




import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def       UpperCAmelCase_       (      __UpperCamelCase,							__UpperCamelCase      ):
    SCREAMING_SNAKE_CASE__        =f"""{sampling_rate}"""
    SCREAMING_SNAKE_CASE__        ="""1"""
    SCREAMING_SNAKE_CASE__        ="""f32le"""
    SCREAMING_SNAKE_CASE__        =[
        """ffmpeg""",
        """-i""",
        """pipe:0""",
        """-ac""",
        ac,
        """-ar""",
        ar,
        """-f""",
        format_for_conversion,
        """-hide_banner""",
        """-loglevel""",
        """quiet""",
        """pipe:1""",
    ]

    try:
        with subprocess.Popen(__UpperCamelCase,							stdin=subprocess.PIPE,							stdout=subprocess.PIPE      ) as ffmpeg_process:
            SCREAMING_SNAKE_CASE__        =ffmpeg_process.communicate(__UpperCamelCase      )
    except FileNotFoundError as error:
        raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""      ) from error
    SCREAMING_SNAKE_CASE__        =output_stream[0]
    SCREAMING_SNAKE_CASE__        =np.frombuffer(__UpperCamelCase,							np.floataa      )
    if audio.shape[0] == 0:
        raise ValueError("""Malformed soundfile"""      )
    return audio



def       UpperCAmelCase_       (      __UpperCamelCase,							__UpperCamelCase,							__UpperCamelCase = "f32le",							):
    SCREAMING_SNAKE_CASE__        =f"""{sampling_rate}"""
    SCREAMING_SNAKE_CASE__        ="""1"""
    if format_for_conversion == "s16le":
        SCREAMING_SNAKE_CASE__        =2
    elif format_for_conversion == "f32le":
        SCREAMING_SNAKE_CASE__        =4
    else:
        raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

    SCREAMING_SNAKE_CASE__        =platform.system()
    if system == "Linux":
        SCREAMING_SNAKE_CASE__        ="""alsa"""
        SCREAMING_SNAKE_CASE__        ="""default"""
    elif system == "Darwin":
        SCREAMING_SNAKE_CASE__        ="""avfoundation"""
        SCREAMING_SNAKE_CASE__        =""":0"""
    elif system == "Windows":
        SCREAMING_SNAKE_CASE__        ="""dshow"""
        SCREAMING_SNAKE_CASE__        ="""default"""

    SCREAMING_SNAKE_CASE__        =[
        """ffmpeg""",
        """-f""",
        format_,
        """-i""",
        input_,
        """-ac""",
        ac,
        """-ar""",
        ar,
        """-f""",
        format_for_conversion,
        """-fflags""",
        """nobuffer""",
        """-hide_banner""",
        """-loglevel""",
        """quiet""",
        """pipe:1""",
    ]
    SCREAMING_SNAKE_CASE__        =int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
    SCREAMING_SNAKE_CASE__        =_ffmpeg_stream(__UpperCamelCase,							__UpperCamelCase      )
    for item in iterator:
        yield item



def       UpperCAmelCase_       (      __UpperCamelCase,							__UpperCamelCase,							__UpperCamelCase = None,							__UpperCamelCase = None,							__UpperCamelCase = "f32le",							):
    if stream_chunk_s is not None:
        SCREAMING_SNAKE_CASE__        =stream_chunk_s
    else:
        SCREAMING_SNAKE_CASE__        =chunk_length_s

    SCREAMING_SNAKE_CASE__        =ffmpeg_microphone(__UpperCamelCase,							__UpperCamelCase,							format_for_conversion=__UpperCamelCase      )
    if format_for_conversion == "s16le":
        SCREAMING_SNAKE_CASE__        =np.intaa
        SCREAMING_SNAKE_CASE__        =2
    elif format_for_conversion == "f32le":
        SCREAMING_SNAKE_CASE__        =np.floataa
        SCREAMING_SNAKE_CASE__        =4
    else:
        raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""      )

    if stride_length_s is None:
        SCREAMING_SNAKE_CASE__        =chunk_length_s / 6
    SCREAMING_SNAKE_CASE__        =int(round(sampling_rate * chunk_length_s      )      ) * size_of_sample
    if isinstance(__UpperCamelCase,							(int, float)      ):
        SCREAMING_SNAKE_CASE__        =[stride_length_s, stride_length_s]

