
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()
__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     logging.get_logger(__name__)
def     UpperCAmelCase__							(       __magic_name__						:     Optional[int]  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[str]  	=     MobileNetVaConfig(layer_norm_eps=0.001  )

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

					lowerCAmelCase		:					int  	=     re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''							,  __magic_name__  )
					if matches:
										lowerCAmelCase		:					int  	=     float(matches[1]  )
										lowerCAmelCase		:					List[Any]  	=     int(matches[2]  )

					if model_name.startswith('''deeplabv3_'''  ):
										lowerCAmelCase		:					Dict  	=     8
										lowerCAmelCase		:					Optional[Any]  	=     21
										lowerCAmelCase		:					List[str]  	=     '''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".
										lowerCAmelCase		:					Optional[int]  	=     10_01
										lowerCAmelCase		:					int  	=     '''imagenet-1k-id2label.json'''

					lowerCAmelCase		:					Tuple  	=     '''huggingface/label-files'''
					lowerCAmelCase		:					Tuple  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )

					if config.num_labels == 10_01:
										lowerCAmelCase		:					Tuple  	=     {int(__magic_name__  ) + 1: v for k, v in idalabel.items()}
										lowerCAmelCase		:					List[str]  	=     '''background'''
					else:
										lowerCAmelCase		:					Union[str, Any]  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}

					lowerCAmelCase		:					Any  	=     idalabel
					lowerCAmelCase		:					Dict  	=     {v: k for k, v in idalabel.items()}

					return config
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCAmelCase		:					int  	=     Image.open(requests.get(__magic_name__							,  stream=__magic_name__  ).raw  )
					return im
@torch.no_grad()
def     UpperCAmelCase__							(       __magic_name__						:     Optional[Any]							,  __magic_name__						:     Optional[int]							,  __magic_name__						:     List[str]							,  __magic_name__						:     str=False  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     get_mobilenet_va_config(__magic_name__  )

					# Load 🤗 model
					if model_name.startswith('''deeplabv3_'''  ):
										lowerCAmelCase		:					List[str]  	=     MobileNetVaForSemanticSegmentation(__magic_name__  ).eval()
					else:
										lowerCAmelCase		:					Optional[Any]  	=     MobileNetVaForImageClassification(__magic_name__  ).eval()

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

					# Check outputs on an image, prepared by MobileNetV2ImageProcessor
					lowerCAmelCase		:					Tuple  	=     MobileNetVaImageProcessor(
					    crop_size={'''width''': config.image_size, '''height''': config.image_size}							,  size={'''shortest_edge''': config.image_size + 32}							,  )
					lowerCAmelCase		:					Tuple  	=     image_processor(images=prepare_img()							,  return_tensors='''pt'''  )
					lowerCAmelCase		:					str  	=     model(**__magic_name__  )
					lowerCAmelCase		:					Optional[Any]  	=     outputs.logits

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

										if model_name == "deeplabv3_mobilenet_v2_1.0_513":
															lowerCAmelCase		:					List[str]  	=     torch.tensor(
															    [
															        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
															        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
															        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
															    ]  )

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

										assert torch.allclose(logits[0, :3, :3, :3]							,  __magic_name__							,  atol=1e-4  )
					else:
										assert logits.shape == (1, 10_01)

										if model_name == "mobilenet_v2_1.4_224":
															lowerCAmelCase		:					str  	=     torch.tensor([0.0181, -1.0015, 0.4688]  )
										elif model_name == "mobilenet_v2_1.0_224":
															lowerCAmelCase		:					Any  	=     torch.tensor([0.2445, -1.1993, 0.1905]  )
										elif model_name == "mobilenet_v2_0.75_160":
															lowerCAmelCase		:					str  	=     torch.tensor([0.2482, 0.4136, 0.6669]  )
										elif model_name == "mobilenet_v2_0.35_96":
															lowerCAmelCase		:					Optional[int]  	=     torch.tensor([0.1451, -0.4624, 0.7192]  )
										else:
															lowerCAmelCase		:					Dict  	=     None

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

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

					if push_to_hub:
										print('''Pushing to the hub...'''  )
										lowerCAmelCase		:					str  	=     '''google/''' + model_name
										image_processor.push_to_hub(__magic_name__  )
										model.push_to_hub(__magic_name__  )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     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.'
		)

		__SCREAMING_SNAKE_CASE			:   str         =     parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__SCREAMING_SNAKE_CASE			:   List[Any]         =     10_00_00_00_00_00
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     1_00_00

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Any         =     os.path.split(__file__)
__SCREAMING_SNAKE_CASE			:   List[str]         =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     pa.Table.from_pydict({'''col''': [0] * chunk_size}  )
					lowerCAmelCase		:					int  	=     pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__magic_name__							,  fingerprint='''table_100B'''  )


@dataclass
class 		__magic_name__   :
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Union[str, Any]  	=     np.random.default_rng(self.seed				)
							lowerCAmelCase		:					Tuple  	=     rng.integers(low=self.low						,  high=self.high						,  size=self.size				).tolist()





		def __iter__(							self    :	Dict				):
							return iter(self._sampled_values				)





		def __len__(							self    :	Tuple				):
							return self.size



@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     dataset[0]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     dataset[-1]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     dataset[range(len(__magic_name__  ) // 2							,  len(__magic_name__  ) // 2 + 10_24  )]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     dataset[RandIter(0							,  len(__magic_name__  )							,  10_24							,  seed=42  )]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
					lowerCAmelCase		:					Tuple  	=     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('''generating dataset'''  )
					lowerCAmelCase		:					Dict  	=     generate_100B_dataset(num_examples=__magic_name__							,  chunk_size=__magic_name__  )
					print('''Functions'''  )
					for func in functions:
										print(func.__name__  )
										lowerCAmelCase		:					List[Any]  	=     func(__magic_name__  )

					with open(__magic_name__							,  '''wb'''  ) as f:
										f.write(json.dumps(__magic_name__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()
