# Copyright 2022 The HuggingFace 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 argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase_  : Optional[int]								=					'Run commands across TPU VMs for initial setup before running `accelerate launch`.'







def       A__   (						snake_case_			:		Tuple=None   ):
 if subparsers is not None:
  SCREAMING_SNAKE_CASE__: int=   subparsers.add_parser('''tpu-config'''							,					description=_description   )
 else:
  SCREAMING_SNAKE_CASE__: Optional[Any]=   argparse.ArgumentParser('''Accelerate tpu-config command'''							,					description=_description   )
 # Core arguments
 SCREAMING_SNAKE_CASE__: List[str]=   parser.add_argument_group(
     '''Config Arguments'''							,					'''Arguments that can be configured through `accelerate config`.'''   )
 config_args.add_argument(
     '''--config_file'''							,					type=snake_case_							,					default=snake_case_							,					help='''Path to the config file to use for accelerate.'''							,					)
 config_args.add_argument(
     '''--tpu_name'''							,					default=snake_case_							,					help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''							,					)
 config_args.add_argument(
     '''--tpu_zone'''							,					default=snake_case_							,					help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''							,					)
 SCREAMING_SNAKE_CASE__: int=   parser.add_argument_group('''TPU Arguments'''							,					'''Arguments for options ran inside the TPU.'''   )
 pod_args.add_argument(
     '''--use_alpha'''							,					action='''store_true'''							,					help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''							,					)
 pod_args.add_argument(
     '''--command_file'''							,					default=snake_case_							,					help='''The path to the file containing the commands to run on the pod on startup.'''							,					)
 pod_args.add_argument(
     '''--command'''							,					action='''append'''							,					nargs='''+'''							,					help='''A command to run on the pod. Can be passed multiple times.'''							,					)
 pod_args.add_argument(
     '''--install_accelerate'''							,					action='''store_true'''							,					help='''Whether to install accelerate on the pod. Defaults to False.'''							,					)
 pod_args.add_argument(
     '''--accelerate_version'''							,					default='''latest'''							,					help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''							,					)
 pod_args.add_argument(
     '''--debug'''							,					action='''store_true'''							,					help='''If set, will print the command that would be run instead of running it.'''   )

 if subparsers is not None:
  parser.set_defaults(func=snake_case_   )
 return parser







def       A__   (						snake_case_			:		Optional[Any]   ):
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   None

 # Get the default from the config file if it exists.
 if args.config_file is not None or os.path.isfile(snake_case_   ):
  SCREAMING_SNAKE_CASE__: int=   load_config_from_file(args.config_file   )
  if not args.command_file and defaults.command_file is not None and not args.command:
   SCREAMING_SNAKE_CASE__: Optional[int]=   defaults.command_file
  if not args.command and defaults.commands is not None:
   SCREAMING_SNAKE_CASE__: int=   defaults.commands
  if not args.tpu_name:
   SCREAMING_SNAKE_CASE__: str=   defaults.tpu_name
  if not args.tpu_zone:
   SCREAMING_SNAKE_CASE__: List[Any]=   defaults.tpu_zone
 if args.accelerate_version == "dev":
  SCREAMING_SNAKE_CASE__: str=   '''git+https://github.com/huggingface/accelerate.git'''
 elif args.accelerate_version == "latest":
  SCREAMING_SNAKE_CASE__: Optional[int]=   '''accelerate -U'''
 elif isinstance(parse(args.accelerate_version   )							,					snake_case_   ):
  SCREAMING_SNAKE_CASE__: List[Any]=   F'accelerate=={args.accelerate_version}'

 if not args.command_file and not args.command:
  raise ValueError('''You must specify either a command file or a command to run on the pod.'''   )

 if args.command_file:
  with open(args.command_file							,					'''r'''   ) as f:
   SCREAMING_SNAKE_CASE__: List[Any]=   [f.read().splitlines()]

    # To turn list of lists into list of strings
 if isinstance(args.command[0]							,					snake_case_   ):
  SCREAMING_SNAKE_CASE__: Dict=   [line for cmd in args.command for line in cmd]
 # Default to the shared folder and install accelerate
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   ['''cd /usr/share''']
 if args.install_accelerate:
  new_cmd += [F'pip install {args.accelerate_version}']
 new_cmd += args.command
 SCREAMING_SNAKE_CASE__: Dict=   '''; '''.join(snake_case_   )

 # Then send it to gcloud
 # Eventually try to use google-api-core to do this instead of subprocess
 SCREAMING_SNAKE_CASE__: List[Any]=   ['''gcloud''']
 if args.use_alpha:
  cmd += ["alpha"]
 cmd += [
     "compute",
     "tpus",
     "tpu-vm",
     "ssh",
     args.tpu_name,
     "--zone",
     args.tpu_zone,
     "--command",
     args.command,
     "--worker",
     "all",
 ]
 if args.debug:
  print(F'Running {" ".join(snake_case_   )}'   )
  return
 subprocess.run(snake_case_   )
 print('''Successfully setup pod.'''   )







def       A__   (						):
 SCREAMING_SNAKE_CASE__: List[str]=   tpu_command_parser()
 SCREAMING_SNAKE_CASE__: Dict=   parser.parse_args()

 tpu_command_launcher(snake_case_   )








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				(      snake_case							)		->    Optional[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=snake_case						,   is_encoder_decoder=snake_case						,   add_cross_attention=snake_case						,   decoder_layers=original_config.decoder_layer						,   max_position_embeddings=original_config.max_position_embeddings						,   vocab_size=len(
		        model.decoder.tokenizer							)						,   scale_embedding=snake_case						,   add_final_layer_norm=snake_case						,   )

		return encoder_config, decoder_config



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		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				(      snake_case						,   snake_case							)		->    List[Any]:
		for key in orig_state_dict.copy().keys():
				__lowercase											=     orig_state_dict.pop(snake_case							)

				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				(      snake_case						,   snake_case=None						,   snake_case=False							)		->    Any:
		# load original model
		__lowercase											=     DonutModel.from_pretrained(snake_case							).eval()

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

		__lowercase											=     original_model.state_dict()
		__lowercase											=     convert_state_dict(snake_case						,   snake_case							)
		model.load_state_dict(snake_case							)

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

		__lowercase											=     XLMRobertaTokenizerFast.from_pretrained(snake_case						,   from_slow=snake_case							)
		__lowercase											=     DonutImageProcessor(
		    do_align_long_axis=original_model.config.align_long_axis						,   size=original_model.config.input_size[::-1]							)
		__lowercase											=     DonutProcessor(snake_case						,   snake_case							)
		__lowercase											=     processor(snake_case						,   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}'						,   snake_case							)
		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(snake_case						,   add_special_tokens=snake_case						,   return_tensors='pt'							)[
		    'input_ids'
		]

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

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

		# verify decoder hidden states
		__lowercase											=     original_model(snake_case						,   snake_case						,   snake_case							).logits
		__lowercase											=     model(snake_case						,   decoder_input_ids=snake_case							).logits
		assert torch.allclose(snake_case						,   snake_case						,   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(snake_case							)
				processor.save_pretrained(snake_case							)

		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__":
		SCREAMING_SNAKE_CASE_   :      Dict			=   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.''',
		)

		SCREAMING_SNAKE_CASE_   :      Tuple			=   parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




