


import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


lowerCamelCase					:		Any            =						logging.get_logger(__name__)

lowerCamelCase					:		Tuple            =						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase					:		Tuple            =						{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
lowerCamelCase					:		int            =						{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
lowerCamelCase					:		List[Any]            =						{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase					:		str            =						{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
lowerCamelCase					:		Any            =						{
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
lowerCamelCase					:		Tuple            =						{
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


lowerCamelCase					:		Dict            =						{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase					:		int            =						{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
lowerCamelCase					:		Optional[int]            =						{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}


class snake_case__			(    UpperCamelCase_					):
       _lowerCAmelCase   =VOCAB_FILES_NAMES
       _lowerCAmelCase   =CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       _lowerCAmelCase   =CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowerCAmelCase   =CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION


class snake_case__			(    UpperCamelCase_					):
       _lowerCAmelCase   =VOCAB_FILES_NAMES
       _lowerCAmelCase   =QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       _lowerCAmelCase   =QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowerCAmelCase   =QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

lowerCamelCase					:		Optional[int]            =						collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

lowerCamelCase					:		Union[str, Any]            =						collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCamelCase					:		Union[str, Any]            =						r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    ```
    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
    ```

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Returns:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """


@add_start_docstrings(UpperCamelCase_					)
class snake_case__			:




       def __call__( self				:    Union[str, Any]    ,				_lowerCamelCase				:    Dict    ,				_lowerCamelCase				:    Optional[str] = None    ,				_lowerCamelCase				:    Optional[str] = None    ,				_lowerCamelCase				:    Union[bool, str] = False    ,				_lowerCamelCase				:    Union[bool, str] = False    ,				_lowerCamelCase				:    Optional[int] = None    ,				_lowerCamelCase				:    Optional[Union[str, TensorType]] = None    ,				_lowerCamelCase				:    Optional[bool] = None    ,				**_lowerCamelCase				:    Union[str, Any]    ,				):
              if titles is None and texts is None:
                     return super().__call__(
                         snake_case__    ,				padding=snake_case__    ,				truncation=snake_case__    ,				max_length=snake_case__    ,				return_tensors=snake_case__    ,				return_attention_mask=snake_case__    ,				**snake_case__    ,				)
              elif titles is None or texts is None:
                     snake_case__ :       str	   =							titles if texts is None else texts
                     return super().__call__(
                         snake_case__    ,				snake_case__    ,				padding=snake_case__    ,				truncation=snake_case__    ,				max_length=snake_case__    ,				return_tensors=snake_case__    ,				return_attention_mask=snake_case__    ,				**snake_case__    ,				)
              snake_case__ :       Dict	   =							titles if not isinstance(snake_case__    ,				snake_case__			) else [titles]
              snake_case__ :       int	   =							texts if not isinstance(snake_case__    ,				snake_case__			) else [texts]
              snake_case__ :       str	   =							len(snake_case__			)
              snake_case__ :       Union[str, Any]	   =							questions if not isinstance(snake_case__    ,				snake_case__			) else [questions] * n_passages
              if len(snake_case__			) != len(snake_case__			):
                     raise ValueError(
                         F'''There should be as many titles than texts but got {len(snake_case__			)} titles and {len(snake_case__			)} texts.'''			)
              snake_case__ :       Dict	   =							super().__call__(snake_case__    ,				snake_case__    ,				padding=snake_case__    ,				truncation=snake_case__			)["input_ids"]
              snake_case__ :       Dict	   =							super().__call__(snake_case__    ,				add_special_tokens=snake_case__    ,				padding=snake_case__    ,				truncation=snake_case__			)["input_ids"]
              snake_case__ :       Optional[int]	   =							{
                  "input_ids": [
                      (encoded_question_and_title + encoded_text)[:max_length]
                      if max_length is not None and truncation
                      else encoded_question_and_title + encoded_text
                      for encoded_question_and_title, encoded_text in zip(snake_case__    ,				snake_case__			)
                  ]
              }
              if return_attention_mask is not False:
                     snake_case__ :       Tuple	   =							[]
                     for input_ids in encoded_inputs["input_ids"]:
                            attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
                     snake_case__ :       Any	   =							attention_mask
              return self.pad(snake_case__    ,				padding=snake_case__    ,				max_length=snake_case__    ,				return_tensors=snake_case__			)




