

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_a:      List[str]								=     logging.get_logger(__name__)

class   __UpperCamelCase      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       ['input_values', 'attention_mask']




	def __init__(						self		:  str						,		lowerCAmelCase		:  Dict=1						,		lowerCAmelCase		:  Optional[Any]=16_000						,		lowerCAmelCase		:  Optional[Any]=0.0						,		lowerCAmelCase		:  Union[str, Any]=False						,		lowerCAmelCase		:  Dict=True						,		**lowerCAmelCase		:  Tuple						,		):


		'''simple docstring'''







		super().__init__(feature_size=lowerCAmelCase						,		sampling_rate=lowerCAmelCase						,		padding_value=lowerCAmelCase						,		**lowerCAmelCase							)
		UpperCAmelCase_    			=     return_attention_mask
		UpperCAmelCase_    			=     do_normalize




	@staticmethod
	def  __A   (						lowerCAmelCase		:  List[np.ndarray]						,		lowerCAmelCase		:  List[np.ndarray]						,		lowerCAmelCase		:  float = 0.0							):


		'''simple docstring'''







		if attention_mask is not None:
			UpperCAmelCase_    			=     np.array(lowerCAmelCase						,		np.intaa							)
			UpperCAmelCase_    			=     []

			for vector, length in zip(lowerCAmelCase						,		attention_mask.sum(-1							)							):
				UpperCAmelCase_    			=     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
				if length < normed_slice.shape[0]:
					UpperCAmelCase_    			=     padding_value

				normed_input_values.append(lowerCAmelCase							)
		else:
			UpperCAmelCase_    			=     [(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

		return normed_input_values




	def __call__(						self		:  Optional[int]						,		lowerCAmelCase		:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,		lowerCAmelCase		:  Union[bool, str, PaddingStrategy] = False						,		lowerCAmelCase		:  Optional[int] = None						,		lowerCAmelCase		:  bool = False						,		lowerCAmelCase		:  Optional[int] = None						,		lowerCAmelCase		:  Optional[bool] = None						,		lowerCAmelCase		:  Optional[Union[str, TensorType]] = None						,		lowerCAmelCase		:  Optional[int] = None						,		**lowerCAmelCase		:  Dict						,		):


		'''simple docstring'''







		if sampling_rate is not None:
			if sampling_rate != self.sampling_rate:
				raise ValueError(
				    F"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
				    F" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
				    F" {self.sampling_rate} and not {sampling_rate}."							)
		else:
			logger.warning(
			    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
			    "Failing to do so can result in silent errors that might be hard to debug."							)

		UpperCAmelCase_    			=     isinstance(lowerCAmelCase						,		np.ndarray							) and len(raw_speech.shape							) > 1
		if is_batched_numpy and len(raw_speech.shape							) > 2:
			raise ValueError(F"Only mono-channel audio is supported for input to {self}"							)
		UpperCAmelCase_    			=     is_batched_numpy or (
		    isinstance(lowerCAmelCase						,		(list, tuple)							) and (isinstance(raw_speech[0]						,		(np.ndarray, tuple, list)							))
		)

		# always return batch
		if not is_batched:
			UpperCAmelCase_    			=     [raw_speech]

		# convert into correct format for padding
		UpperCAmelCase_    			=     BatchFeature({"input_values": raw_speech}							)

		UpperCAmelCase_    			=     self.pad(
		    lowerCAmelCase						,		padding=lowerCAmelCase						,		max_length=lowerCAmelCase						,		truncation=lowerCAmelCase						,		pad_to_multiple_of=lowerCAmelCase						,		return_attention_mask=lowerCAmelCase						,		)

		# convert input values to correct format
		UpperCAmelCase_    			=     padded_inputs["input_values"]
		if not isinstance(input_values[0]						,		np.ndarray							):
			UpperCAmelCase_    			=     [np.asarray(lowerCAmelCase						,		dtype=np.floataa							) for array in input_values]
		elif (
		    not isinstance(lowerCAmelCase						,		np.ndarray							)
		    and isinstance(input_values[0]						,		np.ndarray							)
		    and input_values[0].dtype is np.dtype(np.floataa							)
		):
			UpperCAmelCase_    			=     [array.astype(np.floataa							) for array in input_values]
		elif isinstance(lowerCAmelCase						,		np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
			UpperCAmelCase_    			=     input_values.astype(np.floataa							)

		# convert attention_mask to correct format
		UpperCAmelCase_    			=     padded_inputs.get("attention_mask"							)
		if attention_mask is not None:
			UpperCAmelCase_    			=     [np.asarray(lowerCAmelCase						,		dtype=np.intaa							) for array in attention_mask]

		# zero-mean and unit-variance normalization
		if self.do_normalize:
			UpperCAmelCase_    			=     (
			    attention_mask
			    if self._get_padding_strategies(lowerCAmelCase						,		max_length=lowerCAmelCase							) is not PaddingStrategy.DO_NOT_PAD
			    else None
			)
			UpperCAmelCase_    			=     self.zero_mean_unit_var_norm(
			    padded_inputs["input_values"]						,		attention_mask=lowerCAmelCase						,		padding_value=self.padding_value							)

		if return_tensors is not None:
			UpperCAmelCase_    			=     padded_inputs.convert_to_tensors(lowerCAmelCase							)

		return padded_inputs


import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class   __UpperCamelCase      (   unittest.TestCase       ,       lowercase  ):




	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_tool("document-question-answering"							)
		self.tool.setup()
		UpperCAmelCase_    			=     load_tool("document-question-answering"						,		remote=lowerCAmelCase							)




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(lowerCAmelCase						,		"When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		self.tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     load_dataset("hf-internal-testing/example-documents"						,		split="test"							)
		UpperCAmelCase_    			=     dataset[0]["image"]

		UpperCAmelCase_    			=     self.remote_tool(document=lowerCAmelCase						,		question="When is the coffee break?"							)
		self.assertEqual(lowerCAmelCase						,		"11-14 to 11:39 a.m."							)