




import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase    :		Any			   =       logging.get_logger(__name__)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[int]     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      lowerCamelCase_							=   []
      for i in range(encoder_config.num_hidden_layers					):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append(
                (f"""encoder.deit.blocks.{i}.norm1.weight""", f"""encoder.encoder.layer.{i}.layernorm_before.weight""")					)
            rename_keys.append((f"""encoder.deit.blocks.{i}.norm1.bias""", f"""encoder.encoder.layer.{i}.layernorm_before.bias""")					)
            rename_keys.append(
                (f"""encoder.deit.blocks.{i}.attn.proj.weight""", f"""encoder.encoder.layer.{i}.attention.output.dense.weight""")					)
            rename_keys.append(
                (f"""encoder.deit.blocks.{i}.attn.proj.bias""", f"""encoder.encoder.layer.{i}.attention.output.dense.bias""")					)
            rename_keys.append(
                (f"""encoder.deit.blocks.{i}.norm2.weight""", f"""encoder.encoder.layer.{i}.layernorm_after.weight""")					)
            rename_keys.append((f"""encoder.deit.blocks.{i}.norm2.bias""", f"""encoder.encoder.layer.{i}.layernorm_after.bias""")					)
            rename_keys.append(
                (f"""encoder.deit.blocks.{i}.mlp.fc1.weight""", f"""encoder.encoder.layer.{i}.intermediate.dense.weight""")					)
            rename_keys.append(
                (f"""encoder.deit.blocks.{i}.mlp.fc1.bias""", f"""encoder.encoder.layer.{i}.intermediate.dense.bias""")					)
            rename_keys.append(
                (f"""encoder.deit.blocks.{i}.mlp.fc2.weight""", f"""encoder.encoder.layer.{i}.output.dense.weight""")					)
            rename_keys.append((f"""encoder.deit.blocks.{i}.mlp.fc2.bias""", f"""encoder.encoder.layer.{i}.output.dense.bias""")					)

      # cls token, position embeddings and patch embeddings of encoder
      rename_keys.extend(
          [
              ('encoder.deit.cls_token', 'encoder.embeddings.cls_token'),
              ('encoder.deit.pos_embed', 'encoder.embeddings.position_embeddings'),
              ('encoder.deit.patch_embed.proj.weight', 'encoder.embeddings.patch_embeddings.projection.weight'),
              ('encoder.deit.patch_embed.proj.bias', 'encoder.embeddings.patch_embeddings.projection.bias'),
              ('encoder.deit.norm.weight', 'encoder.layernorm.weight'),
              ('encoder.deit.norm.bias', 'encoder.layernorm.bias'),
          ]					)

      return rename_keys

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



      for i in range(encoder_config.num_hidden_layers					):
            # queries, keys and values (only weights, no biases)
            lowerCamelCase_							=   state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""					)

            lowerCamelCase_							=   in_proj_weight[
                : encoder_config.hidden_size, :
            ]
            lowerCamelCase_							=   in_proj_weight[
                encoder_config.hidden_size : encoder_config.hidden_size * 2, :
            ]
            lowerCamelCase_							=   in_proj_weight[
                -encoder_config.hidden_size :, :
            ]

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]     ,							lowercase     :	List[Any]     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   dct.pop(lowercase					)
      lowerCamelCase_							=   val

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]					):


      '''simple docstring'''



      if "handwritten" in checkpoint_url:
            lowerCamelCase_							=   'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'  # industry
            # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
            # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
            # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
            # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
      elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
            lowerCamelCase_							=   'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
      lowerCamelCase_							=   Image.open(requests.get(lowercase     ,							stream=lowercase					).raw					).convert('RGB'					)
      return im

@torch.no_grad()
def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      lowerCamelCase_							=   ViTConfig(image_size=3_84     ,							qkv_bias=lowercase					)
      lowerCamelCase_							=   TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            lowerCamelCase_							=   7_68
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            lowerCamelCase_							=   10_24
            lowerCamelCase_							=   40_96
            lowerCamelCase_							=   24
            lowerCamelCase_							=   16
            lowerCamelCase_							=   10_24
      else:
            raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'					)

      # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
      if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
            lowerCamelCase_							=   False
            lowerCamelCase_							=   'relu'
            lowerCamelCase_							=   10_24
            lowerCamelCase_							=   True
            lowerCamelCase_							=   False
            lowerCamelCase_							=   False

      # load HuggingFace model
      lowerCamelCase_							=   ViTModel(lowercase     ,							add_pooling_layer=lowercase					)
      lowerCamelCase_							=   TrOCRForCausalLM(lowercase					)
      lowerCamelCase_							=   VisionEncoderDecoderModel(encoder=lowercase     ,							decoder=lowercase					)
      model.eval()

      # load state_dict of original model, rename some keys
      lowerCamelCase_							=   torch.hub.load_state_dict_from_url(lowercase     ,							map_location='cpu'     ,							check_hash=lowercase					)['model']

      lowerCamelCase_							=   create_rename_keys(lowercase     ,							lowercase					)
      for src, dest in rename_keys:
            rename_key(lowercase     ,							lowercase     ,							lowercase					)
      read_in_q_k_v(lowercase     ,							lowercase					)

      # remove parameters we don't need
      del state_dict["encoder.deit.head.weight"]
      del state_dict["encoder.deit.head.bias"]
      del state_dict["decoder.version"]

      # add prefix to decoder keys
      for key, val in state_dict.copy().items():
            lowerCamelCase_							=   state_dict.pop(lowercase					)
            if key.startswith('decoder'					) and "output_projection" not in key:
                  lowerCamelCase_							=   val
            else:
                  lowerCamelCase_							=   val