       def     UpperCAmelCase__    ( self				:    Any    ,				_lowerCamelCase				:    BatchEncoding    ,				_lowerCamelCase				:    DPRReaderOutput    ,				_lowerCamelCase				:    int = 1_6    ,				_lowerCamelCase				:    int = 6_4    ,				_lowerCamelCase				:    int = 4    ,				):
              snake_case__ :       List[str]	   =							reader_input["input_ids"]
              snake_case__ :       Dict	   =							reader_output[:3]
              snake_case__ :       List[str]	   =							len(snake_case__			)
              snake_case__ :       int	   =							sorted(range(snake_case__			)    ,				reverse=snake_case__    ,				key=relevance_logits.__getitem__			)
              snake_case__ :       List[DPRReaderOutput]	   =							[]
              for doc_id in sorted_docs:
                     snake_case__ :       Optional[Any]	   =							list(input_ids[doc_id]			)
                     # assuming question & title information is at the beginning of the sequence
                     snake_case__ :       List[Any]	   =							sequence_ids.index(self.sep_token_id    ,				2			) + 1  # second sep id
                     if sequence_ids[-1] == self.pad_token_id:
                            snake_case__ :       Union[str, Any]	   =							sequence_ids.index(self.pad_token_id			)
                     else:
                            snake_case__ :       Optional[Any]	   =							len(snake_case__			)

                     snake_case__ :       List[str]	   =							self._get_best_spans(
                         start_logits=start_logits[doc_id][passage_offset:sequence_len]    ,				end_logits=end_logits[doc_id][passage_offset:sequence_len]    ,				max_answer_length=snake_case__    ,				top_spans=snake_case__    ,				)
                     for start_index, end_index in best_spans:
                            start_index += passage_offset
                            end_index += passage_offset
                            nbest_spans_predictions.append(
                                DPRSpanPrediction(
                                    span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]    ,				relevance_score=relevance_logits[doc_id]    ,				doc_id=snake_case__    ,				start_index=snake_case__    ,				end_index=snake_case__    ,				text=self.decode(sequence_ids[start_index : end_index + 1]			)    ,				)			)
                     if len(snake_case__			) >= num_spans:
                            break
              return nbest_spans_predictions[:num_spans]




       def     UpperCAmelCase__    ( self				:    Union[str, Any]    ,				_lowerCamelCase				:    List[int]    ,				_lowerCamelCase				:    List[int]    ,				_lowerCamelCase				:    int    ,				_lowerCamelCase				:    int    ,				):
              snake_case__ :       Optional[Any]	   =							[]
              for start_index, start_score in enumerate(snake_case__			):
                     for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]			):
                            scores.append(((start_index, start_index + answer_length), start_score + end_score)			)
              snake_case__ :       Union[str, Any]	   =							sorted(snake_case__    ,				key=lambda _lowerCamelCase			: x[1]    ,				reverse=snake_case__			)
              snake_case__ :       Any	   =							[]
              for (start_index, end_index), score in scores:
                     if start_index > end_index:
                            raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]'''			)
                     snake_case__ :       Tuple	   =							end_index - start_index + 1
                     if length > max_answer_length:
                            raise ValueError(F'''Span is too long: {length} > {max_answer_length}'''			)
                     if any(
                         start_index <= prev_start_index <= prev_end_index <= end_index
                         or prev_start_index <= start_index <= end_index <= prev_end_index
                         for (prev_start_index, prev_end_index) in chosen_span_intervals			):
                            continue
                     chosen_span_intervals.append((start_index, end_index)			)

                     if len(snake_case__			) == top_spans:
                            break
              return chosen_span_intervals


@add_end_docstrings(UpperCamelCase_					)
class snake_case__			(    UpperCamelCase_    ,   UpperCamelCase_					):
       _lowerCAmelCase   =VOCAB_FILES_NAMES
       _lowerCAmelCase   =READER_PRETRAINED_VOCAB_FILES_MAP
       _lowerCAmelCase   =READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowerCAmelCase   =READER_PRETRAINED_INIT_CONFIGURATION
       _lowerCAmelCase   =['input_ids', 'attention_mask']













"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase	:	Optional[Any]				=      logging.get_logger(__name__)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Any=False			,					snake_case       : Any=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""

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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f'{prefix}cls_token', "beit.embeddings.cls_token"),
             (f'{prefix}patch_embed.proj.weight', "beit.embeddings.patch_embeddings.projection.weight"),
             (f'{prefix}patch_embed.proj.bias', "beit.embeddings.patch_embeddings.projection.bias"),
             (f'{prefix}pos_embed', "beit.embeddings.position_embeddings"),
         ]				)

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ("mask_token", "beit.embeddings.mask_token"),
                  ("norm.weight", "layernorm.weight"),
                  ("norm.bias", "layernorm.bias"),
              ]				)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ("fc_norm.weight", "beit.pooler.layernorm.weight"),
                  ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                  ("head.weight", "classifier.weight"),
                  ("head.bias", "classifier.bias"),
              ]				)

     return rename_keys


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : List[str]			,					snake_case       : List[Any]=False			,					snake_case       : int=False				)->		Any:






     '''simple docstring'''



     for i in range(config.num_hidden_layers				):
          UpperCAmelCase__						:    Dict							=							"backbone." if is_semantic else ""
          # queries, keys and values
          UpperCAmelCase__						:    List[str]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'				)
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'				)
          UpperCAmelCase__						:    Optional[int]							=							state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'				)

          UpperCAmelCase__						:    List[Any]							=							in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase__						:    Dict							=							q_bias
          UpperCAmelCase__						:    Optional[int]							=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase__						:    Any							=							in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase__						:    int							=							v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCAmelCase__						:    int							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_1'				)
          UpperCAmelCase__						:    Tuple							=							state_dict.pop(f'{prefix}blocks.{i}.gamma_2'				)

          UpperCAmelCase__						:    Dict							=							gamma_a
          UpperCAmelCase__						:    Tuple							=							gamma_a


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Dict			,					snake_case       : Tuple				)->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							dct.pop(snake_case				)
     UpperCAmelCase__						:    Tuple							=							val


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							"http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase__						:    List[str]							=							Image.open(requests.get(snake_case			,					stream=snake_case				).raw				)
     return im


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : str			,					snake_case       : Optional[Any]=False				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[Any]							=							False if "rvlcdip" in checkpoint_url else True
     UpperCAmelCase__						:    int							=							BeitConfig(use_absolute_position_embeddings=snake_case			,					use_mask_token=snake_case				)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          UpperCAmelCase__						:    Optional[int]							=							1024
          UpperCAmelCase__						:    Optional[Any]							=							4096
          UpperCAmelCase__						:    List[str]							=							24
          UpperCAmelCase__						:    List[str]							=							16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCAmelCase__						:    List[Any]							=							16
          UpperCAmelCase__						:    Optional[int]							=							"huggingface/label-files"
          UpperCAmelCase__						:    List[Any]							=							"rvlcdip-id2label.json"
          UpperCAmelCase__						:    List[str]							=							json.load(open(hf_hub_download(snake_case			,					snake_case			,					repo_type="dataset"				)			,					"r"				)				)
          UpperCAmelCase__						:    Dict							=							{int(snake_case				): v for k, v in idalabel.items()}
          UpperCAmelCase__						:    List[Any]							=							idalabel
          UpperCAmelCase__						:    Optional[Any]							=							{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase__						:    List[Any]							=							torch.hub.load_state_dict_from_url(snake_case			,					map_location="cpu"				)["model"]

     UpperCAmelCase__						:    str							=							create_rename_keys(snake_case			,					has_lm_head=snake_case				)
     for src, dest in rename_keys:
          rename_key(snake_case			,					snake_case			,					snake_case				)
     read_in_q_k_v(snake_case			,					snake_case			,					has_lm_head=snake_case				)

     # load HuggingFace model
     UpperCAmelCase__						:    int							=							BeitForMaskedImageModeling(snake_case				) if has_lm_head else BeitForImageClassification(snake_case				)
     model.eval()
     model.load_state_dict(snake_case				)

     # Check outputs on an image
     UpperCAmelCase__						:    List[Any]							=							BeitImageProcessor(
         size=config.image_size			,					resample=PILImageResampling.BILINEAR			,					do_center_crop=snake_case				)
     UpperCAmelCase__						:    Any							=							prepare_img()

     UpperCAmelCase__						:    Any							=							image_processor(images=snake_case			,					return_tensors="pt"				)
     UpperCAmelCase__						:    List[str]							=							encoding["pixel_values"]

     UpperCAmelCase__						:    Optional[Any]							=							model(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							outputs.logits

     # verify logits
     UpperCAmelCase__						:    Union[str, Any]							=							[1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(snake_case				), "Shape of logits not as expected"

     Path(snake_case				).mkdir(exist_ok=snake_case				)
     print(f'Saving model to {pytorch_dump_folder_path}'				)
     model.save_pretrained(snake_case				)
     print(f'Saving image processor to {pytorch_dump_folder_path}'				)
     image_processor.save_pretrained(snake_case				)

     if push_to_hub:
          if has_lm_head:
               UpperCAmelCase__						:    Any							=							"dit-base" if "base" in checkpoint_url else "dit-large"
          else:
               UpperCAmelCase__						:    int							=							"dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
          image_processor.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add image processor"			,					use_temp_dir=snake_case			,					)
          model.push_to_hub(
              repo_path_or_name=Path(snake_case			,					snake_case				)			,					organization="nielsr"			,					commit_message="Add model"			,					use_temp_dir=snake_case			,					)


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

 parser.add_argument(
     """--checkpoint_url""",
     default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
     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."""
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
 )
 _lowerCAmelCase	:	str				=      parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


