





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__			:    Union[str, Any]			      =		logging.get_logger(__name__)

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]							)  ->							Optional[Any]:
     '''simple docstring'''


     A_					      =					MobileNetVaConfig(layer_norm_eps=0.001							)

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

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

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

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

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

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

     return config

def     UpperCAmelCase_						(						)  ->							str:
     '''simple docstring'''


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

@torch.no_grad()
def     UpperCAmelCase_						(						_UpperCAmelCase       :List[Any]     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :Dict     ,	_UpperCAmelCase       :Dict=False							)  ->							List[str]:
     '''simple docstring'''


     A_					      =					get_mobilenet_va_config(_UpperCAmelCase							)

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

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

          if model_name == "deeplabv3_mobilenet_v2_1.0_513":
               A_					      =					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":
               A_					      =					torch.tensor([0.0181, -1.0015, 0.4688]							)
          elif model_name == "mobilenet_v2_1.0_224":
               A_					      =					torch.tensor([0.2445, -1.1993, 0.1905]							)
          elif model_name == "mobilenet_v2_0.75_160":
               A_					      =					torch.tensor([0.2482, 0.4136, 0.6669]							)
          elif model_name == "mobilenet_v2_0.35_96":
               A_					      =					torch.tensor([0.1451, -0.4624, 0.7192]							)
          else:
               A_					      =					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...'''							)
          A_					      =					'''google/''' + model_name
          image_processor.push_to_hub(_UpperCAmelCase							)
          model.push_to_hub(_UpperCAmelCase							)


if __name__ == "__main__":
      a__			:    List[str]			      =		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__			:    Optional[int]			      =		parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )










# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  pass




def     UpperCAmelCase_						(						_UpperCAmelCase       :str     ,	_UpperCAmelCase       :Dict[str, Callable]     ,	_UpperCAmelCase       :Tuple=None     ,	_UpperCAmelCase       :Union[str, Any]=False							)  ->							Optional[int]:
     '''simple docstring'''


     try:
          A_					      =					ast.parse(_UpperCAmelCase							)
     except SyntaxError as e:
          print('''The code generated by the agent is not valid.\n'''     ,	_UpperCAmelCase							)
          return
     if state is None:
          A_					      =					{}
     A_					      =					None
     for idx, node in enumerate(expression.body							):
          try:
               A_					      =					evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
          except InterpretorError as e:
               A_					      =					f'Evaluation of the code stopped at line {idx} before the end because of the following error'
               if chat_mode:
                    msg += (
                        f'. Copy paste the following error message and send it back to the agent:\nI get an error: \'{e}\''
                    )
               else:
                    msg += f':\n{e}'
               print(_UpperCAmelCase							)
               break
          if line_result is not None:
               A_					      =					line_result

     return result

def     UpperCAmelCase_						(						_UpperCAmelCase       :ast.AST     ,	_UpperCAmelCase       :Dict[str, Any]     ,	_UpperCAmelCase       :Dict[str, Callable]							)  ->							Dict:
     '''simple docstring'''


     if isinstance(_UpperCAmelCase     ,	ast.Assign							):
          # Assignement -> we evaluate the assignement which should update the state
          # We return the variable assigned as it may be used to determine the final result.
          return evaluate_assign(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif isinstance(_UpperCAmelCase     ,	ast.Call							):
          # Function call -> we return the value of the function call
          return evaluate_call(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif isinstance(_UpperCAmelCase     ,	ast.Constant							):
          # Constant -> just return the value
          return expression.value
     elif isinstance(_UpperCAmelCase     ,	ast.Dict							):
          # Dict -> evaluate all keys and values
          A_					      =					[evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							) for k in expression.keys]
          A_					      =					[evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							) for v in expression.values]
          return dict(zip(_UpperCAmelCase     ,	_UpperCAmelCase							)							)
     elif isinstance(_UpperCAmelCase     ,	ast.Expr							):
          # Expression -> evaluate the content
          return evaluate_ast(expression.value     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif isinstance(_UpperCAmelCase     ,	ast.For							):
          # For loop -> execute the loop
          return evaluate_for(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif isinstance(_UpperCAmelCase     ,	ast.FormattedValue							):
          # Formatted value (part of f-string) -> evaluate the content and return
          return evaluate_ast(expression.value     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif isinstance(_UpperCAmelCase     ,	ast.If							):
          # If -> execute the right branch
          return evaluate_if(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif hasattr(_UpperCAmelCase     ,	'''Index'''							) and isinstance(_UpperCAmelCase     ,	ast.Index							):
          return evaluate_ast(expression.value     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif isinstance(_UpperCAmelCase     ,	ast.JoinedStr							):
          return "".join([str(evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)							) for v in expression.values]							)
     elif isinstance(_UpperCAmelCase     ,	ast.List							):
          # List -> evaluate all elements
          return [evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							) for elt in expression.elts]
     elif isinstance(_UpperCAmelCase     ,	ast.Name							):
          # Name -> pick up the value in the state
          return evaluate_name(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     elif isinstance(_UpperCAmelCase     ,	ast.Subscript							):
          # Subscript -> return the value of the indexing
          return evaluate_subscript(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     else:
          # For now we refuse anything else. Let's add things as we need them.
          raise InterpretorError(f'{expression.__class__.__name__} is not supported.'							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :str     ,	_UpperCAmelCase       :int     ,	_UpperCAmelCase       :int							)  ->							Any:
     '''simple docstring'''


