





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()
_lowerCamelCase							=     logging.get_logger(__name__)






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[Any]      ):
		SCREAMING_SNAKE_CASE__								= MobileNetVaConfig(layer_norm_eps=0.0_0_1      )

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

		SCREAMING_SNAKE_CASE__								= re.match(R"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""   ,				UpperCamelCase__      )
		if matches:
				SCREAMING_SNAKE_CASE__								= float(matches[1]      )
				SCREAMING_SNAKE_CASE__								= int(matches[2]      )

		if model_name.startswith("""deeplabv3_"""      ):
				SCREAMING_SNAKE_CASE__								= 8
				SCREAMING_SNAKE_CASE__								= 21
				SCREAMING_SNAKE_CASE__								= """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".
				SCREAMING_SNAKE_CASE__								= 1_001
				SCREAMING_SNAKE_CASE__								= """imagenet-1k-id2label.json"""

		SCREAMING_SNAKE_CASE__								= """huggingface/label-files"""
		SCREAMING_SNAKE_CASE__								= json.load(open(hf_hub_download(UpperCamelCase__   ,				UpperCamelCase__   ,				repo_type="""dataset"""      )   ,				"""r"""      )      )

		if config.num_labels == 1_001:
				SCREAMING_SNAKE_CASE__								= {int(UpperCamelCase__      ) + 1: v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE__								= """background"""
		else:
				SCREAMING_SNAKE_CASE__								= {int(UpperCamelCase__      ): v for k, v in idalabel.items()}

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

		return config






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






@torch.no_grad()
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							str   ,				UpperCamelCase__:							str=False      ):
		SCREAMING_SNAKE_CASE__								= get_mobilenet_va_config(UpperCamelCase__      )

		# Load 🤗 model
		if model_name.startswith("""deeplabv3_"""      ):
				SCREAMING_SNAKE_CASE__								= MobileNetVaForSemanticSegmentation(UpperCamelCase__      ).eval()
		else:
				SCREAMING_SNAKE_CASE__								= MobileNetVaForImageClassification(UpperCamelCase__      ).eval()

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

		# Check outputs on an image, prepared by MobileNetV2ImageProcessor
		SCREAMING_SNAKE_CASE__								= MobileNetVaImageProcessor(
		    crop_size={"""width""": config.image_size, """height""": config.image_size}   ,				size={"""shortest_edge""": config.image_size + 32}   ,				)
		SCREAMING_SNAKE_CASE__								= image_processor(images=prepare_img()   ,				return_tensors="""pt"""      )
		SCREAMING_SNAKE_CASE__								= model(**UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= outputs.logits

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

				if model_name == "deeplabv3_mobilenet_v2_1.0_513":
						SCREAMING_SNAKE_CASE__								= 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]   ,				UpperCamelCase__   ,				atol=1e-4      )
		else:
				assert logits.shape == (1, 1_001)

				if model_name == "mobilenet_v2_1.4_224":
						SCREAMING_SNAKE_CASE__								= 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":
						SCREAMING_SNAKE_CASE__								= 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":
						SCREAMING_SNAKE_CASE__								= 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":
						SCREAMING_SNAKE_CASE__								= torch.tensor([0.1_4_5_1, -0.4_6_2_4, 0.7_1_9_2]      )
				else:
						SCREAMING_SNAKE_CASE__								= None

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

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

		if push_to_hub:
				print("""Pushing to the hub..."""      )
				SCREAMING_SNAKE_CASE__								= """google/""" + model_name
				image_processor.push_to_hub(UpperCamelCase__      )
				model.push_to_hub(UpperCamelCase__      )


if __name__ == "__main__":
	_lowerCamelCase							=     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.'
	)

	_lowerCamelCase							=     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_flax_available, is_tf_available, is_torch_available


