




import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


a_   		=     "\\n\n"

a_   		=     "\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

a_   		=     "\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION   )
class  _lowerCamelCase						(			datasets.Metric   ):

      """simple docstring"""

      def       snake_case	(     self					: Dict				):
          return datasets.MetricInfo(
              description=_DESCRIPTION ,       citation=_CITATION ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
                  {
                      '''input_texts''': datasets.Value('''string'''				),
                  }				) ,       reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] ,       )







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Dict ,       snake_case					: Any ,       snake_case					: int = 16 ,       snake_case					: bool = True ,       snake_case					: str=None				):
          if device is not None:
              assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
              if device == "gpu":
                  __UpperCamelCase  		=						'''cuda'''
          else:
              __UpperCamelCase  		=						'''cuda''' if torch.cuda.is_available() else '''cpu'''

          __UpperCamelCase  		=						AutoModelForCausalLM.from_pretrained(snake_case				)
          __UpperCamelCase  		=						model.to(snake_case				)

          __UpperCamelCase  		=						AutoTokenizer.from_pretrained(snake_case				)

          # if batch_size > 1 (which generally leads to padding being required), and
          # if there is not an already assigned pad_token, assign an existing
          # special token to also be the padding token
          if tokenizer.pad_token is None and batch_size > 1:
              __UpperCamelCase  		=						list(tokenizer.special_tokens_map_extended.values()				)
              # check that the model already has at least one special token defined
              assert (
                  len(snake_case				) > 0
              ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
              # assign one of the special tokens to also be the pad token
              tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]}				)

          if add_start_token:
              # leave room for <BOS> token to be added:
              assert (
                  tokenizer.bos_token is not None
              ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
              __UpperCamelCase  		=						model.config.max_length - 1
          else:
              __UpperCamelCase  		=						model.config.max_length

          __UpperCamelCase  		=						tokenizer(
              snake_case ,       add_special_tokens=snake_case ,       padding=snake_case ,       truncation=snake_case ,       max_length=snake_case ,       return_tensors='''pt''' ,       return_attention_mask=snake_case ,       ).to(snake_case				)

          __UpperCamelCase  		=						encodings['''input_ids''']
          __UpperCamelCase  		=						encodings['''attention_mask''']

          # check that each input is long enough:
          if add_start_token:
              assert torch.all(torch.ge(attn_masks.sum(1				) ,       1				)				), "Each input text must be at least one token long."
          else:
              assert torch.all(
                  torch.ge(attn_masks.sum(1				) ,       2				)				), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						CrossEntropyLoss(reduction='''none'''				)

          for start_index in logging.tqdm(range(0 ,       len(snake_case				) ,       snake_case				)				):
              __UpperCamelCase  		=						min(start_index + batch_size ,       len(snake_case				)				)
              __UpperCamelCase  		=						encoded_texts[start_index:end_index]
              __UpperCamelCase  		=						attn_masks[start_index:end_index]

              if add_start_token:
                  __UpperCamelCase  		=						torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0				)				).to(snake_case				)
                  __UpperCamelCase  		=						torch.cat([bos_tokens_tensor, encoded_batch] ,       dim=1				)
                  __UpperCamelCase  		=						torch.cat(
                      [torch.ones(bos_tokens_tensor.size() ,       dtype=torch.intaa				).to(snake_case				), attn_mask] ,       dim=1				)

              __UpperCamelCase  		=						encoded_batch

              with torch.no_grad():
                  __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case				).logits

              __UpperCamelCase  		=						out_logits[..., :-1, :].contiguous()
              __UpperCamelCase  		=						labels[..., 1:].contiguous()
              __UpperCamelCase  		=						attn_mask[..., 1:].contiguous()

              __UpperCamelCase  		=						torch.expa(
                  (loss_fct(shift_logits.transpose(1 ,       2				) ,       snake_case				) * shift_attention_mask_batch).sum(1				)
                  / shift_attention_mask_batch.sum(1				)				)

              ppls += perplexity_batch.tolist()

          return {"perplexities": ppls, "mean_perplexity": np.mean(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   (  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)