     A_					      =					assign.targets
     A_					      =					evaluate_ast(assign.value     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)

     if len(_UpperCAmelCase							) == 1:
          A_					      =					result
     else:
          if len(_UpperCAmelCase							) != len(_UpperCAmelCase							):
               raise InterpretorError(f'Expected {len(_UpperCAmelCase							)} values but got {len(_UpperCAmelCase							)}.'							)
          for var_name, r in zip(_UpperCAmelCase     ,	_UpperCAmelCase							):
               A_					      =					r
     return result

def     UpperCAmelCase_						(						_UpperCAmelCase       :Dict     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :int							)  ->							Optional[Any]:
     '''simple docstring'''


     if not isinstance(call.func     ,	ast.Name							):
          raise InterpretorError(
              f'It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of '
              f'type {type(call.func							)}.'							)
     A_					      =					call.func.id
     if func_name not in tools:
          raise InterpretorError(
              f'It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id}).'							)

     A_					      =					tools[func_name]
     # Todo deal with args
     A_					      =					[evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							) for arg in call.args]
     A_					      =					{keyword.arg: evaluate_ast(keyword.value     ,	_UpperCAmelCase     ,	_UpperCAmelCase							) for keyword in call.keywords}
     return func(*_UpperCAmelCase     ,	**_UpperCAmelCase							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :Tuple							)  ->							Union[str, Any]:
     '''simple docstring'''


     A_					      =					evaluate_ast(subscript.slice     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     A_					      =					evaluate_ast(subscript.value     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     if isinstance(_UpperCAmelCase     ,	(list, tuple)							):
          return value[int(_UpperCAmelCase							)]
     if index in value:
          return value[index]
     if isinstance(_UpperCAmelCase     ,	_UpperCAmelCase							) and isinstance(_UpperCAmelCase     ,	_UpperCAmelCase							):
          A_					      =					difflib.get_close_matches(_UpperCAmelCase     ,	list(value.keys()							)							)
          if len(_UpperCAmelCase							) > 0:
               return value[close_matches[0]]

     raise InterpretorError(f'Could not index {value} with \'{index}\'.'							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]     ,	_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Tuple							)  ->							Tuple:
     '''simple docstring'''


     if name.id in state:
          return state[name.id]
     A_					      =					difflib.get_close_matches(name.id     ,	list(state.keys()							)							)
     if len(_UpperCAmelCase							) > 0:
          return state[close_matches[0]]
     raise InterpretorError(f'The variable `{name.id}` is not defined.'							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[int]     ,	_UpperCAmelCase       :Union[str, Any]     ,	_UpperCAmelCase       :str							)  ->							Optional[int]:
     '''simple docstring'''


     if len(condition.ops							) > 1:
          raise InterpretorError('''Cannot evaluate conditions with multiple operators'''							)

     A_					      =					evaluate_ast(condition.left     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     A_					      =					condition.ops[0]
     A_					      =					evaluate_ast(condition.comparators[0]     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)

     if isinstance(_UpperCAmelCase     ,	ast.Eq							):
          return left == right
     elif isinstance(_UpperCAmelCase     ,	ast.NotEq							):
          return left != right
     elif isinstance(_UpperCAmelCase     ,	ast.Lt							):
          return left < right
     elif isinstance(_UpperCAmelCase     ,	ast.LtE							):
          return left <= right
     elif isinstance(_UpperCAmelCase     ,	ast.Gt							):
          return left > right
     elif isinstance(_UpperCAmelCase     ,	ast.GtE							):
          return left >= right
     elif isinstance(_UpperCAmelCase     ,	ast.Is							):
          return left is right
     elif isinstance(_UpperCAmelCase     ,	ast.IsNot							):
          return left is not right
     elif isinstance(_UpperCAmelCase     ,	ast.In							):
          return left in right
     elif isinstance(_UpperCAmelCase     ,	ast.NotIn							):
          return left not in right
     else:
          raise InterpretorError(f'Operator not supported: {comparator}'							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[Any]     ,	_UpperCAmelCase       :str     ,	_UpperCAmelCase       :List[Any]							)  ->							Optional[Any]:
     '''simple docstring'''


     A_					      =					None
     if evaluate_condition(if_statement.test     ,	_UpperCAmelCase     ,	_UpperCAmelCase							):
          for line in if_statement.body:
               A_					      =					evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
               if line_result is not None:
                    A_					      =					line_result
     else:
          for line in if_statement.orelse:
               A_					      =					evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
               if line_result is not None:
                    A_					      =					line_result
     return result


def     UpperCAmelCase_						(						_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :List[Any]     ,	_UpperCAmelCase       :Tuple							)  ->							str:
     '''simple docstring'''


     A_					      =					None
     A_					      =					evaluate_ast(for_loop.iter     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
     for counter in iterator:
          A_					      =					counter
          for expression in for_loop.body:
               A_					      =					evaluate_ast(_UpperCAmelCase     ,	_UpperCAmelCase     ,	_UpperCAmelCase							)
               if line_result is not None:
                    A_					      =					line_result
     return result



