



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					:				str   	=logging.get_logger(__name__)







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					MobileNetVaConfig(layer_norm_eps=0.001  )

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

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

						if model_name.startswith("""deeplabv3_"""  ):
												lowerCAmelCase	:   str					     =					8
												lowerCAmelCase	:   str					     =					2_1
												lowerCAmelCase	:   int					     =					"""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	:   Dict					     =					1_0_0_1
												lowerCAmelCase	:   List[str]					     =					"""imagenet-1k-id2label.json"""

						lowerCAmelCase	:   int					     =					"""huggingface/label-files"""
						lowerCAmelCase	:   Dict					     =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,repo_type="""dataset"""  )			,"""r"""  )  )

						if config.num_labels == 1_0_0_1:
												lowerCAmelCase	:   List[str]					     =					{int(SCREAMING_SNAKE_CASE__  ) + 1: v for k, v in idalabel.items()}
												lowerCAmelCase	:   Union[str, Any]					     =					"""background"""
						else:
												lowerCAmelCase	:   Dict					     =					{int(SCREAMING_SNAKE_CASE__  ): v for k, v in idalabel.items()}

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

						return config







def  _UpperCAmelCase  (				):


						'''simple docstring'''




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







@torch.no_grad()
def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   int					     =					get_mobilenet_va_config(SCREAMING_SNAKE_CASE__  )

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

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

						# Check outputs on an image, prepared by MobileNetV2ImageProcessor
						lowerCAmelCase	:   Any					     =					MobileNetVaImageProcessor(
						    crop_size={"""width""": config.image_size, """height""": config.image_size}			,size={"""shortest_edge""": config.image_size + 3_2}			,)
						lowerCAmelCase	:   str					     =					image_processor(images=prepare_img()			,return_tensors="""pt"""  )
						lowerCAmelCase	:   Dict					     =					model(**SCREAMING_SNAKE_CASE__  )
						lowerCAmelCase	:   Optional[int]					     =					outputs.logits

						if model_name.startswith("""deeplabv3_"""  ):
												assert logits.shape == (1, 2_1, 6_5, 6_5)

												if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																		lowerCAmelCase	:   int					     =					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]			,SCREAMING_SNAKE_CASE__			,atol=1e-4  )
						else:
												assert logits.shape == (1, 1_0_0_1)

												if model_name == "mobilenet_v2_1.4_224":
																		lowerCAmelCase	:   Dict					     =					torch.tensor([0.0181, -1.0015, 0.4688]  )
												elif model_name == "mobilenet_v2_1.0_224":
																		lowerCAmelCase	:   List[str]					     =					torch.tensor([0.2445, -1.1993, 0.1905]  )
												elif model_name == "mobilenet_v2_0.75_160":
																		lowerCAmelCase	:   Any					     =					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]			,SCREAMING_SNAKE_CASE__			,atol=1e-4  )

						Path(SCREAMING_SNAKE_CASE__  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__  )
						print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

						if push_to_hub:
												print("""Pushing to the hub..."""  )
												lowerCAmelCase	:   Optional[Any]					     =					"""google/""" + model_name
												image_processor.push_to_hub(SCREAMING_SNAKE_CASE__  )
												model.push_to_hub(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
		lowerCAmelCase					:				Union[str, 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.'
		)

		lowerCAmelCase					:				Tuple   	=parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)






import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					GPTNeoConfig(
						    hidden_size=config_json["""n_embd"""]			,num_layers=config_json["""n_layer"""]			,num_heads=config_json["""n_head"""]			,attention_types=config_json["""attention_types"""]			,max_position_embeddings=config_json["""n_positions"""]			,resid_dropout=config_json["""res_dropout"""]			,embed_dropout=config_json["""embed_dropout"""]			,attention_dropout=config_json["""attn_dropout"""]			,)
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   str					     =					GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__  )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
		lowerCAmelCase					:				Optional[Any]   	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowerCAmelCase					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

