
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,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				      =    logging.get_logger(__name__)






def 				_lowercase				(	UpperCamelCase_						)						->						str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					MobileNetVaConfig(layer_norm_eps=0.001						)

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

					SCREAMING_SNAKE_CASE__						=					re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'      ,      lowerCamelCase_						)
					if matches:
										SCREAMING_SNAKE_CASE__						=					float(matches[1]						)
										SCREAMING_SNAKE_CASE__						=					int(matches[2]						)

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					SCREAMING_SNAKE_CASE__						=					1001
					SCREAMING_SNAKE_CASE__						=					"imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE__						=					"huggingface/label-files"
					SCREAMING_SNAKE_CASE__						=					json.load(open(hf_hub_download(lowerCamelCase_      ,      lowerCamelCase_      ,      repo_type='dataset'						)      ,      'r'						)						)
					SCREAMING_SNAKE_CASE__						=					{int(lowerCamelCase_						) + 1: v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE__						=					"background"
					SCREAMING_SNAKE_CASE__						=					idalabel
					SCREAMING_SNAKE_CASE__						=					{v: k for k, v in idalabel.items()}

					return config






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


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






@torch.no_grad()
def 				_lowercase				(	UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_      ,      UpperCamelCase_=False						)						->						int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						=					get_mobilenet_va_config(lowerCamelCase_						)

					# Load 🤗 model
					SCREAMING_SNAKE_CASE__						=					MobileNetVaForImageClassification(lowerCamelCase_						).eval()

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

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					SCREAMING_SNAKE_CASE__						=					MobileNetVaImageProcessor(
					    crop_size={'width': config.image_size, 'height': config.image_size}      ,      size={'shortest_edge': config.image_size + 32}      ,      )
					SCREAMING_SNAKE_CASE__						=					image_processor(images=prepare_img()      ,      return_tensors='pt'						)
					SCREAMING_SNAKE_CASE__						=					model(**lowerCamelCase_						)
					SCREAMING_SNAKE_CASE__						=					outputs.logits

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										SCREAMING_SNAKE_CASE__						=					torch.tensor([-4.1_739, -1.1_233, 3.1_205]						)
					elif model_name == "mobilenet_v1_0.75_192":
										SCREAMING_SNAKE_CASE__						=					torch.tensor([-3.9_440, -2.3_141, -0.3_333]						)
					else:
										SCREAMING_SNAKE_CASE__						=					None

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

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

					if push_to_hub:
										print('Pushing to the hub...'						)
										SCREAMING_SNAKE_CASE__						=					"google/" + model_name
										image_processor.push_to_hub(lowerCamelCase_						)
										model.push_to_hub(lowerCamelCase_						)


if __name__ == "__main__":
				__snake_case				      =    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--model_name""",
				    default="""mobilenet_v1_1.0_224""",
				    type=str,
				    help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<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."""
				)

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







'''simple docstring'''

# 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				int	=			"""openai/whisper-base"""
 A_				:				str	=			(
     """This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
     """transcribed text."""
 )
 A_				:				Union[str, Any]	=			"""transcriber"""
 A_				:				Tuple	=			WhisperProcessor
 A_				:				Dict	=			WhisperForConditionalGeneration

 A_				:				Tuple	=			["""audio"""]
 A_				:				Optional[int]	=			["""text"""]


 def    _A	(    self  : Optional[int]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  return self.pre_processor(a__			,							return_tensors="pt"    ).input_features


 def    _A	(    self  : List[str]			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  return self.model.generate(inputs=a__    )


 def    _A	(    self  : Optional[Any]			,							a__  : Dict    ):
  '''simple docstring'''

  return self.pre_processor.batch_decode(a__			,							skip_special_tokens=a__    )[0]





