import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case__				= 'src/diffusers'
snake_case__				= '.'


# This is to make sure the diffusers module imported is the one in the repo.
snake_case__				= importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
snake_case__				= spec.loader.load_module()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      return line.startswith(__UpperCAmelCase		) or len(__UpperCAmelCase		) <= 1 or re.search(r'''^\s*\)(\s*->.*:|:)\s*$''' ,						__UpperCAmelCase		) is not None
def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				object_name.split('''.'''		)
      _lowerCamelCase			    =				0

      # First let's find the module where our object lives.
      _lowerCamelCase			    =				parts[i]
      while i < len(__UpperCAmelCase		) and not os.path.isfile(os.path.join(__UpperCAmelCase ,						F'{module}.py'		)		):
            i += 1
            if i < len(__UpperCAmelCase		):
                  _lowerCamelCase			    =				os.path.join(__UpperCAmelCase ,						parts[i]		)
      if i >= len(__UpperCAmelCase		):
            raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'		)

      with open(os.path.join(__UpperCAmelCase ,						F'{module}.py'		) ,						'''r''' ,						encoding='''utf-8''' ,						newline='''\n'''		) as f:
            _lowerCamelCase			    =				f.readlines()

      # Now let's find the class / func in the code!
      _lowerCamelCase			    =				''''''
      _lowerCamelCase			    =				0
      for name in parts[i + 1 :]:
            while (
                line_index < len(__UpperCAmelCase		) and re.search(rF'^{indent}(class|def)\s+{name}(\(|\:)' ,						lines[line_index]		) is None
            ):
                  line_index += 1
            indent += "    "
            line_index += 1

      if line_index >= len(__UpperCAmelCase		):
            raise ValueError(F' {object_name} does not match any function or class in {module}.'		)

      # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
      _lowerCamelCase			    =				line_index
      while line_index < len(__UpperCAmelCase		) and _should_continue(lines[line_index] ,						__UpperCAmelCase		):
            line_index += 1
      # Clean up empty lines at the end (if any).
      while len(lines[line_index - 1]		) <= 1:
            line_index -= 1

      _lowerCamelCase			    =				lines[start_index:line_index]
      return "".join(__UpperCAmelCase		)


snake_case__				= re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
snake_case__				= re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
snake_case__				= re.compile(R'<FILL\s+[^>]*>')
def       __magic_name__(  __UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      _lowerCamelCase			    =				code.split('''\n'''		)
      _lowerCamelCase			    =				0
      while idx < len(__UpperCAmelCase		) and len(lines[idx]		) == 0:
            idx += 1
      if idx < len(__UpperCAmelCase		):
            return re.search(r'''^(\s*)\S''' ,						lines[idx]		).groups()[0]
      return ""
def       __magic_name__(  __UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				len(get_indent(__UpperCAmelCase		)		) > 0
      if has_indent:
            _lowerCamelCase			    =				F'class Bla:\n{code}'
      _lowerCamelCase			    =				black.Mode(target_versions={black.TargetVersion.PYaa} ,						line_length=119 ,						preview=__UpperCAmelCase		)
      _lowerCamelCase			    =				black.format_str(__UpperCAmelCase ,						mode=__UpperCAmelCase		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				style_docstrings_in_code(__UpperCAmelCase		)
      return result[len('''class Bla:\n'''		) :] if has_indent else result
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=False		)						->    List[str]:



      '''simple docstring'''

      with open(__UpperCAmelCase ,						'''r''' ,						encoding='''utf-8''' ,						newline='''\n'''		) as f:
            _lowerCamelCase			    =				f.readlines()
      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				0
      # Not a for loop cause `lines` is going to change (if `overwrite=True`).
      while line_index < len(__UpperCAmelCase		):
            _lowerCamelCase			    =				_re_copy_warning.search(lines[line_index]		)
            if search is None:
                  line_index += 1
                  continue

            # There is some copied code here, let's retrieve the original.
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				search.groups()
            _lowerCamelCase			    =				find_code_in_diffusers(__UpperCAmelCase		)
            _lowerCamelCase			    =				get_indent(__UpperCAmelCase		)

            _lowerCamelCase			    =				line_index + 1 if indent == theoretical_indent else line_index + 2
            _lowerCamelCase			    =				theoretical_indent
            _lowerCamelCase			    =				start_index

            # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
            _lowerCamelCase			    =				True
            while line_index < len(__UpperCAmelCase		) and should_continue:
                  line_index += 1
                  if line_index >= len(__UpperCAmelCase		):
                        break
                  _lowerCamelCase			    =				lines[line_index]
                  _lowerCamelCase			    =				_should_continue(__UpperCAmelCase ,						__UpperCAmelCase		) and re.search(F'^{indent}# End copy' ,						__UpperCAmelCase		) is None
            # Clean up empty lines at the end (if any).
            while len(lines[line_index - 1]		) <= 1:
                  line_index -= 1

