import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


snake_case__				= 'bert-base-cased'
snake_case__				= 'google/pegasus-xsum'
snake_case__				= [' Sam ate lunch today.', 'Sams lunch ingredients.']
snake_case__				= ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
snake_case__				= 'patrickvonplaten/t5-tiny-random'
snake_case__				= 'sshleifer/bart-tiny-random'
snake_case__				= 'sshleifer/tiny-mbart'
snake_case__				= 'sshleifer/tiny-marian-en-de'
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[int]:



      '''simple docstring'''

      _lowerCamelCase			    =				'''\n'''.join(__UpperCAmelCase		)
      Path(__UpperCAmelCase		).open('''w'''		).writelines(__UpperCAmelCase		)
def       __magic_name__(  __UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

      for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(__UpperCAmelCase ,						F'{split}.source'		) ,						__UpperCAmelCase		)
            _dump_articles(os.path.join(__UpperCAmelCase ,						F'{split}.target'		) ,						__UpperCAmelCase		)
      return tmp_dir




class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]				,				)
     @slow
     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				AutoTokenizer.from_pretrained(A_						)
           _lowerCamelCase			    =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
           _lowerCamelCase			    =				max(len(tokenizer.encode(A_						)						) for a in ARTICLES						)
           _lowerCamelCase			    =				max(len(tokenizer.encode(A_						)						) for a in SUMMARIES						)
           _lowerCamelCase			    =				4
           _lowerCamelCase			    =				8
           assert max_len_target > max_src_len  # Will be truncated
           assert max_len_source > max_src_len  # Will be truncated
           _lowerCamelCase    ,					_lowerCamelCase			    =				'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
           _lowerCamelCase			    =				SeqaSeqDataset(
               A_				,				data_dir=A_				,				type_path='''train'''				,				max_source_length=A_				,				max_target_length=A_				,				src_lang=A_				,				tgt_lang=A_				,				)
           _lowerCamelCase			    =				DataLoader(A_				,				batch_size=2				,				collate_fn=train_dataset.collate_fn						)
           for batch in dataloader:
                 assert isinstance(A_				,				A_						)
                 assert batch["attention_mask"].shape == batch["input_ids"].shape
                 # show that articles were trimmed.
                 assert batch["input_ids"].shape[1] == max_src_len
                 # show that targets are the same len
                 assert batch["labels"].shape[1] == max_tgt_len
                 if tok_name != MBART_TINY:
                       continue
                 # check language codes in correct place
                 _lowerCamelCase			    =				shift_tokens_right(batch['''labels''']				,				tokenizer.pad_token_id						)
                 assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
                 assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
                 assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
                 assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

                 break  # No need to test every batch



     @parameterized.expand([BART_TINY, BERT_BASE_CASED]						)
     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				AutoTokenizer.from_pretrained(A_						)
           _lowerCamelCase			    =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
           _lowerCamelCase			    =				max(len(tokenizer.encode(A_						)						) for a in ARTICLES						)
           _lowerCamelCase			    =				max(len(tokenizer.encode(A_						)						) for a in SUMMARIES						)
           _lowerCamelCase			    =				4
           _lowerCamelCase			    =				LegacySeqaSeqDataset(
               A_				,				data_dir=A_				,				type_path='''train'''				,				max_source_length=20				,				max_target_length=A_				,				)
           _lowerCamelCase			    =				DataLoader(A_				,				batch_size=2				,				collate_fn=train_dataset.collate_fn						)
           for batch in dataloader:
                 assert batch["attention_mask"].shape == batch["input_ids"].shape
                 # show that articles were trimmed.
                 assert batch["input_ids"].shape[1] == max_len_source
                 assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
                 # show that targets were truncated
                 assert batch["labels"].shape[1] == trunc_target  # Truncated
                 assert max_len_target > trunc_target  # Truncated
                 break  # No need to test every batch



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''						)

           _lowerCamelCase			    =				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
           _lowerCamelCase			    =				tmp_dir.joinpath('''train.source'''						).open().readlines()
           _lowerCamelCase			    =				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
           pack_data_dir(A_				,				A_				,				1_28				,				A_						)
           _lowerCamelCase			    =				{x.name for x in tmp_dir.iterdir()}
           _lowerCamelCase			    =				{x.name for x in save_dir.iterdir()}
           _lowerCamelCase			    =				save_dir.joinpath('''train.source'''						).open().readlines()
           # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
           # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
           assert len(A_						) < len(A_						)
           assert len(A_						) == 1
           assert len(packed_examples[0]						) == sum(len(A_						) for x in orig_examples						)
           assert orig_paths == new_paths



