


import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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







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

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ('''cls_token''', '''deit.embeddings.cls_token'''),
				        ('''dist_token''', '''deit.embeddings.distillation_token'''),
				        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
				        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
				        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
				    ])

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ('''norm.weight''', '''layernorm.weight'''),
								        ('''norm.bias''', '''layernorm.bias'''),
								        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
								        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
								    ])

								# if just the base model, we should remove "deit" from all keys that start with "deit"
								_SCREAMING_SNAKE_CASE			  =[(pair[0], pair[1][4:]) if pair[1].startswith('''deit''') else pair for pair in rename_keys]
				else:
								# layernorm + classification heads
								rename_keys.extend(
								    [
								        ('''norm.weight''', '''deit.layernorm.weight'''),
								        ('''norm.bias''', '''deit.layernorm.bias'''),
								        ('''head.weight''', '''cls_classifier.weight'''),
								        ('''head.bias''', '''cls_classifier.bias'''),
								        ('''head_dist.weight''', '''distillation_classifier.weight'''),
								        ('''head_dist.bias''', '''distillation_classifier.bias'''),
								    ])

				return rename_keys







def 						SCREAMING_SNAKE_CASE__(   a__	,a__	,a__=False):
				for i in range(config.num_hidden_layers):
								if base_model:
												_SCREAMING_SNAKE_CASE			  =''''''
								else:
												_SCREAMING_SNAKE_CASE			  ='''deit.'''
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_SCREAMING_SNAKE_CASE			  =state_dict.pop(f"blocks.{i}.attn.qkv.weight")
								_SCREAMING_SNAKE_CASE			  =state_dict.pop(f"blocks.{i}.attn.qkv.bias")
								# next, add query, keys and values (in that order) to the state dict
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    : config.hidden_size, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[: config.hidden_size]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[
								    -config.hidden_size :, :
								]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[-config.hidden_size :]







def 						SCREAMING_SNAKE_CASE__(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =dct.pop(a__)
				_SCREAMING_SNAKE_CASE			  =val







def 						SCREAMING_SNAKE_CASE__(   ):
				_SCREAMING_SNAKE_CASE			  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(a__	,stream=a__).raw)
				return im







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =DeiTConfig()
				# all deit models have fine-tuned heads
				_SCREAMING_SNAKE_CASE			  =False
				# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
				_SCREAMING_SNAKE_CASE			  =1000
				_SCREAMING_SNAKE_CASE			  ='''huggingface/label-files'''
				_SCREAMING_SNAKE_CASE			  ='''imagenet-1k-id2label.json'''
				_SCREAMING_SNAKE_CASE			  =json.load(open(hf_hub_download(a__	,a__	,repo_type='''dataset''')	,'''r'''))
				_SCREAMING_SNAKE_CASE			  ={int(a__): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE			  =idalabel
				_SCREAMING_SNAKE_CASE			  ={v: k for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE			  =int(deit_name[-6:-4])
				_SCREAMING_SNAKE_CASE			  =int(deit_name[-3:])
				# size of the architecture
				if deit_name[9:].startswith('''tiny'''):
								_SCREAMING_SNAKE_CASE			  =192
								_SCREAMING_SNAKE_CASE			  =768
								_SCREAMING_SNAKE_CASE			  =12
								_SCREAMING_SNAKE_CASE			  =3
				elif deit_name[9:].startswith('''small'''):
								_SCREAMING_SNAKE_CASE			  =384
								_SCREAMING_SNAKE_CASE			  =1536
								_SCREAMING_SNAKE_CASE			  =12
								_SCREAMING_SNAKE_CASE			  =6
				if deit_name[9:].startswith('''base'''):
								pass
				elif deit_name[4:].startswith('''large'''):
								_SCREAMING_SNAKE_CASE			  =1024
								_SCREAMING_SNAKE_CASE			  =4096
								_SCREAMING_SNAKE_CASE			  =24
								_SCREAMING_SNAKE_CASE			  =16

