
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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a        = logging.get_logger(__name__)




def     a						(							snake_case__: str ):


				'''simple docstring'''
				lowercase_					  = MobileNetVaConfig(layer_norm_eps=0.0_0_1 )

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

				lowercase_					  = re.match(r'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''						,       snake_case__ )
				if matches:
								lowercase_					  = float(matches[1] )
								lowercase_					  = int(matches[2] )

				if model_name.startswith('''deeplabv3_''' ):
								lowercase_					  = 8
								lowercase_					  = 21
								lowercase_					  = '''pascal-voc-id2label.json'''
				else:
								# The TensorFlow version of MobileNetV2 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''' ) )

				if config.num_labels == 1_001:
								lowercase_					  = {int(snake_case__ ) + 1: v for k, v in idalabel.items()}
								lowercase_					  = '''background'''
				else:
								lowercase_					  = {int(snake_case__ ): v for k, v in idalabel.items()}

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

				return config




def     a						(							):


				'''simple docstring'''
				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     a						(							snake_case__: int						,       snake_case__: List[str]						,       snake_case__: Any						,       snake_case__: Optional[Any]=False ):


				'''simple docstring'''
				lowercase_					  = get_mobilenet_va_config(snake_case__ )

				# Load 🤗 model
				if model_name.startswith('''deeplabv3_''' ):
								lowercase_					  = MobileNetVaForSemanticSegmentation(snake_case__ ).eval()
				else:
								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 MobileNetV2ImageProcessor
				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

				if model_name.startswith('''deeplabv3_''' ):
								assert logits.shape == (1, 21, 65, 65)

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												lowercase_					  = torch.tensor(
												    [
												        [[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
												        [[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
												        [[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
												    ] )

								else:
												raise ValueError(F'''Unknown model name: {model_name}''' )

								assert torch.allclose(logits[0, :3, :3, :3]						,       snake_case__						,       atol=1e-4 )
				else:
								assert logits.shape == (1, 1_001)

								if model_name == "mobilenet_v2_1.4_224":
												lowercase_					  = torch.tensor([0.0_1_8_1, -1.0_0_1_5, 0.4_6_8_8] )
								elif model_name == "mobilenet_v2_1.0_224":
												lowercase_					  = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] )
								elif model_name == "mobilenet_v2_0.75_160":
												lowercase_					  = torch.tensor([0.2_4_8_2, 0.4_1_3_6, 0.6_6_6_9] )
								elif model_name == "mobilenet_v2_0.35_96":
												lowercase_					  = torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2] )
								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__":
						__a        = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='mobilenet_v2_1.0_224',
						    type=str,
						    help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<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.'
						)

						__a        = parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :Any   					=     ['image_processor', 'tokenizer']
	a      :Tuple   					=     'LayoutLMv2ImageProcessor'
	a      :List[Any]   					=     ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
									lowercase_					  = kwargs.pop('''feature_extractor'''	)

					lowercase_					  = image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
									raise ValueError('''You need to specify an `image_processor`.'''	)
					if tokenizer is None:
									raise ValueError('''You need to specify a `tokenizer`.'''	)

					super().__init__(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
	def __call__(	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,							SCREAMING_SNAKE_CASE_  :      Union[List[List[int]], List[List[List[int]]]] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[List[int], List[List[int]]]] = None	,							SCREAMING_SNAKE_CASE_  :      bool = True	,							SCREAMING_SNAKE_CASE_  :      Union[bool, str, PaddingStrategy] = False	,							SCREAMING_SNAKE_CASE_  :      Union[bool, str, TruncationStrategy] = False	,							SCREAMING_SNAKE_CASE_  :      Optional[int] = None	,							SCREAMING_SNAKE_CASE_  :      int = 0	,							SCREAMING_SNAKE_CASE_  :      Optional[int] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[bool] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[bool] = None	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = True	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[str, TensorType]] = None	,							**SCREAMING_SNAKE_CASE_  :      List[Any]	,							)	->					BatchEncoding:
					# verify input
					if self.image_processor.apply_ocr and (boxes is not None):
									raise ValueError(
									    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''	)

					if self.image_processor.apply_ocr and (word_labels is not None):
									raise ValueError(
									    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''	)

					if return_overflowing_tokens is True and return_offsets_mapping is False:
									raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''	)

					# first, apply the image processor
					lowercase_					  = self.image_processor(images=SCREAMING_SNAKE_CASE_	,							return_tensors=SCREAMING_SNAKE_CASE_	)

					# second, apply the tokenizer
					if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	):
													lowercase_					  = [text]  # add batch dimension (as the image processor always adds a batch dimension)
									lowercase_					  = features['''words''']

					lowercase_					  = self.tokenizer(
					    text=text if text is not None else features['''words''']	,							text_pair=text_pair if text_pair is not None else None	,							boxes=boxes if boxes is not None else features['''boxes''']	,							word_labels=SCREAMING_SNAKE_CASE_	,							add_special_tokens=SCREAMING_SNAKE_CASE_	,							padding=SCREAMING_SNAKE_CASE_	,							truncation=SCREAMING_SNAKE_CASE_	,							max_length=SCREAMING_SNAKE_CASE_	,							stride=SCREAMING_SNAKE_CASE_	,							pad_to_multiple_of=SCREAMING_SNAKE_CASE_	,							return_token_type_ids=SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							return_overflowing_tokens=SCREAMING_SNAKE_CASE_	,							return_special_tokens_mask=SCREAMING_SNAKE_CASE_	,							return_offsets_mapping=SCREAMING_SNAKE_CASE_	,							return_length=SCREAMING_SNAKE_CASE_	,							verbose=SCREAMING_SNAKE_CASE_	,							return_tensors=SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	,							)

					# add pixel values
					lowercase_					  = features.pop('''pixel_values'''	)
					if return_overflowing_tokens is True:
									lowercase_					  = self.get_overflowing_images(SCREAMING_SNAKE_CASE_	,							encoded_inputs['''overflow_to_sample_mapping''']	)
					lowercase_					  = images

					return encoded_inputs
	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					List[str]:
					# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
					lowercase_					  = []
					for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]	)

					if len(SCREAMING_SNAKE_CASE_	) != len(SCREAMING_SNAKE_CASE_	):
									raise ValueError(
									    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
									    f''' {len(SCREAMING_SNAKE_CASE_	)} and {len(SCREAMING_SNAKE_CASE_	)}'''	)

					return images_with_overflow
	def 						_lowercase  (	self  :      List[Any]	,							*SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							**SCREAMING_SNAKE_CASE_  :      Tuple	)	->					List[str]:
					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[Any]	,							*SCREAMING_SNAKE_CASE_  :      Dict	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					List[Any]:
					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	@property
	def 						_lowercase  (	self  :      List[Any]	)	->					List[Any]:
					return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
	@property
	def 						_lowercase  (	self  :      Dict	)	->					str:
					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
					return self.image_processor_class




	@property
	def 						_lowercase  (	self  :      int	)	->					List[str]:
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
					return self.image_processor




