
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(
												    [
												        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
												        [[-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
							)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




