




"""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 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		__lowercase				:		Dict												=				model.config

		__lowercase				:		List[Any]												=				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=1_28,  )
		__lowercase				:		Tuple												=				MBartConfig(
		    is_decoder=__A,  is_encoder_decoder=__A,  add_cross_attention=__A,  decoder_layers=original_config.decoder_layer,  max_position_embeddings=original_config.max_position_embeddings,  vocab_size=len(
		        model.decoder.tokenizer				),  scale_embedding=__A,  add_final_layer_norm=__A,  )

		return encoder_config, decoder_config

def 						snake_case__       (					_lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


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

				if name == "encoder.norm.weight":
						__lowercase				:		List[str]												=				"""encoder.layernorm.weight"""
				if name == "encoder.norm.bias":
						__lowercase				:		Tuple												=				"""encoder.layernorm.bias"""

		return name

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Optional[int]:






		"""simple docstring"""


		for key in orig_state_dict.copy().keys():
				__lowercase				:		str												=				orig_state_dict.pop(__A				)

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

						if "weight" in key:
								__lowercase				:		List[str]												=				val[:dim, :]
								__lowercase				:		Tuple												=				val[dim : dim * 2, :]
								__lowercase				:		List[str]												=				val[-dim:, :]
						else:
								__lowercase				:		Optional[Any]												=				val[:dim]
								__lowercase				:		Tuple												=				val[dim : dim * 2]
								__lowercase				:		List[str]												=				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:
						__lowercase				:		Union[str, Any]												=				val

		return orig_state_dict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None,  _lowerCamelCase=False				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase				:		int												=				DonutModel.from_pretrained(__A				).eval()

		# load HuggingFace model
		__lowercase				:		int												=				get_configs(__A				)
		__lowercase				:		Tuple												=				DonutSwinModel(__A				)
		__lowercase				:		str												=				MBartForCausalLM(__A				)
		__lowercase				:		Dict												=				VisionEncoderDecoderModel(encoder=__A,  decoder=__A				)
		model.eval()

		__lowercase				:		List[str]												=				original_model.state_dict()
		__lowercase				:		str												=				convert_state_dict(__A,  __A				)
		model.load_state_dict(__A				)

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

		__lowercase				:		Optional[Any]												=				XLMRobertaTokenizerFast.from_pretrained(__A,  from_slow=__A				)
		__lowercase				:		Any												=				DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis,  size=original_model.config.input_size[::-1]				)
		__lowercase				:		str												=				DonutProcessor(__A,  __A				)
		__lowercase				:		List[Any]												=				processor(__A,  return_tensors="pt"				).pixel_values

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

		__lowercase				:		Optional[int]												=				original_model.encoder.model.patch_embed(__A				)
		__lowercase				:		List[str]												=				model.encoder.embeddings(__A				)
		assert torch.allclose(__A,  __A,  atol=1E-3				)

		# verify encoder hidden states
		__lowercase				:		Any												=				original_model.encoder(__A				)
		__lowercase				:		Tuple												=				model.encoder(__A				).last_hidden_state
		assert torch.allclose(__A,  __A,  atol=1E-2				)

		# verify decoder hidden states
		__lowercase				:		Any												=				original_model(__A,  __A,  __A				).logits
		__lowercase				:		Optional[Any]												=				model(__A,  decoder_input_ids=__A				).logits
		assert torch.allclose(__A,  __A,  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(__A				)
				processor.save_pretrained(__A				)

		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__":
				__A   :  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.',
				)

				__A   :  Optional[int]					=      parser.parse_args()
				convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
__A   :  int					=      '\nHuman: <<task>>\n\nAssistant: '


__A   :  List[str]					=      'huggingface-tools/default-prompts'
__A   :  Tuple					=      {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase="run"				)			->Optional[int]:






		"""simple docstring"""


		if prompt_or_repo_id is None:
				__lowercase				:		Any												=				DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search("\\s",  _lowerCamelCase				) is not None:
				return prompt_or_repo_id

		__lowercase				:		Optional[Any]												=				cached_file(
		    _lowerCamelCase,  PROMPT_FILES[mode],  repo_type="dataset",  user_agent={"agent": agent_name}				)
		with open(_lowerCamelCase,  "r",  encoding="utf-8"				) as f:
				return f.read()