_lowerCamelCase							=     {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase							=     [
	    'Constraint',
	    'ConstraintListState',
	    'DisjunctiveConstraint',
	    'PhrasalConstraint',
	]
	_lowerCamelCase							=     [
	    'BeamHypotheses',
	    'BeamScorer',
	    'BeamSearchScorer',
	    'ConstrainedBeamSearchScorer',
	]
	_lowerCamelCase							=     [
	    'EpsilonLogitsWarper',
	    'EtaLogitsWarper',
	    'ForcedBOSTokenLogitsProcessor',
	    'ForcedEOSTokenLogitsProcessor',
	    'HammingDiversityLogitsProcessor',
	    'InfNanRemoveLogitsProcessor',
	    'LogitsProcessor',
	    'LogitsProcessorList',
	    'LogitsWarper',
	    'MinLengthLogitsProcessor',
	    'MinNewTokensLengthLogitsProcessor',
	    'NoBadWordsLogitsProcessor',
	    'NoRepeatNGramLogitsProcessor',
	    'PrefixConstrainedLogitsProcessor',
	    'RepetitionPenaltyLogitsProcessor',
	    'SequenceBiasLogitsProcessor',
	    'EncoderRepetitionPenaltyLogitsProcessor',
	    'TemperatureLogitsWarper',
	    'TopKLogitsWarper',
	    'TopPLogitsWarper',
	    'TypicalLogitsWarper',
	    'EncoderNoRepeatNGramLogitsProcessor',
	    'ExponentialDecayLengthPenalty',
	    'LogitNormalization',
	]
	_lowerCamelCase							=     [
	    'MaxNewTokensCriteria',
	    'MaxLengthCriteria',
	    'MaxTimeCriteria',
	    'StoppingCriteria',
	    'StoppingCriteriaList',
	    'validate_stopping_criteria',
	]
	_lowerCamelCase							=     [
	    'GenerationMixin',
	    'top_k_top_p_filtering',
	    'GreedySearchEncoderDecoderOutput',
	    'GreedySearchDecoderOnlyOutput',
	    'SampleEncoderDecoderOutput',
	    'SampleDecoderOnlyOutput',
	    'BeamSearchEncoderDecoderOutput',
	    'BeamSearchDecoderOnlyOutput',
	    'BeamSampleEncoderDecoderOutput',
	    'BeamSampleDecoderOnlyOutput',
	    'ContrastiveSearchEncoderDecoderOutput',
	    'ContrastiveSearchDecoderOnlyOutput',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase							=     [
	    'TFForcedBOSTokenLogitsProcessor',
	    'TFForcedEOSTokenLogitsProcessor',
	    'TFLogitsProcessor',
	    'TFLogitsProcessorList',
	    'TFLogitsWarper',
	    'TFMinLengthLogitsProcessor',
	    'TFNoBadWordsLogitsProcessor',
	    'TFNoRepeatNGramLogitsProcessor',
	    'TFRepetitionPenaltyLogitsProcessor',
	    'TFTemperatureLogitsWarper',
	    'TFTopKLogitsWarper',
	    'TFTopPLogitsWarper',
	    'TFForceTokensLogitsProcessor',
	    'TFSuppressTokensAtBeginLogitsProcessor',
	    'TFSuppressTokensLogitsProcessor',
	]
	_lowerCamelCase							=     [
	    'TFGenerationMixin',
	    'tf_top_k_top_p_filtering',
	    'TFGreedySearchDecoderOnlyOutput',
	    'TFGreedySearchEncoderDecoderOutput',
	    'TFSampleEncoderDecoderOutput',
	    'TFSampleDecoderOnlyOutput',
	    'TFBeamSearchEncoderDecoderOutput',
	    'TFBeamSearchDecoderOnlyOutput',
	    'TFBeamSampleEncoderDecoderOutput',
	    'TFBeamSampleDecoderOnlyOutput',
	    'TFContrastiveSearchEncoderDecoderOutput',
	    'TFContrastiveSearchDecoderOnlyOutput',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase							=     [
	    'FlaxForcedBOSTokenLogitsProcessor',
	    'FlaxForcedEOSTokenLogitsProcessor',
	    'FlaxLogitsProcessor',
	    'FlaxLogitsProcessorList',
	    'FlaxLogitsWarper',
	    'FlaxMinLengthLogitsProcessor',
	    'FlaxTemperatureLogitsWarper',
	    'FlaxTopKLogitsWarper',
	    'FlaxTopPLogitsWarper',
	]
	_lowerCamelCase							=     [
	    'FlaxGenerationMixin',
	    'FlaxGreedySearchOutput',
	    'FlaxSampleOutput',
	    'FlaxBeamSearchOutput',
	]

