



"""simple docstring"""







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


A		=						logging.get_logger(__name__)

@add_end_docstrings(SCREAMING_SNAKE_CASE__)
class       _a  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				Dict   ,	**_lowercase							:				List[Any]		)							->					List[str]:
									super().__init__(**_lowercase		)

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



			def __call__(			self							:				Union[str, Any]   ,	_lowercase							:				Union[np.ndarray, bytes, str]   ,	**_lowercase							:				Optional[int]		)							->					Tuple:
									return super().__call__(_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				List[Any]   ,	**_lowercase							:				Any		)							->					int:
									snake_case   :			int						    = {}
									if "candidate_labels" in kwargs:
															snake_case   :			List[Any]						    = kwargs["candidate_labels"]
									if "hypothesis_template" in kwargs:
															snake_case   :			Dict						    = kwargs["hypothesis_template"]

									return preprocess_params, {}, {}



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				List[Any]=None   ,	_lowercase							:				List[Any]="This is a sound of {}."		)							->					Optional[Any]:
									if isinstance(_lowercase   ,	_lowercase		):
															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
																					snake_case   :			List[Any]						    = requests.get(_lowercase		).content
															else:
																					with open(_lowercase   ,	"rb"		) as f:
																											snake_case   :			str						    = f.read()

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

									if not isinstance(_lowercase   ,	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"		)

									snake_case   :			Dict						    = self.feature_extractor(
									    [audio]   ,	sampling_rate=self.feature_extractor.sampling_rate   ,	return_tensors="pt"		)
									snake_case   :			Any						    = candidate_labels
									snake_case   :			Union[str, Any]						    = [hypothesis_template.format(_lowercase		) for x in candidate_labels]
									snake_case   :			Any						    = self.tokenizer(_lowercase   ,	return_tensors=self.framework   ,	padding=_lowercase		)
									snake_case   :			List[str]						    = [text_inputs]
									return inputs



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Optional[int]		)							->					Optional[int]:
									snake_case   :			List[Any]						    = model_inputs.pop("candidate_labels"		)
									snake_case   :			int						    = model_inputs.pop("text_inputs"		)
									if isinstance(text_inputs[0]   ,	_lowercase		):
															snake_case   :			Optional[Any]						    = text_inputs[0]
									else:
															# Batching case.
															snake_case   :			Dict						    = text_inputs[0][0]

									snake_case   :			Optional[int]						    = self.model(**_lowercase   ,	**_lowercase		)

									snake_case   :			Tuple						    = {
									    "candidate_labels": candidate_labels,
									    "logits": outputs.logits_per_audio,
									}
									return model_outputs



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				Union[str, Any]		)							->					List[Any]:
									snake_case   :			Optional[int]						    = model_outputs.pop("candidate_labels"		)
									snake_case   :			int						    = model_outputs["logits"][0]

									if self.framework == "pt":
															snake_case   :			int						    = logits.softmax(dim=0		)
															snake_case   :			Optional[Any]						    = probs.tolist()
									else:
															raise ValueError("`tf` framework not supported."		)

									snake_case   :			Union[str, Any]						    = [
									    {"score": score, "label": candidate_label}
									    for score, candidate_label in sorted(zip(_lowercase   ,	_lowercase		)   ,	key=lambda _lowercase		: -x[0]		)
									]
									return result








"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



			@property
			def 				__lowercase					(			self							:				str		)							->					int:
									return 13