     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,				reason='''This test requires fairseq'''						)
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           if not FAIRSEQ_AVAILABLE:
                 return
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self._get_dataset(max_len=64						)
           _lowerCamelCase			    =				64
           _lowerCamelCase			    =				ds.make_dynamic_sampler(A_				,				required_batch_size_multiple=A_						)
           _lowerCamelCase			    =				[len(A_						) for x in batch_sampler]
           assert len(set(A_						)						) > 1  # it's not dynamic batch size if every batch is the same length
           assert sum(A_						) == len(A_						)  # no dropped or added examples
           _lowerCamelCase			    =				DataLoader(A_				,				batch_sampler=A_				,				collate_fn=ds.collate_fn				,				num_workers=2						)
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				[]
           for batch in data_loader:
                 _lowerCamelCase			    =				batch['''input_ids'''].shape
                 _lowerCamelCase			    =				src_shape[0]
                 assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                 _lowerCamelCase			    =				np.product(batch['''input_ids'''].shape						)
                 num_src_per_batch.append(A_						)
                 if num_src_tokens > (max_tokens * 1.1):
                       failures.append(A_						)
           assert num_src_per_batch[0] == max(A_						)
           if failures:
                 raise AssertionError(F'too many tokens in {len(A_						)} batches'						)



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self._get_dataset(max_len=5_12						)
           _lowerCamelCase			    =				2
           _lowerCamelCase			    =				ds.make_sortish_sampler(A_				,				shuffle=A_						)

           _lowerCamelCase			    =				DataLoader(A_				,				batch_size=A_				,				collate_fn=ds.collate_fn				,				num_workers=2						)
           _lowerCamelCase			    =				DataLoader(A_				,				batch_size=A_				,				collate_fn=ds.collate_fn				,				num_workers=2				,				sampler=A_						)

           _lowerCamelCase			    =				tokenizer.pad_token_id

           def count_pad_tokens(A_				,				A_="input_ids"						):
                 return [batch[k].eq(A_						).sum().item() for batch in data_loader]

           assert sum(count_pad_tokens(A_				,				k='''labels'''						)						) < sum(count_pad_tokens(A_				,				k='''labels'''						)						)
           assert sum(count_pad_tokens(A_						)						) < sum(count_pad_tokens(A_						)						)
           assert len(A_						) == len(A_						)



     def 		UpperCamelCase_						(					self				,				A_=10_00				,				A_=1_28						) ->				Tuple:






           """simple docstring"""
           if os.getenv('''USE_REAL_DATA'''				,				A_						):
                 _lowerCamelCase			    =				'''examples/seq2seq/wmt_en_ro'''
                 _lowerCamelCase			    =				max_len * 2 * 64
                 if not Path(A_						).joinpath('''train.len'''						).exists():
                       save_len_file(A_				,				A_						)
           else:
                 _lowerCamelCase			    =				'''examples/seq2seq/test_data/wmt_en_ro'''
                 _lowerCamelCase			    =				max_len * 4
                 save_len_file(A_				,				A_						)

           _lowerCamelCase			    =				AutoTokenizer.from_pretrained(A_						)
           _lowerCamelCase			    =				SeqaSeqDataset(
               A_				,				data_dir=A_				,				type_path='''train'''				,				max_source_length=A_				,				max_target_length=A_				,				n_obs=A_				,				)
           return ds, max_tokens, tokenizer



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				self._get_dataset()
           _lowerCamelCase			    =				set(DistributedSortishSampler(A_				,				2_56				,				num_replicas=2				,				rank=0				,				add_extra_examples=A_						)						)
           _lowerCamelCase			    =				set(DistributedSortishSampler(A_				,				2_56				,				num_replicas=2				,				rank=1				,				add_extra_examples=A_						)						)
           assert idsa.intersection(A_						) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]				,				)
     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				AutoTokenizer.from_pretrained(A_				,				use_fast=A_						)
           if tok_name == MBART_TINY:
                 _lowerCamelCase			    =				SeqaSeqDataset(
                     A_				,				data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)				,				type_path='''train'''				,				max_source_length=4				,				max_target_length=8				,				src_lang='''EN'''				,				tgt_lang='''FR'''				,				)
                 _lowerCamelCase			    =				train_dataset.dataset_kwargs
                 assert "src_lang" in kwargs and "tgt_lang" in kwargs
           else:
                 _lowerCamelCase			    =				SeqaSeqDataset(
                     A_				,				data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)				,				type_path='''train'''				,				max_source_length=4				,				max_target_length=8				,				)
                 _lowerCamelCase			    =				train_dataset.dataset_kwargs
                 assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
                 assert len(A_						) == 1 if tok_name == BART_TINY else len(A_						) == 0
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)