

from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_UpperCamelCase					     =						logging.get_logger(__name__)  # pylint: disable=invalid-name







class     __lowercase (A__					):
 def __init__(				self ,				A_ ,				A_   )   ->List[str]:



       '''simple docstring'''





       super().__init__()
       self.register_modules(unet=a__ ,				scheduler=a__   )





 @torch.no_grad()
 def __call__(				self ,				A_ = 1 ,				A_ = 100 ,				A_ = None ,				A_ = None ,				A_ = True ,				)   ->List[str]:



       '''simple docstring'''





       if audio_length_in_s is None:
             __lowerCAmelCase   :		str   =   self.unet.config.sample_size / self.unet.config.sample_rate

       __lowerCAmelCase   :		List[str]   =   audio_length_in_s * self.unet.config.sample_rate

       __lowerCAmelCase   :		Optional[int]   =   2 ** len(self.unet.up_blocks   )
       if sample_size < 3 * down_scale_factor:
             raise ValueError(
                 f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                 f""" {3 * down_scale_factor / self.unet.config.sample_rate}."""   )

       __lowerCAmelCase   :		Optional[int]   =   int(a__   )
       if sample_size % down_scale_factor != 0:
             __lowerCAmelCase   :		Tuple   =   (
                 (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
             ) * down_scale_factor
             logger.info(
                 f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                 f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                 ''' process.'''   )
       __lowerCAmelCase   :		List[str]   =   int(a__   )

       __lowerCAmelCase   :		Optional[int]   =   next(iter(self.unet.parameters()   )   ).dtype
       __lowerCAmelCase   :		str   =   (batch_size, self.unet.config.in_channels, sample_size)
       if isinstance(a__ ,				a__   ) and len(a__   ) != batch_size:
             raise ValueError(
                 f"""You have passed a list of generators of length {len(a__   )}, but requested an effective batch"""
                 f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

       __lowerCAmelCase   :		Optional[int]   =   randn_tensor(a__ ,				generator=a__ ,				device=self.device ,				dtype=a__   )

       # set step values
       self.scheduler.set_timesteps(a__ ,				device=audio.device   )
       __lowerCAmelCase   :		List[Any]   =   self.scheduler.timesteps.to(a__   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
             # 1. predict noise model_output
             __lowerCAmelCase   :		List[Any]   =   self.unet(a__ ,				a__   ).sample

             # 2. compute previous image: x_t -> t_t-1
             __lowerCAmelCase   :		Dict   =   self.scheduler.step(a__ ,				a__ ,				a__   ).prev_sample

       __lowerCAmelCase   :		Any   =   audio.clamp(-1 ,				1   ).float().cpu().numpy()

       __lowerCAmelCase   :		Optional[Any]   =   audio[:, :, :original_sample_size]

       if not return_dict:
             return (audio,)

       return AudioPipelineOutput(audios=a__   )







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()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

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

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

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

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    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 MobileNetV1ImageProcessor
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

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

    if model_name == "mobilenet_v1_1.0_224":
        A__					=       torch.tensor([-4.1739, -1.1233, 3.1205]						)
    elif model_name == "mobilenet_v1_0.75_192":
        A__					=       torch.tensor([-3.9440, -2.3141, -0.3333]						)
    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__":
   _lowercase    	=		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."
   )

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