


"""simple docstring"""







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__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    MobileNetVaConfig(layer_norm_eps=0.001 )

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

						UpperCAmelCase__		=    re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''	,			_UpperCamelCase )
						if matches:
												UpperCAmelCase__		=    float(matches[1] )
												UpperCAmelCase__		=    int(matches[2] )

						if model_name.startswith('''deeplabv3_''' ):
												UpperCAmelCase__		=    8
												UpperCAmelCase__		=    21
												UpperCAmelCase__		=    '''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".
												UpperCAmelCase__		=    1001
												UpperCAmelCase__		=    '''imagenet-1k-id2label.json'''

						UpperCAmelCase__		=    '''huggingface/label-files'''
						UpperCAmelCase__		=    json.load(open(hf_hub_download(_UpperCamelCase	,			_UpperCamelCase	,			repo_type='''dataset''' )	,			'''r''' ) )

						if config.num_labels == 1001:
												UpperCAmelCase__		=    {int(_UpperCamelCase ) + 1: v for k, v in idalabel.items()}
												UpperCAmelCase__		=    '''background'''
						else:
												UpperCAmelCase__		=    {int(_UpperCamelCase ): v for k, v in idalabel.items()}

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

						return config






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase__		=    Image.open(requests.get(_UpperCamelCase	,			stream=_UpperCamelCase ).raw )
						return im






@torch.no_grad()
def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=False ):





						"""simple docstring"""

						UpperCAmelCase__		=    get_mobilenet_va_config(_UpperCamelCase )

						# Load 🤗 model
						if model_name.startswith('''deeplabv3_''' ):
												UpperCAmelCase__		=    MobileNetVaForSemanticSegmentation(_UpperCamelCase ).eval()
						else:
												UpperCAmelCase__		=    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
						UpperCAmelCase__		=    MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size}	,			size={'''shortest_edge''': config.image_size + 32}	,			)
						UpperCAmelCase__		=    image_processor(images=prepare_img()	,			return_tensors='''pt''' )
						UpperCAmelCase__		=    model(**_UpperCamelCase )
						UpperCAmelCase__		=    outputs.logits

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

												if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																		UpperCAmelCase__		=    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]	,			_UpperCamelCase	,			atol=1E-4 )
						else:
												assert logits.shape == (1, 1001)

												if model_name == "mobilenet_v2_1.4_224":
																		UpperCAmelCase__		=    torch.tensor([0.0181, -1.0015, 0.4688] )
												elif model_name == "mobilenet_v2_1.0_224":
																		UpperCAmelCase__		=    torch.tensor([0.2445, -1.1993, 0.1905] )
												elif model_name == "mobilenet_v2_0.75_160":
																		UpperCAmelCase__		=    torch.tensor([0.2482, 0.4136, 0.6669] )
												elif model_name == "mobilenet_v2_0.35_96":
																		UpperCAmelCase__		=    torch.tensor([0.1451, -0.4624, 0.7192] )
												else:
																		UpperCAmelCase__		=    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...''' )
												UpperCAmelCase__		=    '''google/''' + model_name
												image_processor.push_to_hub(_UpperCamelCase )
												model.push_to_hub(_UpperCamelCase )


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
						)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a			 =   {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   ["CamembertTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "CamembertForCausalLM",
						    "CamembertForMaskedLM",
						    "CamembertForMultipleChoice",
						    "CamembertForQuestionAnswering",
						    "CamembertForSequenceClassification",
						    "CamembertForTokenClassification",
						    "CamembertModel",
						    "CamembertPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a			 =   [
						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFCamembertForCausalLM",
						    "TFCamembertForMaskedLM",
						    "TFCamembertForMultipleChoice",
						    "TFCamembertForQuestionAnswering",
						    "TFCamembertForSequenceClassification",
						    "TFCamembertForTokenClassification",
						    "TFCamembertModel",
						    "TFCamembertPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

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