


'''simple docstring'''

from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
     from transformers import (
         TFBertModel,
         TFCLIPVisionModel,
         TFDeiTModel,
         TFRobertaModel,
         TFVisionTextDualEncoderModel,
         TFViTModel,
         VisionTextDualEncoderConfig,
     )

if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 if isinstance(__UpperCAmelCase					,				collections.abc.Iterable		):
  return x
 return (x, x)





@require_tf
class      snake_case    :





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase			,  _lowerCAmelCase     )

   lowerCamelCase_      =  TFVisionTextDualEncoderModel(_lowerCAmelCase     )

   lowerCamelCase_      =  model(input_ids=_lowerCAmelCase			,  pixel_values=_lowerCAmelCase			,  attention_mask=_lowerCAmelCase     )

   self.assertEqual(output["text_embeds"].shape			,  (input_ids.shape[0], config.projection_dim)     )
   self.assertEqual(output["image_embeds"].shape			,  (pixel_values.shape[0], config.projection_dim)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.get_vision_text_model(_lowerCAmelCase			,  _lowerCAmelCase     )
   lowerCamelCase_      =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,  text_model=_lowerCAmelCase     )

   lowerCamelCase_      =  model(input_ids=_lowerCAmelCase			,  pixel_values=_lowerCAmelCase			,  attention_mask=_lowerCAmelCase     )

   self.assertEqual(output["text_embeds"].shape			,  (input_ids.shape[0], model.config.projection_dim)     )
   self.assertEqual(output["image_embeds"].shape			,  (pixel_values.shape[0], model.config.projection_dim)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.get_vision_text_model(_lowerCAmelCase			,  _lowerCAmelCase     )
   lowerCamelCase_      =  {"vision_model": vision_model, "text_model": text_model}
   lowerCamelCase_      =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase     )

   lowerCamelCase_      =  model(input_ids=_lowerCAmelCase			,  pixel_values=_lowerCAmelCase			,  attention_mask=_lowerCAmelCase     )

   self.assertEqual(output["text_embeds"].shape			,  (input_ids.shape[0], model.config.projection_dim)     )
   self.assertEqual(output["image_embeds"].shape			,  (pixel_values.shape[0], model.config.projection_dim)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.get_vision_text_model(_lowerCAmelCase			,  _lowerCAmelCase     )
   lowerCamelCase_      =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,  text_model=_lowerCAmelCase     )

   lowerCamelCase_      =  model(input_ids=_lowerCAmelCase			,  pixel_values=_lowerCAmelCase			,  attention_mask=_lowerCAmelCase     )
   lowerCamelCase_      =  output[0].numpy()

   with tempfile.TemporaryDirectory() as tmpdirname:
    model.save_pretrained(_lowerCAmelCase     )
    lowerCamelCase_      =  TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase     )

    lowerCamelCase_      =  model(input_ids=_lowerCAmelCase			,  pixel_values=_lowerCAmelCase			,  attention_mask=_lowerCAmelCase     )
    lowerCamelCase_      =  after_output[0].numpy()
    lowerCamelCase_      =  np.amax(np.abs(out_a - out_a     )     )
    self.assertLessEqual(_lowerCAmelCase			,  1e-5     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.get_vision_text_model(_lowerCAmelCase			,  _lowerCAmelCase     )
   lowerCamelCase_      =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,  text_model=_lowerCAmelCase     )

   lowerCamelCase_      =  model(
       input_ids=_lowerCAmelCase			,  pixel_values=_lowerCAmelCase			,  attention_mask=_lowerCAmelCase			,  output_attentions=_lowerCAmelCase     )

   lowerCamelCase_      =  output.vision_model_output.attentions
   self.assertEqual(len(_lowerCAmelCase     )			,  vision_config.num_hidden_layers     )

   # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
   lowerCamelCase_      =  to_atuple(vision_model.config.image_size     )
   lowerCamelCase_      =  to_atuple(vision_model.config.patch_size     )
   lowerCamelCase_      =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
   lowerCamelCase_      =  num_patches + 1
   self.assertEqual(vision_attentions[0].shape[-3:]			,  (vision_config.num_attention_heads, seq_len, seq_len)     )

   lowerCamelCase_      =  output.text_model_output.attentions
   self.assertEqual(len(_lowerCAmelCase     )			,  text_config.num_hidden_layers     )

   self.assertEqual(
       text_attentions[0].shape[-3:]			,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,  )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  np.abs((a - b)     ).max()
   self.assertLessEqual(_lowerCAmelCase			,  _lowerCAmelCase			,  f'''Difference between torch and flax is {diff} (>= {tol}).'''     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   self.check_vision_text_dual_encoder_model(**_lowerCAmelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   self.check_model_from_pretrained_configs(**_lowerCAmelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   self.check_save_load(**_lowerCAmelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   self.check_vision_text_output_attention(**_lowerCAmelCase     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.get_pretrained_model_and_inputs()

