
'''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()
_UpperCamelCase											=			logging.get_logger(__name__)


def   lowercase_	(   lowerCAmelCase__	:  Optional[Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Any										=     MobileNetVaConfig(layer_norm_eps=0.001 )

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

			__UpperCAmelCase   :      Any										=     re.match(r"""^.*mobilenet_v2_([^_]*)_([^_]*)$"""			,							_UpperCamelCase )
			if matches:
						__UpperCAmelCase   :      str										=     float(matches[1] )
						__UpperCAmelCase   :      Optional[int]										=     int(matches[2] )

			if model_name.startswith("""deeplabv3_""" ):
						__UpperCAmelCase   :      Any										=     8
						__UpperCAmelCase   :      Any										=     21
						__UpperCAmelCase   :      Dict										=     """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   :      str										=     1001
						__UpperCAmelCase   :      Dict										=     """imagenet-1k-id2label.json"""

			__UpperCAmelCase   :      List[Any]										=     """huggingface/label-files"""
			__UpperCAmelCase   :      str										=     json.load(open(hf_hub_download(_UpperCamelCase			,							_UpperCamelCase			,							repo_type="""dataset""" )			,							"""r""" ) )

			if config.num_labels == 1001:
						__UpperCAmelCase   :      List[Any]										=     {int(_UpperCamelCase ) + 1: v for k, v in idalabel.items()}
						__UpperCAmelCase   :      Optional[int]										=     """background"""
			else:
						__UpperCAmelCase   :      int										=     {int(_UpperCamelCase ): v for k, v in idalabel.items()}

			__UpperCAmelCase   :      Any										=     idalabel
			__UpperCAmelCase   :      Optional[int]										=     {v: k for k, v in idalabel.items()}

			return config


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      Optional[Any]										=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__UpperCAmelCase   :      Optional[int]										=     Image.open(requests.get(_UpperCamelCase			,							stream=_UpperCamelCase ).raw )
			return im


@torch.no_grad()
def   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  Optional[int]=False ):

			"""simple docstring"""





			__UpperCAmelCase   :      Tuple										=     get_mobilenet_va_config(_UpperCamelCase )

			# Load 🤗 model
			if model_name.startswith("""deeplabv3_""" ):
						__UpperCAmelCase   :      Union[str, Any]										=     MobileNetVaForSemanticSegmentation(_UpperCamelCase ).eval()
			else:
						__UpperCAmelCase   :      Optional[int]										=     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   :      List[str]										=     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}			,							size={"""shortest_edge""": config.image_size + 32}			,							)
			__UpperCAmelCase   :      int										=     image_processor(images=prepare_img()			,							return_tensors="""pt""" )
			__UpperCAmelCase   :      Any										=     model(**_UpperCamelCase )
			__UpperCAmelCase   :      Tuple										=     outputs.logits

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

						if model_name == "deeplabv3_mobilenet_v2_1.0_513":
									__UpperCAmelCase   :      Dict										=     torch.tensor(
									    [
									        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
									        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
									        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
									    ] )

						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   :      int										=     torch.tensor([0.0_181, -1.0_015, 0.4_688] )
						elif model_name == "mobilenet_v2_1.0_224":
									__UpperCAmelCase   :      Optional[Any]										=     torch.tensor([0.2_445, -1.1_993, 0.1_905] )
						elif model_name == "mobilenet_v2_0.75_160":
									__UpperCAmelCase   :      str										=     torch.tensor([0.2_482, 0.4_136, 0.6_669] )
						elif model_name == "mobilenet_v2_0.35_96":
									__UpperCAmelCase   :      Optional[Any]										=     torch.tensor([0.1_451, -0.4_624, 0.7_192] )
						else:
									__UpperCAmelCase   :      Dict										=     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   :      Optional[int]										=     """google/""" + model_name
						image_processor.push_to_hub(_UpperCamelCase )
						model.push_to_hub(_UpperCamelCase )


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

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









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}



class _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     vocab_size
					__UpperCAmelCase   :      Optional[int]										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     hidden_act
					__UpperCAmelCase   :      List[Any]										=     intermediate_size
					__UpperCAmelCase   :      Optional[Any]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Dict										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      int										=     max_position_embeddings
					__UpperCAmelCase   :      str										=     type_vocab_size
					__UpperCAmelCase   :      int										=     task_type_vocab_size
					__UpperCAmelCase   :      List[str]										=     use_task_id
					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      List[str]										=     position_embedding_type
					__UpperCAmelCase   :      Union[str, Any]										=     use_cache
					__UpperCAmelCase   :      Optional[int]										=     classifier_dropout



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase   :      Union[str, Any]										=     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					        ("""task_type_ids""", dynamic_axis),
					    ]				)