    SCREAMING_SNAKE_CASE__        =int(round(sampling_rate * stride_length_s[0]      )      ) * size_of_sample
    SCREAMING_SNAKE_CASE__        =int(round(sampling_rate * stride_length_s[1]      )      ) * size_of_sample
    SCREAMING_SNAKE_CASE__        =datetime.datetime.now()
    SCREAMING_SNAKE_CASE__        =datetime.timedelta(seconds=__UpperCamelCase      )
    for item in chunk_bytes_iter(__UpperCamelCase,							__UpperCamelCase,							stride=(stride_left, stride_right),							stream=__UpperCamelCase      ):
        # Put everything back in numpy scale
        SCREAMING_SNAKE_CASE__        =np.frombuffer(item["""raw"""],							dtype=__UpperCamelCase      )
        SCREAMING_SNAKE_CASE__        =(
            item["""stride"""][0] // size_of_sample,
            item["""stride"""][1] // size_of_sample,
        )
        SCREAMING_SNAKE_CASE__        =sampling_rate
        audio_time += delta
        if datetime.datetime.now() > audio_time + 10 * delta:
            # We're late !! SKIP
            continue
        yield item



def       UpperCAmelCase_       (      __UpperCamelCase,							__UpperCamelCase,							__UpperCamelCase,							__UpperCamelCase = False      ):
    SCREAMING_SNAKE_CASE__        =B""""""
    SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =stride
    if stride_left + stride_right >= chunk_len:
        raise ValueError(
            f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""      )
    SCREAMING_SNAKE_CASE__        =0
    for raw in iterator:
        acc += raw
        if stream and len(__UpperCamelCase      ) < chunk_len:
            SCREAMING_SNAKE_CASE__        =(_stride_left, 0)
            yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
        else:
            while len(__UpperCamelCase      ) >= chunk_len:
                # We are flushing the accumulator
                SCREAMING_SNAKE_CASE__        =(_stride_left, stride_right)
                SCREAMING_SNAKE_CASE__        ={"""raw""": acc[:chunk_len], """stride""": stride}
                if stream:
                    SCREAMING_SNAKE_CASE__        =False
                yield item
                SCREAMING_SNAKE_CASE__        =stride_left
                SCREAMING_SNAKE_CASE__        =acc[chunk_len - stride_left - stride_right :]
        # Last chunk
    if len(__UpperCamelCase      ) > stride_left:
        SCREAMING_SNAKE_CASE__        ={"""raw""": acc, """stride""": (_stride_left, 0)}
        if stream:
            SCREAMING_SNAKE_CASE__        =False
        yield item




def       UpperCAmelCase_       (      __UpperCamelCase,							__UpperCamelCase      ):
    SCREAMING_SNAKE_CASE__        =2**24  # 16Mo
    try:
        with subprocess.Popen(__UpperCamelCase,							stdout=subprocess.PIPE,							bufsize=__UpperCamelCase      ) as ffmpeg_process:
            while True:
                SCREAMING_SNAKE_CASE__        =ffmpeg_process.stdout.read(__UpperCamelCase      )
                if raw == b"":
                    break
                yield raw
    except FileNotFoundError as error:
        raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""      ) from error









'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_a					:			List[Any]							=    logging.get_logger()



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			List[nn.Module]     	= field(default_factory=a			)
  __lowerCAmelCase       :			list     	= field(default_factory=a			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Tuple:


    """simple docstring"""
    UpperCAmelCase    				=						len(list(m.modules()  )  ) == 1 or isinstance(a_ ,			nn.Convad  ) or isinstance(a_ ,			nn.BatchNormad  )
    if has_not_submodules:
      self.traced.append(a_  )
  def __call__(  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    for m in self.module.modules():
      self.handles.append(m.register_forward_hook(self._forward_hook  )  )
    self.module(a_  )
    [x.remove() for x in self.handles]
    return self





