

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        _lowercase  (					a_					:       Optional[int]					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     MobileNetVaConfig(layer_norm_eps=0.001					)

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

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

					if model_name.startswith('deeplabv3_'					):
										__magic_name__				=     8
										__magic_name__				=     2_1
										__magic_name__				=     '''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".
										__magic_name__				=     1_0_0_1
										__magic_name__				=     '''imagenet-1k-id2label.json'''

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

					if config.num_labels == 1_0_0_1:
										__magic_name__				=     {int(a__					) + 1: v for k, v in idalabel.items()}
										__magic_name__				=     '''background'''
					else:
										__magic_name__				=     {int(a__					): v for k, v in idalabel.items()}

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

					return config




def        _lowercase  (					)					->						Any:




					'''simple docstring'''







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




@torch.no_grad()
def        _lowercase  (					a_					:       str       ,a_					:       Any       ,a_					:       int       ,a_					:       Optional[int]=False					)					->						Any:




					'''simple docstring'''







					__magic_name__				=     get_mobilenet_va_config(a__					)

					# Load 🤗 model
					if model_name.startswith('deeplabv3_'					):
										__magic_name__				=     MobileNetVaForSemanticSegmentation(a__					).eval()
					else:
										__magic_name__				=     MobileNetVaForImageClassification(a__					).eval()

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

					# Check outputs on an image, prepared by MobileNetV2ImageProcessor
					__magic_name__				=     MobileNetVaImageProcessor(
					    crop_size={'width': config.image_size, 'height': config.image_size}       ,size={'shortest_edge': config.image_size + 3_2}       ,)
					__magic_name__				=     image_processor(images=prepare_img()       ,return_tensors='pt'					)
					__magic_name__				=     model(**a__					)
					__magic_name__				=     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":
															__magic_name__				=     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]       ,a__       ,atol=1e-4					)
					else:
										assert logits.shape == (1, 1_0_0_1)

										if model_name == "mobilenet_v2_1.4_224":
															__magic_name__				=     torch.tensor([0.0181, -1.0015, 0.4688]					)
										elif model_name == "mobilenet_v2_1.0_224":
															__magic_name__				=     torch.tensor([0.2445, -1.1993, 0.1905]					)
										elif model_name == "mobilenet_v2_0.75_160":
															__magic_name__				=     torch.tensor([0.2482, 0.4136, 0.6669]					)
										elif model_name == "mobilenet_v2_0.35_96":
															__magic_name__				=     torch.tensor([0.1451, -0.4624, 0.7192]					)
										else:
															__magic_name__				=     None

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

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

					if push_to_hub:
										print('Pushing to the hub...'					)
										__magic_name__				=     '''google/''' + model_name
										image_processor.push_to_hub(a__					)
										model.push_to_hub(a__					)


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_torch_available


a__				:      List[Any]    		=     {
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__				:      List[str]    		=     [
          '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DecisionTransformerGPT2Model''',
          '''DecisionTransformerGPT2PreTrainedModel''',
          '''DecisionTransformerModel''',
          '''DecisionTransformerPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DecisionTransformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_decision_transformer import (
                DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DecisionTransformerGPTaModel,
                DecisionTransformerGPTaPreTrainedModel,
                DecisionTransformerModel,
                DecisionTransformerPreTrainedModel,
            )


else:
      import sys

      a__				:      str    		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
