s_rvlcdip>"
			elif model_name in [
			    "naver-clova-ix/donut-base-finetuned-cord-v1",
			    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
			]:
						a					=    "<s_cord>"
			elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
						a					=    "s_cord-v2>"
			elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
						a					=    "<s_zhtrainticket>"
			elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
						# use a random prompt
						a					=    "hello world"
			else:
						raise ValueError("Model name not supported"     )
			a					=    original_model.decoder.tokenizer(lowercase_,						add_special_tokens=lowercase_,						return_tensors="pt"     )[
			    "input_ids"
			]

			a					=    original_model.encoder.model.patch_embed(lowercase_     )
			a					=    model.encoder.embeddings(lowercase_     )
			assert torch.allclose(lowercase_,						lowercase_,						atol=1E-3     )

			# verify encoder hidden states
			a					=    original_model.encoder(lowercase_     )
			a					=    model.encoder(lowercase_     ).last_hidden_state
			assert torch.allclose(lowercase_,						lowercase_,						atol=1E-2     )

			# verify decoder hidden states
			a					=    original_model(lowercase_,						lowercase_,						lowercase_     ).logits
			a					=    model(lowercase_,						decoder_input_ids=lowercase_     ).logits
			assert torch.allclose(lowercase_,						lowercase_,						atol=1E-3     )
			print("Looks ok!"     )

			if pytorch_dump_folder_path is not None:
						print(F"""Saving model and processor to {pytorch_dump_folder_path}"""     )
						model.save_pretrained(lowercase_     )
						processor.save_pretrained(lowercase_     )

			if push_to_hub:
						model.push_to_hub("nielsr/" + model_name.split("/"     )[-1],						commit_message="Update model"     )
						processor.push_to_hub("nielsr/" + model_name.split("/"     )[-1],						commit_message="Update model"     )


if __name__ == "__main__":
		__lowerCAmelCase	:       List[Any]     =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='naver-clova-ix/donut-base-finetuned-docvqa',
		    required=False,
		    type=str,
		    help='Name of the original model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    required=False,
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		    help='Whether or not to push the converted model and processor to the 🤗 hub.',
		)

		__lowerCAmelCase	:       Any     =	parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












import argparse
import os
import re


__lowerCAmelCase	:       Union[str, Any]     =	'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase	:       Dict     =	re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__lowerCAmelCase	:       Any     =	re.compile(r'\s*\(\s*"(\S[^"]+)"')




def     __magic_name__       (				A	:      int,						A	:      bool = False     ):






			'''simple docstring'''







			with open(A,						"r",						encoding="utf-8"     ) as f:
						a					=    f.read()

			a					=    content.split("\n"     )
			a					=    []
			a					=    0
			while line_idx < len(A     ):
						if _re_intro_mapping.search(lines[line_idx]     ) is not None:
									a					=    len(re.search(R"^(\s*)\S",						lines[line_idx]     ).groups()[0]     ) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(" " * indent + "("     ):
												new_lines.append(lines[line_idx]     )
												line_idx += 1

									a					=    []
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															a					=    line_idx
															while not lines[line_idx].startswith(" " * indent + ")"     ):
																		line_idx += 1
															blocks.append("\n".join(lines[start_idx : line_idx + 1]     )     )
												else:
															blocks.append(lines[line_idx]     )
												line_idx += 1

									# Sort blocks by their identifiers
									a					=    sorted(A,						key=lambda A     : _re_identifier.search(A     ).groups()[0]     )
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]     )
									line_idx += 1

			if overwrite:
						with open(A,						"w",						encoding="utf-8"     ) as f:
									f.write("\n".join(A     )     )
			elif "\n".join(A     ) != content:
						return True




def     __magic_name__       (				A	:      bool = False     ):






			'''simple docstring'''







			a					=    [os.path.join(A,						A     ) for f in os.listdir(A     ) if f.endswith(".py"     )]
			a					=    [sort_auto_mapping(A,						overwrite=A     ) for fname in fnames]

			if not overwrite and any(A     ):
						a					=    [f for f, d in zip(A,						A     ) if d]
						raise ValueError(
						    F"""The following files have auto mappings that need sorting: {", ".join(A     )}. Run `make style` to fix"""
						    " this."     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Dict     =	argparse.ArgumentParser()
		parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
		__lowerCAmelCase	:       Optional[Any]     =	parser.parse_args()

		sort_all_auto_mappings(not args.check_only)






