




import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: List[Any]    =					CLIPTokenizer
      lowerCAmelCase__			: str    =					CLIPTokenizerFast
      lowerCAmelCase__			: Dict    =					True
      lowerCAmelCase__			: Dict    =					{}
      lowerCAmelCase__			: str    =					False







      def       snake_case	(     self					: Dict				):
          super().setUp()

          # fmt: off
          __UpperCamelCase  		=						['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
          # fmt: on
          __UpperCamelCase  		=						dict(zip(snake_case ,       range(len(snake_case				)				)				)				)
          __UpperCamelCase  		=						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
          __UpperCamelCase  		=						{'''unk_token''': '''<unk>'''}

          __UpperCamelCase  		=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''vocab_file''']				)
          __UpperCamelCase  		=						os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''merges_file''']				)
          with open(self.vocab_file ,       '''w''' ,       encoding='''utf-8'''				) as fp:
              fp.write(json.dumps(snake_case				) + '''\n'''				)
          with open(self.merges_file ,       '''w''' ,       encoding='''utf-8'''				) as fp:
              fp.write('''\n'''.join(snake_case				)				)







      def       snake_case	(     self					: int ,       **snake_case					: List[Any]				):
          kwargs.update(self.special_tokens_map				)
          return CLIPTokenizer.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: List[str] ,       **snake_case					: Tuple				):
          kwargs.update(self.special_tokens_map				)
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: str				):
          __UpperCamelCase  		=						'''lower newer'''
          __UpperCamelCase  		=						'''lower newer'''
          return input_text, output_text







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						CLIPTokenizer(self.vocab_file ,       self.merges_file ,       **self.special_tokens_map				)
          __UpperCamelCase  		=						'''lower newer'''
          __UpperCamelCase  		=						['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
          __UpperCamelCase  		=						tokenizer.tokenize(snake_case				)
          self.assertListEqual(snake_case ,       snake_case				)

          __UpperCamelCase  		=						tokens + [tokenizer.unk_token]
          __UpperCamelCase  		=						[10, 2, 16, 9, 3, 2, 16, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case				) ,       snake_case				)







      @require_ftfy
      def       snake_case	(     self					: List[Any]				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                  __UpperCamelCase  		=						self.tokenizer_class.from_pretrained(snake_case ,       **snake_case				)
                  __UpperCamelCase  		=						self.rust_tokenizer_class.from_pretrained(snake_case ,       **snake_case				)

                  __UpperCamelCase  		=						'''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
                  __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                  __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                  self.assertListEqual(snake_case ,       snake_case				)

                  # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                  # with Tilde) encoded in 2 different ways
                  __UpperCamelCase  		=						'''xa\u0303y''' + ''' ''' + '''x\xe3y'''
                  __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                  __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                  self.assertListEqual(snake_case ,       snake_case				)

                  # Test that the tokenization is identical on unicode of space type
                  __UpperCamelCase  		=						[
                      '''\u0009''',  # (horizontal tab, '\t')
                      '''\u000B''',  # (vertical tab)
                      '''\u000C''',  # (form feed)
                      '''\u0020''',  # (space, ' ')
                      '''\u200E''',  # (left-to-right mark):w
                      '''\u200F''',  # (right-to-left mark)
                  ]
                  for unicode_seq in spaces_unicodes:
                      __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                      __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                      self.assertListEqual(snake_case ,       snake_case				)

                  # Test that the tokenization is identical on unicode of line break type
                  __UpperCamelCase  		=						[
                      '''\u000A''',  # (line feed, '\n')
                      '''\r\n''',  # (carriage return and line feed, '\r\n')
                      '''\u000D''',  # (carriage return, '\r')
                      '''\r''',  # (carriage return, '\r')
                      '''\u000D''',  # (carriage return, '\r')
                      '''\u2028''',  # (line separator)
                      '''\u2029''',  # (paragraph separator)
                      # "\u0085", # (next line)
                  ]

                  # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                  # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                  # space (and thus into an empty list).

                  for unicode_seq in line_break_unicodes:
                      __UpperCamelCase  		=						tokenizer_s.tokenize(snake_case				)
                      __UpperCamelCase  		=						tokenizer_r.tokenize(snake_case				)

                      self.assertListEqual(snake_case ,       snake_case				)







      def       snake_case	(     self					: Tuple				):
          # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                  __UpperCamelCase  		=						'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                  __UpperCamelCase  		=						F"{text_of_1_token} {text_of_1_token}"

                  __UpperCamelCase  		=						self.rust_tokenizer_class.from_pretrained(
                      snake_case ,       use_fast=snake_case ,       )
                  __UpperCamelCase  		=						tokenizer_r(snake_case ,       return_offsets_mapping=snake_case ,       add_special_tokens=snake_case				)
                  self.assertEqual(encoding.offset_mapping[0] ,       (0, len(snake_case				))				)
                  self.assertEqual(
                      encoding.offset_mapping[1] ,       (len(snake_case				) + 1, len(snake_case				) + 1 + len(snake_case				)) ,       )

                  __UpperCamelCase  		=						F" {text}"

                  __UpperCamelCase  		=						self.rust_tokenizer_class.from_pretrained(
                      snake_case ,       use_fast=snake_case ,       )
                  __UpperCamelCase  		=						tokenizer_r(snake_case ,       return_offsets_mapping=snake_case ,       add_special_tokens=snake_case				)
                  self.assertEqual(encoding.offset_mapping[0] ,       (1, 1 + len(snake_case				))				)
                  self.assertEqual(
                      encoding.offset_mapping[1] ,       (1 + len(snake_case				) + 1, 1 + len(snake_case				) + 1 + len(snake_case				)) ,       )







      def       snake_case	(     self					: Tuple				):
          # Test related to the breaking change introduced in transformers v4.17.0
          # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
          with self.assertRaises(snake_case				) as context:
              self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''				)

          self.assertTrue(
              context.exception.args[0].startswith(
                  '''The `backend_tokenizer` provided does not match the expected format.'''				)				)







      @require_ftfy
      def       snake_case	(     self					: Tuple				):
          super().test_tokenization_python_rust_equals()







      def       snake_case	(     self					: List[Any]				):
          # CLIP always lower cases letters
          pass







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_      )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						model.config

    __UpperCamelCase  		=						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					,     )
    __UpperCamelCase  		=						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_      )       ->	Any:

    """simple docstring"""


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

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

    return name




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


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

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

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

    return orig_state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=None					,     lowercase_=False      )       ->	Dict:

    """simple docstring"""


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

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

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

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

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

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

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

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

    # verify decoder hidden states
    __UpperCamelCase  		=						original_model(lowercase_					,     lowercase_					,     lowercase_      ).logits
    __UpperCamelCase  		=						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__":
 a_   		=     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_   		=     parser.parse_args()
 convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