  @property
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    # check the len of the state_dict keys to see if we have learnable params
    return list(filter(lambda a_  : len(list(x.state_dict().keys()  )  ) > 0 ,			self.traced  )  )




@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			int     	= 0
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  def __call__(  self ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						Tracker(self.dest  )(a_  ).parametrized
    UpperCAmelCase    				=						Tracker(self.src  )(a_  ).parametrized

    UpperCAmelCase    				=						list(filter(lambda a_  : type(a_  ) not in self.src_skip ,			a_  )  )
    UpperCAmelCase    				=						list(filter(lambda a_  : type(a_  ) not in self.dest_skip ,			a_  )  )

    if len(a_  ) != len(a_  ):
      raise Exception(
          F'''Numbers of operations are different. Source module has {len(a_  )} operations while'''
          F''' destination module has {len(a_  )}.'''  )

    for dest_m, src_m in zip(a_ ,			a_  ):
      dest_m.load_state_dict(src_m.state_dict()  )
      if self.verbose == 1:
        print(F'''Transfered from={src_m} to={dest_m}'''  )




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : ResNetConfig							, SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : bool = True    ):
  print(f'''Converting {name}...'''    )
  with torch.no_grad():
    UpperCAmelCase    				=						timm.create_model(SCREAMING_SNAKE_CASE							, pretrained=SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ResNetForImageClassification(SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ModuleTransfer(src=SCREAMING_SNAKE_CASE							, dest=SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						torch.randn((1, 3, 224, 224)    )
    module_transfer(SCREAMING_SNAKE_CASE    )

  assert torch.allclose(from_model(SCREAMING_SNAKE_CASE    )							, our_model(SCREAMING_SNAKE_CASE    ).logits    ), "The model logits don't match the original one."

  UpperCAmelCase    				=						f'''resnet{'-'.join(name.split('resnet'    )    )}'''
  print(SCREAMING_SNAKE_CASE    )

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							, commit_message='Add model'							, use_temp_dir=SCREAMING_SNAKE_CASE							, )

    # we can use the convnext one
    UpperCAmelCase    				=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							, commit_message='Add image processor'							, use_temp_dir=SCREAMING_SNAKE_CASE							, )

    print(f'''Pushed {checkpoint_name}'''    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : str = None							, SCREAMING_SNAKE_CASE   : bool = True    ):
  UpperCAmelCase    				=						'imagenet-1k-id2label.json'
  UpperCAmelCase    				=						1000
  UpperCAmelCase    				=						(1, num_labels)

  UpperCAmelCase    				=						'huggingface/label-files'
  UpperCAmelCase    				=						num_labels
  UpperCAmelCase    				=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, repo_type='dataset'    )							, 'r'    )    )
  UpperCAmelCase    				=						{int(SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}

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

  UpperCAmelCase    				=						partial(SCREAMING_SNAKE_CASE							, num_labels=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid=SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						{
      'resnet18': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							, hidden_sizes=[64, 128, 256, 512]							, layer_type='basic'    ),
      'resnet26': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
      'resnet34': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							, hidden_sizes=[64, 128, 256, 512]							, layer_type='basic'    ),
      'resnet50': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
      'resnet101': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
      'resnet152': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]							, hidden_sizes=[256, 512, 1024, 2048]							, layer_type='bottleneck'    ),
  }

  if model_name:
    convert_weight_and_push(SCREAMING_SNAKE_CASE							, names_to_config[model_name]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  return config, expected_shape


if __name__ == "__main__":
  _a					:			List[str]							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help=(
          'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
          ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=Path,
      required=True,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      default=True,
      type=bool,
      required=False,
      help='If True, push model and image processor to the hub.',
  )

  _a					:			Tuple							=    parser.parse_args()
  _a					:			Path							=    args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


