
'''simple docstring'''

import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

UpperCAmelCase__				:	Union[str, Any]					=						logging.get_logger(__name__)







def 					A       ( UpperCamelCase_   :       bytes  ,		UpperCamelCase_   :       int )   ->    np.array:







			'''simple docstring'''



			lowerCAmelCase__									=							F"""{sampling_rate}"""
			lowerCAmelCase__									=							"1"
			lowerCAmelCase__									=							"f32le"
			lowerCAmelCase__									=							[
			    "ffmpeg",
			    "-i",
			    "pipe:0",
			    "-ac",
			    ac,
			    "-ar",
			    ar,
			    "-f",
			    format_for_conversion,
			    "-hide_banner",
			    "-loglevel",
			    "quiet",
			    "pipe:1",
			]

			try:
						lowerCAmelCase__									=							subprocess.Popen(UpperCamelCase_  ,		stdin=subprocess.PIPE  ,		stdout=subprocess.PIPE )
			except FileNotFoundError:
						raise ValueError("ffmpeg was not found but is required to load audio files from filename" )
			lowerCAmelCase__									=							ffmpeg_process.communicate(UpperCamelCase_ )
			lowerCAmelCase__									=							output_stream[0]

			lowerCAmelCase__									=							np.frombuffer(UpperCamelCase_  ,		np.floataa )
			if audio.shape[0] == 0:
						raise ValueError("Malformed soundfile" )
			return audio






@add_end_docstrings(SCREAMING_SNAKE_CASE__      )
class 				A       (		SCREAMING_SNAKE_CASE__      ):





	def __init__(   self     :    Union[str, Any] ,							*__magic_name__     :    Union[str, Any] ,							**__magic_name__     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							5
				super().__init__(*__magic_name__ ,							**__magic_name__							)

				if self.framework != "pt":
							raise ValueError(f"""The {self.__class__} is only available in PyTorch."""							)

				self.check_model_type(__magic_name__							)





	def __call__(   self     :    Union[str, Any] ,							__magic_name__     :    Union[np.ndarray, bytes, str] ,							**__magic_name__     :    Optional[int] ,							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int ,							__magic_name__     :    Optional[Any]=None ,							**__magic_name__     :    Any							):


				"""simple docstring"""

				lowerCAmelCase__									=							{}
				if top_k is not None:
							if top_k > self.model.config.num_labels:
										lowerCAmelCase__									=							self.model.config.num_labels
							lowerCAmelCase__									=							top_k
				return {}, {}, postprocess_params





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

				if isinstance(__magic_name__ ,							__magic_name__							):
							if inputs.startswith("http://"							) or inputs.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
										lowerCAmelCase__									=							requests.get(__magic_name__							).content
							else:
										with open(__magic_name__ ,							"rb"							) as f:
													lowerCAmelCase__									=							f.read()

				if isinstance(__magic_name__ ,							__magic_name__							):
							lowerCAmelCase__									=							ffmpeg_read(__magic_name__ ,							self.feature_extractor.sampling_rate							)

				if isinstance(__magic_name__ ,							__magic_name__							):
							# Accepting `"array"` which is the key defined in `datasets` for
							# better integration
							if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
										raise ValueError(
										    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
										    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
										    "containing the sampling_rate associated with that array"							)

							lowerCAmelCase__									=							inputs.pop("raw" ,							__magic_name__							)
							if _inputs is None:
										# Remove path which will not be used from `datasets`.
										inputs.pop("path" ,							__magic_name__							)
										lowerCAmelCase__									=							inputs.pop("array" ,							__magic_name__							)
							lowerCAmelCase__									=							inputs.pop("sampling_rate"							)
							lowerCAmelCase__									=							_inputs
							if in_sampling_rate != self.feature_extractor.sampling_rate:
										import torch

										if is_torchaudio_available():
													from torchaudio import functional as F
										else:
													raise ImportError(
													    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
													    "The torchaudio package can be installed through: `pip install torchaudio`."							)

										lowerCAmelCase__									=							F.resample(
										    torch.from_numpy(__magic_name__							) ,							__magic_name__ ,							self.feature_extractor.sampling_rate							).numpy()

				if not isinstance(__magic_name__ ,							np.ndarray							):
							raise ValueError("We expect a numpy ndarray as input"							)
				if len(inputs.shape							) != 1:
							raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"							)

				lowerCAmelCase__									=							self.feature_extractor(
				    __magic_name__ ,							sampling_rate=self.feature_extractor.sampling_rate ,							return_tensors="pt"							)
				return processed





