





'''simple docstring'''
import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 				lowercase__   (   __lowercase						:  Any     ) ->      Tuple:



       """simple docstring"""






       __UpperCamelCase       =  []
       embed.append(
           (
               F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
               F'''stage{idx}.patch_embed.proj.weight''',
           )     )
       embed.append(
           (
               F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
               F'''stage{idx}.patch_embed.proj.bias''',
           )     )
       embed.append(
           (
               F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
               F'''stage{idx}.patch_embed.norm.weight''',
           )     )
       embed.append(
           (
               F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
               F'''stage{idx}.patch_embed.norm.bias''',
           )     )
       return embed
def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Dict     ) ->      Tuple:



       """simple docstring"""






       __UpperCamelCase       =  []
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
               F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
               F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
               F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
               F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
               F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
           )     )
       attention_weights.append(
           (
               F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
               F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
           )     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''')     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''')     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''')     )
       attention_weights.append(
           (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''')     )
       return attention_weights
def 				lowercase__   (   __lowercase						:  List[Any]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  []
       token.append((F'''cvt.encoder.stages.{idx}.cls_token''', 'stage2.cls_token')     )
       return token
def 				lowercase__   (   ) ->      Optional[Any]:



       """simple docstring"""






       __UpperCamelCase       =  []
       head.append(('layernorm.weight', 'norm.weight')     )
       head.append(('layernorm.bias', 'norm.bias')     )
       head.append(('classifier.weight', 'head.weight')     )
       head.append(('classifier.bias', 'head.bias')     )
       return head
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  Tuple				,  __lowercase						:  Any				,  __lowercase						:  Any     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  'imagenet-1k-id2label.json'
       __UpperCamelCase       =  1000

       __UpperCamelCase       =  'huggingface/label-files'
       __UpperCamelCase       =  num_labels
       __UpperCamelCase       =  json.load(open(cached_download(hf_hub_url(__lowercase				,  __lowercase				,  repo_type='dataset'     )     )				,  'r'     )     )
       __UpperCamelCase       =  {int(__lowercase     ): v for k, v in idalabel.items()}

       __UpperCamelCase       =  idalabel
       __UpperCamelCase       =  {v: k for k, v in idalabel.items()}

       __UpperCamelCase       =  __UpperCamelCase       =  CvtConfig(num_labels=__lowercase				,  idalabel=__lowercase				,  labelaid=__lowercase     )

       # For depth size 13 (13 = 1+2+10)
       if cvt_model.rsplit('/'				,  1     )[-1][4:6] == "13":
              __UpperCamelCase       =  [1, 2, 10]

       # For depth size 21 (21 = 1+4+16)
       elif cvt_model.rsplit('/'				,  1     )[-1][4:6] == "21":
              __UpperCamelCase       =  [1, 4, 16]

       # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
       else:
              __UpperCamelCase       =  [2, 2, 20]
              __UpperCamelCase       =  [3, 12, 16]
              __UpperCamelCase       =  [192, 768, 1024]

       __UpperCamelCase       =  CvtForImageClassification(__lowercase     )
       __UpperCamelCase       =  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     )
       __UpperCamelCase       =  image_size
       __UpperCamelCase       =  torch.load(__lowercase				,  map_location=torch.device('cpu'     )     )

       __UpperCamelCase       =  OrderedDict()
       __UpperCamelCase       =  []

       for idx in range(len(config.depth     )     ):
              if config.cls_token[idx]:
                     __UpperCamelCase       =  list_of_state_dict + cls_token(__lowercase     )
              __UpperCamelCase       =  list_of_state_dict + embeddings(__lowercase     )
              for cnt in range(config.depth[idx]     ):
                     __UpperCamelCase       =  list_of_state_dict + attention(__lowercase				,  __lowercase     )

       __UpperCamelCase       =  list_of_state_dict + final()
       for gg in list_of_state_dict:
              print(__lowercase     )
       for i in range(len(__lowercase     )     ):
              __UpperCamelCase       =  original_weights[list_of_state_dict[i][1]]

       model.load_state_dict(__lowercase     )
       model.save_pretrained(__lowercase     )
       image_processor.save_pretrained(__lowercase     )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    a__							:							List[str] =argparse.ArgumentParser()
    parser.add_argument(
        '''--cvt_model''',
        default='''cvt-w24''',
        type=str,
        help='''Name of the cvt model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--image_size''',
        default=384,
        type=int,
        help='''Input Image Size''',
    )
    parser.add_argument(
        '''--cvt_file_name''',
        default=r'''cvtmodels\CvT-w24-384x384-IN-22k.pth''',
        type=str,
        help='''Input Image Size''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    a__							:							Dict =parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
    from PIL import Image

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="naver-clova-ix/donut-base-finetuned-docvqa"
 SCREAMING_SNAKE_CASE_	:					Dict					=(
     "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
     "should be the document containing the information, as well as a `question` that is the question about the "
     "document. It returns a text that contains the answer to the question."
 )
 SCREAMING_SNAKE_CASE_	:					List[str]					="document_qa"
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=AutoProcessor
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=VisionEncoderDecoderModel

 SCREAMING_SNAKE_CASE_	:					List[Any]					=["image", "text"]
 SCREAMING_SNAKE_CASE_	:					Any					=["text"]






 def __init__(       self       :							Optional[int]							,	*__A       :							List[str]							,	**__A       :							List[Any]	):
        if not is_vision_available():
               raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'	)

        super().__init__(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							"Image"							,	__A       :							str	):
        __UpperCamelCase       =  '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
        __UpperCamelCase       =  task_prompt.replace('{user_input}'							,	__A	)
        __UpperCamelCase       =  self.pre_processor.tokenizer(
            __A							,	add_special_tokens=__A							,	return_tensors='pt'	).input_ids
        __UpperCamelCase       =  self.pre_processor(__A							,	return_tensors='pt'	).pixel_values

        return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							Optional[Any]	):
        return self.model.generate(
            inputs['pixel_values'].to(self.device	)							,	decoder_input_ids=inputs['decoder_input_ids'].to(self.device	)							,	max_length=self.model.decoder.config.max_position_embeddings							,	early_stopping=__A							,	pad_token_id=self.pre_processor.tokenizer.pad_token_id							,	eos_token_id=self.pre_processor.tokenizer.eos_token_id							,	use_cache=__A							,	num_beams=1							,	bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]							,	return_dict_in_generate=__A							,	).sequences






 def        _lowerCamelCase					(       self       :							Tuple							,	__A       :							List[Any]	):
        __UpperCamelCase       =  self.pre_processor.batch_decode(__A	)[0]
        __UpperCamelCase       =  sequence.replace(self.pre_processor.tokenizer.eos_token							,	''	)
        __UpperCamelCase       =  sequence.replace(self.pre_processor.tokenizer.pad_token							,	''	)
        __UpperCamelCase       =  re.sub(R'<.*?>'							,	''							,	__A							,	count=1	).strip()  # remove first task start token
        __UpperCamelCase       =  self.pre_processor.tokenajson(__A	)

        return sequence["answer"]