				# load original model from timm
				_SCREAMING_SNAKE_CASE			  =timm.create_model(a__	,pretrained=a__)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				_SCREAMING_SNAKE_CASE			  =timm_model.state_dict()
				_SCREAMING_SNAKE_CASE			  =create_rename_keys(a__	,a__)
				for src, dest in rename_keys:
								rename_key(a__	,a__	,a__)
				read_in_q_k_v(a__	,a__	,a__)

				# load HuggingFace model
				_SCREAMING_SNAKE_CASE			  =DeiTForImageClassificationWithTeacher(a__).eval()
				model.load_state_dict(a__)

				# Check outputs on an image, prepared by DeiTImageProcessor
				_SCREAMING_SNAKE_CASE			  =int(
				    (256 / 224) * config.image_size)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
				_SCREAMING_SNAKE_CASE			  =DeiTImageProcessor(size=a__	,crop_size=config.image_size)
				_SCREAMING_SNAKE_CASE			  =image_processor(images=prepare_img()	,return_tensors='''pt''')
				_SCREAMING_SNAKE_CASE			  =encoding['''pixel_values''']
				_SCREAMING_SNAKE_CASE			  =model(a__)

				_SCREAMING_SNAKE_CASE			  =timm_model(a__)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(a__	,outputs.logits	,atol=1e-3)

				Path(a__).mkdir(exist_ok=a__)
				print(f"Saving model {deit_name} to {pytorch_dump_folder_path}")
				model.save_pretrained(a__)
				print(f"Saving image processor to {pytorch_dump_folder_path}")
				image_processor.save_pretrained(a__)


if __name__ == "__main__":
							snake_case_						:			int													=							argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--deit_name''',
							    default='''vit_deit_base_distilled_patch16_224''',
							    type=str,
							    help='''Name of the DeiT timm model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							snake_case_						:			int													=							parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



# 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 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"naver-clova-ix/donut-base-finetuned-docvqa"
						UpperCAmelCase             =						(
						    "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."
						)
						UpperCAmelCase             =						"document_qa"
						UpperCAmelCase             =						AutoProcessor
						UpperCAmelCase             =						VisionEncoderDecoderModel

						UpperCAmelCase             =						["image", "text"]
						UpperCAmelCase             =						["text"]




						def __init__(       self					:      Any				,    *_a					:      int				,    **_a					:      Dict  )							->     int:


										"""simple docstring"""





										if not is_vision_available():
														raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.'''  )

										super().__init__(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      "Image"				,    _a					:      str  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
										_SCREAMING_SNAKE_CASE			  =task_prompt.replace('''{user_input}'''				,    _a  )
										_SCREAMING_SNAKE_CASE			  =self.pre_processor.tokenizer(
										    _a				,    add_special_tokens=_a				,    return_tensors='''pt'''  ).input_ids
										_SCREAMING_SNAKE_CASE			  =self.pre_processor(_a				,    return_tensors='''pt'''  ).pixel_values

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




						def 							__UpperCamelCase				(       self					:      List[Any]				,    _a					:      Optional[Any]  )							->     int:


										"""simple docstring"""





										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 							__UpperCamelCase				(       self					:      Any				,    _a					:      int  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.pre_processor.batch_decode(_a  )[0]
										_SCREAMING_SNAKE_CASE			  =sequence.replace(self.pre_processor.tokenizer.eos_token				,    ''''''  )
										_SCREAMING_SNAKE_CASE			  =sequence.replace(self.pre_processor.tokenizer.pad_token				,    ''''''  )
										_SCREAMING_SNAKE_CASE			  =re.sub(R'''<.*?>'''				,    ''''''				,    _a				,    count=1  ).strip()  # remove first task start token
										_SCREAMING_SNAKE_CASE			  =self.pre_processor.tokenajson(_a  )

										return sequence["answer"]