



'''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      SCREAMING_SNAKE_CASE				(				lowercase_    :      int						):
						lowercase        =							model.config

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

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

						return name







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      List[Any]						):
						for key in orig_state_dict.copy().keys():
												lowercase        =							orig_state_dict.pop(lowercase_						)

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

																		if "weight" in key:
																								lowercase        =							val[:dim, :]
																								lowercase        =							val[dim : dim * 2, :]
																								lowercase        =							val[-dim:, :]
																		else:
																								lowercase        =							val[:dim]
																								lowercase        =							val[dim : dim * 2]
																								lowercase        =							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        =							val

						return orig_state_dict







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      Optional[int]=None ,  lowercase_    :      Dict=False						):
						# load original model
						lowercase        =							DonutModel.from_pretrained(lowercase_						).eval()

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

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

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

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

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

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

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

						# verify decoder hidden states
						lowercase        =							original_model(lowercase_ ,  lowercase_ ,  lowercase_						).logits
						lowercase        =							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__":
						lowercase_      :				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.''',
						)

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







'''simple docstring'''


# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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.

# this script dumps information about the environment

import os
import platform
import sys


lowercase_      :				List[str]   					=    '''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
						import torch

						print('''Torch version:''', torch.__version__)
						print('''Cuda available:''', torch.cuda.is_available())
						print('''Cuda version:''', torch.version.cuda)
						print('''CuDNN version:''', torch.backends.cudnn.version())
						print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
						print('''Torch version:''', None)

try:
						import transformers

						print('''transformers version:''', transformers.__version__)
except ImportError:
						print('''transformers version:''', None)


