




"""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    lowerCAmelCase__							(      UpperCAmelCase_     ):


				"""simple docstring"""





				__UpperCAmelCase			:  Optional[Any]			   =   "naver-clova-ix/donut-base-finetuned-docvqa"
				__UpperCAmelCase			:  int			   =   (
				    "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			:  Tuple			   =   "document_qa"
				__UpperCAmelCase			:  List[str]			   =   AutoProcessor
				__UpperCAmelCase			:  Dict			   =   VisionEncoderDecoderModel

				__UpperCAmelCase			:  str			   =   ["image", "text"]
				__UpperCAmelCase			:  List[Any]			   =   ["text"]



				def __init__(  self       :						int     , *lowercase__       :						List[Any]     , **lowercase__       :						Tuple    ):




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

						super().__init__(*__lowerCamelCase     , **__lowerCamelCase    )



				def    snake_case     (  self       :						Optional[Any]     , lowercase__       :						"Image"     , lowercase__       :						str    ):
						__lowercase					:		Union[str, Any]        =					"<s_docvqa><s_question>{user_input}</s_question><s_answer>"
						__lowercase					:		Dict        =					task_prompt.replace("{user_input}"     , __lowerCamelCase    )
						__lowercase					:		Optional[int]        =					self.pre_processor.tokenizer(
						    __lowerCamelCase     , add_special_tokens=__lowerCamelCase     , return_tensors="pt"    ).input_ids
						__lowercase					:		Dict        =					self.pre_processor(__lowerCamelCase     , return_tensors="pt"    ).pixel_values

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



				def    snake_case     (  self       :						str     , lowercase__       :						Union[str, 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=__lowerCamelCase     , pad_token_id=self.pre_processor.tokenizer.pad_token_id     , eos_token_id=self.pre_processor.tokenizer.eos_token_id     , use_cache=__lowerCamelCase     , num_beams=1     , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]     , return_dict_in_generate=__lowerCamelCase     , ).sequences



				def    snake_case     (  self       :						int     , lowercase__       :						Optional[Any]    ):
						__lowercase					:		List[str]        =					self.pre_processor.batch_decode(__lowerCamelCase    )[0]
						__lowercase					:		Tuple        =					sequence.replace(self.pre_processor.tokenizer.eos_token     , ""    )
						__lowercase					:		List[Any]        =					sequence.replace(self.pre_processor.tokenizer.pad_token     , ""    )
						__lowercase					:		List[Any]        =					re.sub(r"<.*?>"     , ""     , __lowerCamelCase     , count=1    ).strip()  # remove first task start token
						__lowercase					:		int        =					self.pre_processor.tokenajson(__lowerCamelCase    )

						return sequence["answer"]







import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_   :      str			=   logging.get_logger(__name__)



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Tuple:
		__lowercase											=     MobileNetVaConfig(layer_norm_eps=0.001							)

		if "_quant" in model_name:
				raise ValueError('Quantized models are not supported.'							)

		__lowercase											=     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'						,   snake_case							)
		if matches:
				__lowercase											=     float(matches[1]							)
				__lowercase											=     int(matches[2]							)

		# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
		# the usual 1000. The first class (index 0) is "background".
		__lowercase											=     1_001
		__lowercase											=     'imagenet-1k-id2label.json'
		__lowercase											=     'huggingface/label-files'
		__lowercase											=     json.load(open(hf_hub_download(snake_case						,   snake_case						,   repo_type='dataset'							)						,   'r'							)							)
		__lowercase											=     {int(snake_case							) + 1: v for k, v in idalabel.items()}
		__lowercase											=     'background'
		__lowercase											=     idalabel
		__lowercase											=     {v: k for k, v in idalabel.items()}

		return config



def  SCREAMING_SNAKE_CASE				(      )		->    List[Any]:
		__lowercase											=     'http://images.cocodataset.org/val2017/000000039769.jpg'
		__lowercase											=     Image.open(requests.get(snake_case						,   stream=snake_case							).raw							)
		return im



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     get_mobilenet_va_config(snake_case							)

		# Load 🤗 model
		__lowercase											=     MobileNetVaForImageClassification(snake_case							).eval()

		# Load weights from TensorFlow checkpoint
		load_tf_weights_in_mobilenet_va(snake_case						,   snake_case						,   snake_case							)

		# Check outputs on an image, prepared by MobileNetV1ImageProcessor
		__lowercase											=     MobileNetVaImageProcessor(
		    crop_size={'width': config.image_size, 'height': config.image_size}						,   size={'shortest_edge': config.image_size + 32}						,   )
		__lowercase											=     image_processor(images=prepare_img()						,   return_tensors='pt'							)
		__lowercase											=     model(**snake_case							)
		__lowercase											=     outputs.logits

		assert logits.shape == (1, 1_001)

		if model_name == "mobilenet_v1_1.0_224":
				__lowercase											=     torch.tensor([-4.1739, -1.1233, 3.1205]							)
		elif model_name == "mobilenet_v1_0.75_192":
				__lowercase											=     torch.tensor([-3.9440, -2.3141, -0.3333]							)
		else:
				__lowercase											=     None

		if expected_logits is not None:
				assert torch.allclose(logits[0, :3]						,   snake_case						,   atol=1E-4							)

		Path(snake_case							).mkdir(exist_ok=snake_case							)
		print(F"Saving model {model_name} 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:
				print('Pushing to the hub...'							)
				__lowercase											=     'google/' + model_name
				image_processor.push_to_hub(snake_case							)
				model.push_to_hub(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''mobilenet_v1_1.0_224''',
		    type=str,
		    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, 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 to the 🤗 hub.'''
		)

		SCREAMING_SNAKE_CASE_   :      int			=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)