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any] ,							__magic_name__     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.model(**__magic_name__							)
				return model_outputs







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict ,							__magic_name__     :    Any ,							__magic_name__     :    Optional[int]=5							):


				"""simple docstring"""

				lowerCAmelCase__									=							model_outputs.logits[0].softmax(-1							)
				lowerCAmelCase__		,lowerCAmelCase__									=							probs.topk(__magic_name__							)

				lowerCAmelCase__									=							scores.tolist()
				lowerCAmelCase__									=							ids.tolist()

				lowerCAmelCase__									=							[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__magic_name__ ,							__magic_name__							)]

				return labels







'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block






@dataclass
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :torch.FloatTensor






class 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):





	@register_to_config
	def __init__(   self     :    Tuple ,							__magic_name__     :    Optional[Union[int, Tuple[int, int]]] = None ,							__magic_name__     :    int = 3 ,							__magic_name__     :    int = 3 ,							__magic_name__     :    bool = False ,							__magic_name__     :    str = "positional" ,							__magic_name__     :    int = 0 ,							__magic_name__     :    bool = True ,							__magic_name__     :    Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,							__magic_name__     :    Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,							__magic_name__     :    Tuple[int] = (224, 448, 672, 896) ,							__magic_name__     :    int = 2 ,							__magic_name__     :    float = 1 ,							__magic_name__     :    int = 1 ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "conv" ,							__magic_name__     :    str = "silu" ,							__magic_name__     :    Optional[int] = 8 ,							__magic_name__     :    int = 32 ,							__magic_name__     :    float = 1E-5 ,							__magic_name__     :    str = "default" ,							__magic_name__     :    bool = True ,							__magic_name__     :    Optional[str] = None ,							__magic_name__     :    Optional[int] = None ,							):


				"""simple docstring"""

				super().__init__()

				lowerCAmelCase__									=							sample_size
				lowerCAmelCase__									=							block_out_channels[0] * 4

				# Check inputs
				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""							)

				if len(__magic_name__							) != len(__magic_name__							):
							raise ValueError(
							    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""							)

				# input
				lowerCAmelCase__									=							nn.Convad(__magic_name__ ,							block_out_channels[0] ,							kernel_size=3 ,							padding=(1, 1)							)

				# time
				if time_embedding_type == "fourier":
							lowerCAmelCase__									=							GaussianFourierProjection(embedding_size=block_out_channels[0] ,							scale=16							)
							lowerCAmelCase__									=							2 * block_out_channels[0]
				elif time_embedding_type == "positional":
							lowerCAmelCase__									=							Timesteps(block_out_channels[0] ,							__magic_name__ ,							__magic_name__							)
							lowerCAmelCase__									=							block_out_channels[0]

				lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)

				# class embedding
				if class_embed_type is None and num_class_embeds is not None:
							lowerCAmelCase__									=							nn.Embedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "timestep":
							lowerCAmelCase__									=							TimestepEmbedding(__magic_name__ ,							__magic_name__							)
				elif class_embed_type == "identity":
							lowerCAmelCase__									=							nn.Identity(__magic_name__ ,							__magic_name__							)
				else:
							lowerCAmelCase__									=							None

				lowerCAmelCase__									=							nn.ModuleList([]							)
				lowerCAmelCase__									=							None
				lowerCAmelCase__									=							nn.ModuleList([]							)

				# down
				lowerCAmelCase__									=							block_out_channels[0]
				for i, down_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							block_out_channels[i]
							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

							lowerCAmelCase__									=							get_down_block(
							    __magic_name__ ,							num_layers=__magic_name__ ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							temb_channels=__magic_name__ ,							add_downsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							downsample_padding=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							downsample_type=__magic_name__ ,							)
							self.down_blocks.append(__magic_name__							)

				# mid
				lowerCAmelCase__									=							UNetMidBlockaD(
				    in_channels=block_out_channels[-1] ,							temb_channels=__magic_name__ ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							output_scale_factor=__magic_name__ ,							resnet_time_scale_shift=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1] ,							resnet_groups=__magic_name__ ,							add_attention=__magic_name__ ,							)

