





from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowercase :			Tuple     		=    logging.get_logger(__name__)





@add_end_docstrings(_UpperCamelCase      )
class 					A__       (				_UpperCamelCase      ):




   """simple docstring"""



   def __init__(   self   ,    **lowercase)     ->		Optional[Any]:







          '''simple docstring'''

          super().__init__(**__a)

          if self.framework != "pt":
                 raise ValueError(F'The {self.__class__} is only available in PyTorch.')
          # No specific FOR_XXX available yet

   def __call__(   self   ,    lowercase   ,    **lowercase)     ->		str:







          '''simple docstring'''

          return super().__call__(__a   ,    **__a)

   def 				__lowercase						(   self   ,    **lowercase)     ->		str:







          '''simple docstring'''

          a__		:      Optional[int]					=							{}
          if "candidate_labels" in kwargs:
                 a__		:      Dict					=							kwargs["candidate_labels"]
          if "hypothesis_template" in kwargs:
                 a__		:      str					=							kwargs["hypothesis_template"]

          return preprocess_params, {}, {}

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase=None   ,    lowercase="This is a sound of {}.")     ->		List[str]:







          '''simple docstring'''

          if isinstance(__a   ,    __a):
                 if audio.startswith('http://') or audio.startswith('https://'):
                        # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                        # like http_huggingface_co.png
                        a__		:      Optional[Any]					=							requests.get(__a).content
                 else:
                        with open(__a   ,    'rb') as f:
                               a__		:      Optional[Any]					=							f.read()

          if isinstance(__a   ,    __a):
                 a__		:      List[str]					=							ffmpeg_read(__a   ,    self.feature_extractor.sampling_rate)

          if not isinstance(__a   ,    np.ndarray):
                 raise ValueError('We expect a numpy ndarray as input')
          if len(audio.shape) != 1:
                 raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline')

          a__		:      str					=							self.feature_extractor(
              [audio]   ,    sampling_rate=self.feature_extractor.sampling_rate   ,    return_tensors='pt')
          a__		:      Any					=							candidate_labels
          a__		:      Union[str, Any]					=							[hypothesis_template.format(__a) for x in candidate_labels]
          a__		:      Tuple					=							self.tokenizer(__a   ,    return_tensors=self.framework   ,    padding=__a)
          a__		:      Optional[int]					=							[text_inputs]
          return inputs

   def 				__lowercase						(   self   ,    lowercase)     ->		int:







          '''simple docstring'''

          a__		:      Dict					=							model_inputs.pop('candidate_labels')
          a__		:      List[str]					=							model_inputs.pop('text_inputs')
          if isinstance(text_inputs[0]   ,    __a):
                 a__		:      Dict					=							text_inputs[0]
          else:
                 # Batching case.
                 a__		:      Union[str, Any]					=							text_inputs[0][0]

          a__		:      Optional[int]					=							self.model(**__a   ,    **__a)

          a__		:      List[str]					=							{
              "candidate_labels": candidate_labels,
              "logits": outputs.logits_per_audio,
          }
          return model_outputs

   def 				__lowercase						(   self   ,    lowercase)     ->		Optional[Any]:







          '''simple docstring'''

          a__		:      Any					=							model_outputs.pop('candidate_labels')
          a__		:      str					=							model_outputs["logits"][0]

          if self.framework == "pt":
                 a__		:      Union[str, Any]					=							logits.softmax(dim=0)
                 a__		:      Any					=							probs.tolist()
          else:
                 raise ValueError('`tf` framework not supported.')

          a__		:      str					=							[
              {"score": score, "label": candidate_label}
              for score, candidate_label in sorted(zip(__a   ,    __a)   ,    key=lambda lowercase: -x[0])
          ]
          return result









"""simple docstring"""


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase__						= False
lowerCamelCase__						= True
lowerCamelCase__						= False


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()

  parser.add_argument(
      "--repo_path",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the architecture.",
  )

  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

  lowerCamelCase__						= parser.parse_args()

  lowerCamelCase__						= {
      "image_size": "sample_size",
      "num_res_blocks": "layers_per_block",
      "block_channels": "block_out_channels",
      "down_blocks": "down_block_types",
      "up_blocks": "up_block_types",
      "downscale_freq_shift": "freq_shift",
      "resnet_num_groups": "norm_num_groups",
      "resnet_act_fn": "act_fn",
      "resnet_eps": "norm_eps",
      "num_head_channels": "attention_head_dim",
  }

  lowerCamelCase__						= {
      "time_steps": "time_proj",
      "mid": "mid_block",
      "downsample_blocks": "down_blocks",
      "upsample_blocks": "up_blocks",
  }

  lowerCamelCase__						= "" if has_file(args.repo_path, "config.json") else "unet"

  with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
    lowerCamelCase__						= reader.read()
    lowerCamelCase__						= json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, "config.json"):
    lowerCamelCase__						= UNetaDModel(**config)
  else:
    lowerCamelCase__						= UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
    lowerCamelCase__						= class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowerCamelCase__						= dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowerCamelCase__						= config[key]
        del config[key]

    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["down_block_types"]]
    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["up_block_types"]]

  if do_only_weights:
    lowerCamelCase__						= torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

    lowerCamelCase__						= {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
        continue
      lowerCamelCase__						= False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(".")[0] == key:
          lowerCamelCase__						= param_value
          lowerCamelCase__						= True
      if not has_changed:
        lowerCamelCase__						= param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))