   lowerCamelCase_      =  model_a(**_lowerCAmelCase     )
   lowerCamelCase_      =  outputs[0].numpy()

   with tempfile.TemporaryDirectory() as tmp_dirname:
    model_a.save_pretrained(_lowerCAmelCase     )
    lowerCamelCase_      =  TFVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase     )

    lowerCamelCase_      =  model_a(**_lowerCAmelCase     )
    lowerCamelCase_      =  after_outputs[0].numpy()
    lowerCamelCase_      =  np.amax(np.abs(out_a - out_a     )     )
    self.assertLessEqual(_lowerCAmelCase			,  1e-5     )





@require_tf
class      snake_case    (    _SCREAMING_SNAKE_CASE			,						unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
       "hf-internal-testing/tiny-random-vit"			,  "hf-internal-testing/tiny-random-bert"     )
   lowerCamelCase_      =  13
   lowerCamelCase_      =  floats_tensor(
       [
           batch_size,
           model.vision_model.config.num_channels,
           model.vision_model.config.image_size,
           model.vision_model.config.image_size,
       ]     )
   lowerCamelCase_      =  ids_tensor([batch_size, 4]			,  model.text_model.config.vocab_size     )
   lowerCamelCase_      =  random_attention_mask([batch_size, 4]     )
   lowerCamelCase_      =  {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

   return model, inputs






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFViTModel(_lowerCAmelCase			,  name="vision_model"     )
   lowerCamelCase_      =  TFBertModel(_lowerCAmelCase			,  name="text_model"     )
   return vision_model, text_model







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFViTModelTester(self     )
   lowerCamelCase_      =  TFBertModelTester(self     )
   lowerCamelCase_      =  vit_model_tester.prepare_config_and_inputs()
   lowerCamelCase_      =  bert_model_tester.prepare_config_and_inputs()

   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  vision_config_and_inputs

   (
       (
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) , 
   )      =  text_config_and_inputs

   return {
       "text_config": text_config,
       "vision_config": vision_config,
       "pixel_values": pixel_values,
       "attention_mask": input_mask,
       "input_ids": input_ids,
       "text_token_type_ids": token_type_ids,
       "text_sequence_labels": sequence_labels,
       "text_token_labels": token_labels,
       "text_choice_labels": choice_labels,
   }





@require_tf
class      snake_case    (    _SCREAMING_SNAKE_CASE			,						unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's
   # just reinitialize it.
   lowerCamelCase_      =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
       "Rocketknight1/tiny-random-deit-tf"			,  "hf-internal-testing/tiny-random-roberta"     )
   lowerCamelCase_      =  13
   lowerCamelCase_      =  floats_tensor(
       [
           batch_size,
           model.vision_model.config.num_channels,
           model.vision_model.config.image_size,
           model.vision_model.config.image_size,
       ]     )
   lowerCamelCase_      =  ids_tensor([batch_size, 4]			,  model.text_model.config.vocab_size     )
   lowerCamelCase_      =  random_attention_mask([batch_size, 4]     )
   lowerCamelCase_      =  {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

   return model, inputs






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.get_vision_text_model(_lowerCAmelCase			,  _lowerCAmelCase     )
   lowerCamelCase_      =  TFVisionTextDualEncoderModel(vision_model=_lowerCAmelCase			,  text_model=_lowerCAmelCase     )

   lowerCamelCase_      =  model(
       input_ids=_lowerCAmelCase			,  pixel_values=_lowerCAmelCase			,  attention_mask=_lowerCAmelCase			,  output_attentions=_lowerCAmelCase     )

   lowerCamelCase_      =  output.vision_model_output.attentions
   self.assertEqual(len(_lowerCAmelCase     )			,  vision_config.num_hidden_layers     )

   # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
   lowerCamelCase_      =  to_atuple(vision_model.config.image_size     )
   lowerCamelCase_      =  to_atuple(vision_model.config.patch_size     )
   lowerCamelCase_      =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
   lowerCamelCase_      =  num_patches + 2
   self.assertEqual(vision_attentions[0].shape[-3:]			,  (vision_config.num_attention_heads, seq_len, seq_len)     )

   lowerCamelCase_      =  output.text_model_output.attentions
   self.assertEqual(len(_lowerCAmelCase     )			,  text_config.num_hidden_layers     )

   self.assertEqual(
       text_attentions[0].shape[-3:]			,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,  )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFDeiTModel(_lowerCAmelCase			,  name="vision_model"     )
   lowerCamelCase_      =  TFRobertaModel(_lowerCAmelCase			,  name="text_model"     )
   return vision_model, text_model