    # load state dict
      model.load_state_dict(lowercase					)

      # Check outputs on an image
      lowerCamelCase_							=   ViTImageProcessor(size=encoder_config.image_size					)
      lowerCamelCase_							=   RobertaTokenizer.from_pretrained('roberta-large'					)
      lowerCamelCase_							=   TrOCRProcessor(lowercase     ,							lowercase					)

      lowerCamelCase_							=   processor(images=prepare_img(lowercase					)     ,							return_tensors='pt'					).pixel_values

      # verify logits
      lowerCamelCase_							=   torch.tensor([[model.config.decoder.decoder_start_token_id]]					)
      lowerCamelCase_							=   model(pixel_values=lowercase     ,							decoder_input_ids=lowercase					)
      lowerCamelCase_							=   outputs.logits

      lowerCamelCase_							=   torch.Size([1, 1, 5_02_65]					)
      if "trocr-base-handwritten" in checkpoint_url:
            lowerCamelCase_							=   torch.tensor(
                [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]					)
      elif "trocr-large-handwritten" in checkpoint_url:
            lowerCamelCase_							=   torch.tensor(
                [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]					)
      elif "trocr-base-printed" in checkpoint_url:
            lowerCamelCase_							=   torch.tensor(
                [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]					)
      elif "trocr-large-printed" in checkpoint_url:
            lowerCamelCase_							=   torch.tensor(
                [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]					)

      if "stage1" not in checkpoint_url:
            assert logits.shape == expected_shape, "Shape of logits not as expected"
            assert torch.allclose(logits[0, 0, :10]     ,							lowercase     ,							atol=1e-3					), "First elements of logits not as expected"

      Path(lowercase					).mkdir(exist_ok=lowercase					)
      print(f"""Saving model to {pytorch_dump_folder_path}"""					)
      model.save_pretrained(lowercase					)
      print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
      processor.save_pretrained(lowercase					)


if __name__ == "__main__":
 lowerCamelCase    :		Tuple			   =       argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_url",
     default="https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt",
     type=str,
     help="URL to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 lowerCamelCase    :		Tuple			   =       parser.parse_args()
 convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


lowerCamelCase    :		Any			   =       r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''rag'''
 UpperCamelCase						       =						True

 def __init__(    self :  Optional[Any]						,							A_ :  Optional[Any]=None						,							A_ :  Any=True						,							A_ :  Dict=None						,							A_ :  Optional[int]=None						,							A_ :  str=None						,							A_ :  int=None						,							A_ :  List[Any]=None						,							A_ :  List[str]=" / "						,							A_ :  Tuple=" // "						,							A_ :  Union[str, Any]=5						,							A_ :  Optional[Any]=300						,							A_ :  int=768						,							A_ :  Dict=8						,							A_ :  int="wiki_dpr"						,							A_ :  int="train"						,							A_ :  List[str]="compressed"						,							A_ :  Tuple=None						,							A_ :  Optional[Any]=None						,							A_ :  Optional[int]=False						,							A_ :  str=False						,							A_ :  Optional[Any]=0.0						,							A_ :  Union[str, Any]=True						,							A_ :  List[Any]=False						,							A_ :  Union[str, Any]=False						,							A_ :  Dict=False						,							A_ :  str=True						,							A_ :  List[str]=None						,							**A_ :  Optional[Any]						,							)       ->     str:

       """simple docstring"""






       super().__init__(
           bos_token_id=A_						,							pad_token_id=A_						,							eos_token_id=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							is_encoder_decoder=A_						,							prefix=A_						,							vocab_size=A_						,							**A_						,							)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       lowerCamelCase_							=   kwargs.pop('question_encoder'  )
       lowerCamelCase_							=   question_encoder_config.pop('model_type'  )
       lowerCamelCase_							=   kwargs.pop('generator'  )
       lowerCamelCase_							=   decoder_config.pop('model_type'  )

       from ..auto.configuration_auto import AutoConfig

       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )
       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )

       lowerCamelCase_							=   reduce_loss
       lowerCamelCase_							=   label_smoothing
       lowerCamelCase_							=   exclude_bos_score
       lowerCamelCase_							=   do_marginalize

       lowerCamelCase_							=   title_sep
       lowerCamelCase_							=   doc_sep
       lowerCamelCase_							=   n_docs
       lowerCamelCase_							=   max_combined_length

       lowerCamelCase_							=   dataset
       lowerCamelCase_							=   dataset_split
       lowerCamelCase_							=   index_name

       lowerCamelCase_							=   retrieval_vector_size
       lowerCamelCase_							=   retrieval_batch_size
       lowerCamelCase_							=   passages_path
       lowerCamelCase_							=   index_path
       lowerCamelCase_							=   use_dummy_dataset

       lowerCamelCase_							=   output_retrieved

       lowerCamelCase_							=   do_deduplication

       lowerCamelCase_							=   use_cache

       if self.forced_eos_token_id is None:
             lowerCamelCase_							=   getattr(self.generator						,							'forced_eos_token_id'						,							A_  )

 @classmethod
 def     a__			(    cls :  str						,							A_ :  PretrainedConfig						,							A_ :  PretrainedConfig						,							**A_ :  List[str]  )       ->     PretrainedConfig:

       """simple docstring"""






       return cls(question_encoder=question_encoder_config.to_dict()						,							generator=generator_config.to_dict()						,							**A_  )





 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   copy.deepcopy(self.__dict__  )
       lowerCamelCase_							=   self.question_encoder.to_dict()
       lowerCamelCase_							=   self.generator.to_dict()
       lowerCamelCase_							=   self.__class__.model_type
       return output