if TYPE_CHECKING:
	from .configuration_utils import GenerationConfig
	from .streamers import TextIteratorStreamer, TextStreamer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
		from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
		from .logits_process import (
		    EncoderNoRepeatNGramLogitsProcessor,
		    EncoderRepetitionPenaltyLogitsProcessor,
		    EpsilonLogitsWarper,
		    EtaLogitsWarper,
		    ExponentialDecayLengthPenalty,
		    ForcedBOSTokenLogitsProcessor,
		    ForcedEOSTokenLogitsProcessor,
		    HammingDiversityLogitsProcessor,
		    InfNanRemoveLogitsProcessor,
		    LogitNormalization,
		    LogitsProcessor,
		    LogitsProcessorList,
		    LogitsWarper,
		    MinLengthLogitsProcessor,
		    MinNewTokensLengthLogitsProcessor,
		    NoBadWordsLogitsProcessor,
		    NoRepeatNGramLogitsProcessor,
		    PrefixConstrainedLogitsProcessor,
		    RepetitionPenaltyLogitsProcessor,
		    SequenceBiasLogitsProcessor,
		    TemperatureLogitsWarper,
		    TopKLogitsWarper,
		    TopPLogitsWarper,
		    TypicalLogitsWarper,
		)
		from .stopping_criteria import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)
		from .utils import (
		    BeamSampleDecoderOnlyOutput,
		    BeamSampleEncoderDecoderOutput,
		    BeamSearchDecoderOnlyOutput,
		    BeamSearchEncoderDecoderOutput,
		    ContrastiveSearchDecoderOnlyOutput,
		    ContrastiveSearchEncoderDecoderOutput,
		    GenerationMixin,
		    GreedySearchDecoderOnlyOutput,
		    GreedySearchEncoderDecoderOutput,
		    SampleDecoderOnlyOutput,
		    SampleEncoderDecoderOutput,
		    top_k_top_p_filtering,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tf_logits_process import (
		    TFForcedBOSTokenLogitsProcessor,
		    TFForcedEOSTokenLogitsProcessor,
		    TFForceTokensLogitsProcessor,
		    TFLogitsProcessor,
		    TFLogitsProcessorList,
		    TFLogitsWarper,
		    TFMinLengthLogitsProcessor,
		    TFNoBadWordsLogitsProcessor,
		    TFNoRepeatNGramLogitsProcessor,
		    TFRepetitionPenaltyLogitsProcessor,
		    TFSuppressTokensAtBeginLogitsProcessor,
		    TFSuppressTokensLogitsProcessor,
		    TFTemperatureLogitsWarper,
		    TFTopKLogitsWarper,
		    TFTopPLogitsWarper,
		)
		from .tf_utils import (
		    TFBeamSampleDecoderOnlyOutput,
		    TFBeamSampleEncoderDecoderOutput,
		    TFBeamSearchDecoderOnlyOutput,
		    TFBeamSearchEncoderDecoderOutput,
		    TFContrastiveSearchDecoderOnlyOutput,
		    TFContrastiveSearchEncoderDecoderOutput,
		    TFGenerationMixin,
		    TFGreedySearchDecoderOnlyOutput,
		    TFGreedySearchEncoderDecoderOutput,
		    TFSampleDecoderOnlyOutput,
		    TFSampleEncoderDecoderOutput,
		    tf_top_k_top_p_filtering,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .flax_logits_process import (
		    FlaxForcedBOSTokenLogitsProcessor,
		    FlaxForcedEOSTokenLogitsProcessor,
		    FlaxLogitsProcessor,
		    FlaxLogitsProcessorList,
		    FlaxLogitsWarper,
		    FlaxMinLengthLogitsProcessor,
		    FlaxTemperatureLogitsWarper,
		    FlaxTopKLogitsWarper,
		    FlaxTopPLogitsWarper,
		)
		from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
	import sys

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