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


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


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	MobileNetVaConfig(layer_norm_eps=0.001			)

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

 lowerCAmelCase__     :   Tuple     		=	re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"      ,			lowerCamelCase_			)
 if matches:
  lowerCAmelCase__     :   List[Any]     		=	float(matches[1]			)
  lowerCAmelCase__     :   List[Any]     		=	int(matches[2]			)

 # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
 # the usual 1000. The first class (index 0) is "background".
 lowerCAmelCase__     :   Dict     		=	1_0_0_1
 lowerCAmelCase__     :   str     		=	"imagenet-1k-id2label.json"
 lowerCAmelCase__     :   Union[str, Any]     		=	"huggingface/label-files"
 lowerCAmelCase__     :   int     		=	json.load(open(hf_hub_download(lowerCamelCase_      ,			lowerCamelCase_      ,			repo_type="dataset"			)      ,			"r"			)			)
 lowerCAmelCase__     :   Union[str, Any]     		=	{int(lowerCamelCase_			) + 1: v for k, v in idalabel.items()}
 lowerCAmelCase__     :   List[str]     		=	"background"
 lowerCAmelCase__     :   int     		=	idalabel
 lowerCAmelCase__     :   Union[str, Any]     		=	{v: k for k, v in idalabel.items()}

 return config


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCAmelCase__     :   List[str]     		=	Image.open(requests.get(lowerCamelCase_      ,			stream=lowerCamelCase_			).raw			)
 return im


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=False			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	get_mobilenet_va_config(lowerCamelCase_			)

 # Load 🤗 model
 lowerCAmelCase__     :   int     		=	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
 lowerCAmelCase__     :   Optional[Any]     		=	MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}      ,			size={"shortest_edge": config.image_size + 3_2}      ,			)
 lowerCAmelCase__     :   Tuple     		=	image_processor(images=prepare_img()      ,			return_tensors="pt"			)
 lowerCAmelCase__     :   Dict     		=	model(**lowerCamelCase_			)
 lowerCAmelCase__     :   int     		=	outputs.logits

 assert logits.shape == (1, 1_0_0_1)

 if model_name == "mobilenet_v1_1.0_224":
  lowerCAmelCase__     :   Optional[Any]     		=	torch.tensor([-4.1739, -1.1233, 3.1205]			)
 elif model_name == "mobilenet_v1_0.75_192":
  lowerCAmelCase__     :   str     		=	torch.tensor([-3.9440, -2.3141, -0.3333]			)
 else:
  lowerCAmelCase__     :   Tuple     		=	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..."			)
  lowerCAmelCase__     :   List[str]     		=	"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]