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFDeiTModelTester(self     )
   lowerCamelCase_      =  TFRobertaModelTester(self     )
   lowerCamelCase_      =  vit_model_tester.prepare_config_and_inputs()
   lowerCamelCase_      =  bert_model_tester.prepare_config_and_inputs()

   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  vision_config_and_inputs

   (
       (
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) , 
   )      =  text_config_and_inputs

   return {
       "text_config": text_config,
       "vision_config": vision_config,
       "pixel_values": pixel_values,
       "attention_mask": input_mask,
       "input_ids": input_ids,
       "text_token_type_ids": token_type_ids,
       "text_sequence_labels": sequence_labels,
       "text_token_labels": token_labels,
       "text_choice_labels": choice_labels,
   }





@require_tf
class      snake_case    (    _SCREAMING_SNAKE_CASE			,						unittest.TestCase			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFVisionTextDualEncoderModel.from_vision_text_pretrained(
       "Rocketknight1/tiny-random-clip-tf"			,  "hf-internal-testing/tiny-random-bert"     )
   lowerCamelCase_      =  13
   lowerCamelCase_      =  floats_tensor(
       [
           batch_size,
           model.vision_model.config.num_channels,
           model.vision_model.config.image_size,
           model.vision_model.config.image_size,
       ]     )
   lowerCamelCase_      =  ids_tensor([batch_size, 4]			,  model.text_model.config.vocab_size     )
   lowerCamelCase_      =  random_attention_mask([batch_size, 4]     )
   lowerCamelCase_      =  {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

   return model, inputs






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFCLIPVisionModel(_lowerCAmelCase			,  name="vision_model"     )
   lowerCamelCase_      =  TFBertModel(_lowerCAmelCase			,  name="text_model"     )
   return vision_model, text_model







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFCLIPVisionModelTester(self     )
   lowerCamelCase_      =  TFBertModelTester(self     )
   lowerCamelCase_      =  clip_model_tester.prepare_config_and_inputs()
   lowerCamelCase_      =  bert_model_tester.prepare_config_and_inputs()

   lowerCamelCase_ ,lowerCamelCase_      =  vision_config_and_inputs

   (
       (
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) ,(
       lowerCamelCase_ 
   ) , 
   )      =  text_config_and_inputs

   return {
       "text_config": text_config,
       "vision_config": vision_config,
       "pixel_values": pixel_values,
       "attention_mask": input_mask,
       "input_ids": input_ids,
       "text_token_type_ids": token_type_ids,
       "text_sequence_labels": sequence_labels,
       "text_token_labels": token_labels,
       "text_choice_labels": choice_labels,
   }





@require_vision
@require_tf
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFVisionTextDualEncoderModel.from_pretrained(
       "clip-italian/clip-italian"			,  logit_scale_init_value=1.0			,  from_pt=_lowerCAmelCase     )
   lowerCamelCase_      =  VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"     )

   lowerCamelCase_      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
   lowerCamelCase_      =  processor(
       text=["una foto di un gatto", "una foto di un cane"]			,  images=_lowerCAmelCase			,  padding=_lowerCAmelCase			,  return_tensors="np"     )

   lowerCamelCase_      =  model(**_lowerCAmelCase     )

   # verify the logits
   self.assertEqual(outputs.logits_per_image.shape			,  (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])     )
   self.assertEqual(
       outputs.logits_per_text.shape			,  (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])			,  )

   lowerCamelCase_      =  np.array([[1.2_284_727, 0.3_104_122]]     )

   self.assertTrue(np.allclose(outputs.logits_per_image.numpy()			,  _lowerCAmelCase			,  atol=1e-3     )     )









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

import numpy as np

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	bytes			,			__UpperCAmelCase						:	int							)					-> np.array:
      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						:	int			,			__UpperCAmelCase						:	float			,			__UpperCAmelCase						:	str = "f32le"			,			)					-> int:
      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						:	int			,			__UpperCAmelCase						:	float			,			__UpperCAmelCase						:	Optional[int] = None			,			__UpperCAmelCase						:	Optional[Union[Tuple[float, float], float]] = None			,			__UpperCAmelCase						:	str = "f32le"			,			)					-> Tuple:
      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						:	Optional[Any]			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Tuple[int, int]			,			__UpperCAmelCase						:	bool = False							)					-> Union[str, Any]:
      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						:	List[str]			,			__UpperCAmelCase						:	int							)					-> Optional[Any]:
      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