				# up
				lowerCAmelCase__									=							list(reversed(__magic_name__							)							)
				lowerCAmelCase__									=							reversed_block_out_channels[0]
				for i, up_block_type in enumerate(__magic_name__							):
							lowerCAmelCase__									=							output_channel
							lowerCAmelCase__									=							reversed_block_out_channels[i]
							lowerCAmelCase__									=							reversed_block_out_channels[min(i + 1 ,							len(__magic_name__							) - 1							)]

							lowerCAmelCase__									=							i == len(__magic_name__							) - 1

							lowerCAmelCase__									=							get_up_block(
							    __magic_name__ ,							num_layers=layers_per_block + 1 ,							in_channels=__magic_name__ ,							out_channels=__magic_name__ ,							prev_output_channel=__magic_name__ ,							temb_channels=__magic_name__ ,							add_upsample=not is_final_block ,							resnet_eps=__magic_name__ ,							resnet_act_fn=__magic_name__ ,							resnet_groups=__magic_name__ ,							attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,							resnet_time_scale_shift=__magic_name__ ,							upsample_type=__magic_name__ ,							)
							self.up_blocks.append(__magic_name__							)
							lowerCAmelCase__									=							output_channel

				# out
				lowerCAmelCase__									=							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,							32							)
				lowerCAmelCase__									=							nn.GroupNorm(num_channels=block_out_channels[0] ,							num_groups=__magic_name__ ,							eps=__magic_name__							)
				lowerCAmelCase__									=							nn.SiLU()
				lowerCAmelCase__									=							nn.Convad(block_out_channels[0] ,							__magic_name__ ,							kernel_size=3 ,							padding=1							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    torch.FloatTensor ,							__magic_name__     :    Union[torch.Tensor, float, int] ,							__magic_name__     :    Optional[torch.Tensor] = None ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				if self.config.center_input_sample:
							lowerCAmelCase__									=							2 * sample - 1.0

				# 1. time
				lowerCAmelCase__									=							timestep
				if not torch.is_tensor(__magic_name__							):
							lowerCAmelCase__									=							torch.tensor([timesteps] ,							dtype=torch.long ,							device=sample.device							)
				elif torch.is_tensor(__magic_name__							) and len(timesteps.shape							) == 0:
							lowerCAmelCase__									=							timesteps[None].to(sample.device							)

				# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
				lowerCAmelCase__									=							timesteps * torch.ones(sample.shape[0] ,							dtype=timesteps.dtype ,							device=timesteps.device							)

				lowerCAmelCase__									=							self.time_proj(__magic_name__							)

				# timesteps does not contain any weights and will always return f32 tensors
				# but time_embedding might actually be running in fp16. so we need to cast here.
				# there might be better ways to encapsulate this.
				lowerCAmelCase__									=							t_emb.to(dtype=self.dtype							)
				lowerCAmelCase__									=							self.time_embedding(__magic_name__							)

				if self.class_embedding is not None:
							if class_labels is None:
										raise ValueError("class_labels should be provided when doing class conditioning"							)

							if self.config.class_embed_type == "timestep":
										lowerCAmelCase__									=							self.time_proj(__magic_name__							)

							lowerCAmelCase__									=							self.class_embedding(__magic_name__							).to(dtype=self.dtype							)
							lowerCAmelCase__									=							emb + class_emb

				# 2. pre-process
				lowerCAmelCase__									=							sample
				lowerCAmelCase__									=							self.conv_in(__magic_name__							)

				# 3. down
				lowerCAmelCase__									=							(sample,)
				for downsample_block in self.down_blocks:
							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(
										    hidden_states=__magic_name__ ,							temb=__magic_name__ ,							skip_sample=__magic_name__							)
							else:
										lowerCAmelCase__		,lowerCAmelCase__									=							downsample_block(hidden_states=__magic_name__ ,							temb=__magic_name__							)

							down_block_res_samples += res_samples

				# 4. mid
				lowerCAmelCase__									=							self.mid_block(__magic_name__ ,							__magic_name__							)

				# 5. up
				lowerCAmelCase__									=							None
				for upsample_block in self.up_blocks:
							lowerCAmelCase__									=							down_block_res_samples[-len(upsample_block.resnets							) :]
							lowerCAmelCase__									=							down_block_res_samples[: -len(upsample_block.resnets							)]

							if hasattr(__magic_name__ ,							"skip_conv"							):
										lowerCAmelCase__		,lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__							)
							else:
										lowerCAmelCase__									=							upsample_block(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

        # 6. post-process
				lowerCAmelCase__									=							self.conv_norm_out(__magic_name__							)
				lowerCAmelCase__									=							self.conv_act(__magic_name__							)
				lowerCAmelCase__									=							self.conv_out(__magic_name__							)

				if skip_sample is not None:
							sample += skip_sample

				if self.config.time_embedding_type == "fourier":
							lowerCAmelCase__									=							timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]							)))							)
							lowerCAmelCase__									=							sample / timesteps

				if not return_dict:
							return (sample,)

				return UNetaDOutput(sample=__magic_name__							)