            _lowerCamelCase			    =				lines[start_index:line_index]
            _lowerCamelCase			    =				''''''.join(__UpperCAmelCase		)

            # Remove any nested `Copied from` comments to avoid circular copies
            _lowerCamelCase			    =				[line for line in theoretical_code.split('''\n'''		) if _re_copy_warning.search(__UpperCAmelCase		) is None]
            _lowerCamelCase			    =				'''\n'''.join(__UpperCAmelCase		)

            # Before comparing, use the `replace_pattern` on the original code.
            if len(__UpperCAmelCase		) > 0:
                  _lowerCamelCase			    =				replace_pattern.replace('''with''' ,						''''''		).split(''','''		)
                  _lowerCamelCase			    =				[_re_replace_pattern.search(__UpperCAmelCase		) for p in patterns]
                  for pattern in patterns:
                        if pattern is None:
                              continue
                        _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				pattern.groups()
                        _lowerCamelCase			    =				re.sub(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
                        if option.strip() == "all-casing":
                              _lowerCamelCase			    =				re.sub(obja.lower() ,						obja.lower() ,						__UpperCAmelCase		)
                              _lowerCamelCase			    =				re.sub(obja.upper() ,						obja.upper() ,						__UpperCAmelCase		)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
                  _lowerCamelCase			    =				blackify(lines[start_index - 1] + theoretical_code		)
                  _lowerCamelCase			    =				theoretical_code[len(lines[start_index - 1]		) :]

            # Test for a diff and act accordingly.
            if observed_code != theoretical_code:
                  diffs.append([object_name, start_index]		)
                  if overwrite:
                        _lowerCamelCase			    =				lines[:start_index] + [theoretical_code] + lines[line_index:]
                        _lowerCamelCase			    =				start_index + 1

      if overwrite and len(__UpperCAmelCase		) > 0:
            # Warn the user a file has been modified.
            print(F'Detected changes, rewriting {filename}.'		)
            with open(__UpperCAmelCase ,						'''w''' ,						encoding='''utf-8''' ,						newline='''\n'''		) as f:
                  f.writelines(__UpperCAmelCase		)
      return diffs
def       __magic_name__(  __UpperCAmelCase = False		)						->    Tuple:



      '''simple docstring'''

      _lowerCamelCase			    =				glob.glob(os.path.join(__UpperCAmelCase ,						'''**/*.py'''		) ,						recursive=__UpperCAmelCase		)
      _lowerCamelCase			    =				[]
      for filename in all_files:
            _lowerCamelCase			    =				is_copy_consistent(__UpperCAmelCase ,						__UpperCAmelCase		)
            diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
      if not overwrite and len(__UpperCAmelCase		) > 0:
            _lowerCamelCase			    =				'''\n'''.join(__UpperCAmelCase		)
            raise Exception(
                '''Found the following copy inconsistencies:\n'''
                + diff
                + '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'''		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     snake_case__				= parser.parse_args()

     check_copies(args.fix_and_overwrite)
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__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				model.config

      _lowerCamelCase			    =				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 ,						)
      _lowerCamelCase			    =				MBartConfig(
          is_decoder=__UpperCAmelCase ,						is_encoder_decoder=__UpperCAmelCase ,						add_cross_attention=__UpperCAmelCase ,						decoder_layers=original_config.decoder_layer ,						max_position_embeddings=original_config.max_position_embeddings ,						vocab_size=len(
              model.decoder.tokenizer		) ,						scale_embedding=__UpperCAmelCase ,						add_final_layer_norm=__UpperCAmelCase ,						)

      return encoder_config, decoder_config
def       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

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

            if name == "encoder.norm.weight":
                  _lowerCamelCase			    =				'''encoder.layernorm.weight'''
            if name == "encoder.norm.bias":
                  _lowerCamelCase			    =				'''encoder.layernorm.bias'''

      return name
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      for key in orig_state_dict.copy().keys():
            _lowerCamelCase			    =				orig_state_dict.pop(__UpperCAmelCase		)

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

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

      return orig_state_dict
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=False		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				DonutModel.from_pretrained(__UpperCAmelCase		).eval()

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

      _lowerCamelCase			    =				original_model.state_dict()
      _lowerCamelCase			    =				convert_state_dict(__UpperCAmelCase ,						__UpperCAmelCase		)
      model.load_state_dict(__UpperCAmelCase		)

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

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

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

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

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

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

      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__":
     snake_case__				= 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.',
     )

     snake_case__				= parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)