




"""simple docstring"""



from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''informer'''
    lowercase__						 =				{
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
        '''num_hidden_layers''': '''encoder_layers''',
    }




    def __init__(   self						:	Any     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	Optional[int] = None     ,			snake_case_						:	str = "student_t"     ,			snake_case_						:	str = "nll"     ,			snake_case_						:	int = 1     ,			snake_case_						:	List[int] = None     ,			snake_case_						:	Optional[Union[str, bool]] = "mean"     ,			snake_case_						:	int = 0     ,			snake_case_						:	int = 0     ,			snake_case_						:	int = 0     ,			snake_case_						:	int = 0     ,			snake_case_						:	Optional[List[int]] = None     ,			snake_case_						:	Optional[List[int]] = None     ,			snake_case_						:	int = 64     ,			snake_case_						:	int = 32     ,			snake_case_						:	int = 32     ,			snake_case_						:	int = 2     ,			snake_case_						:	int = 2     ,			snake_case_						:	int = 2     ,			snake_case_						:	int = 2     ,			snake_case_						:	bool = True     ,			snake_case_						:	str = "gelu"     ,			snake_case_						:	float = 0.05     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	float = 0.1     ,			snake_case_						:	int = 100     ,			snake_case_						:	float = 0.02     ,			snake_case_						:	Optional[int]=True     ,			snake_case_						:	str = "prob"     ,			snake_case_						:	int = 5     ,			snake_case_						:	bool = True     ,			**snake_case_						:	Optional[Any]     ,			)				->							Any:




     '''simple docstring'''

     A__	=    prediction_length
     A__	=    context_length or prediction_length
     A__	=    distribution_output
     A__	=    loss
     A__	=    input_size
     A__	=    num_time_features
     A__	=    lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
     A__	=    scaling
     A__	=    num_dynamic_real_features
     A__	=    num_static_real_features
     A__	=    num_static_categorical_features

     # set cardinality
     if cardinality and num_static_categorical_features > 0:
      if len(snake_case_				) != num_static_categorical_features:
       raise ValueError(
           "The cardinality should be a list of the same length as `num_static_categorical_features`"				)
      A__	=    cardinality
     else:
      A__	=    [0]

     # set embedding_dimension
     if embedding_dimension and num_static_categorical_features > 0:
      if len(snake_case_				) != num_static_categorical_features:
       raise ValueError(
           "The embedding dimension should be a list of the same length as `num_static_categorical_features`"				)
      A__	=    embedding_dimension
     else:
      A__	=    [min(50     ,			(cat + 1) // 2				) for cat in self.cardinality]

     A__	=    num_parallel_samples

     # Transformer architecture configuration
     A__	=    input_size * len(self.lags_sequence				) + self._number_of_features
     A__	=    d_model
     A__	=    encoder_attention_heads
     A__	=    decoder_attention_heads
     A__	=    encoder_ffn_dim
     A__	=    decoder_ffn_dim
     A__	=    encoder_layers
     A__	=    decoder_layers

     A__	=    dropout
     A__	=    attention_dropout
     A__	=    activation_dropout
     A__	=    encoder_layerdrop
     A__	=    decoder_layerdrop

     A__	=    activation_function
     A__	=    init_std

     A__	=    use_cache

     # Informer
     A__	=    attention_type
     A__	=    sampling_factor
     A__	=    distil

     super().__init__(is_encoder_decoder=snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	Tuple				)				->							int:




     '''simple docstring'''

     return (
         sum(self.embedding_dimension				)
         + self.num_dynamic_real_features
         + self.num_time_features
         + self.num_static_real_features
         + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
     )











"""simple docstring"""



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_						)						->		List[str]:
 A__	=    model.config

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

  if name == "encoder.norm.weight":
   A__	=    "encoder.layernorm.weight"
  if name == "encoder.norm.bias":
   A__	=    "encoder.layernorm.bias"

 return name







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		Any:
 for key in orig_state_dict.copy().keys():
  A__	=    orig_state_dict.pop(lowercase_						)

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

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

 return orig_state_dict







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_=None      ,			lowercase_=False						)						->		Dict:
 # load original model
 A__	=    DonutModel.from_pretrained(lowercase_						).eval()

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

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

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

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

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

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

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

 # verify decoder hidden states
 A__	=    original_model(lowercase_      ,			lowercase_      ,			lowercase_						).logits
 A__	=    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__":
      SCREAMING_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.",
      )

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





