




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
  from transformers import (
      FlaxBertModel,
      FlaxCLIPVisionModel,
      FlaxVisionTextDualEncoderModel,
      FlaxViTModel,
      VisionTextDualEncoderConfig,
      VisionTextDualEncoderProcessor,
  )
  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )


if is_torch_available():
  import torch

  from transformers import VisionTextDualEncoderModel

if is_vision_available():
  from PIL import Image



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[Any]	):




 """simple docstring"""







 if isinstance(_lowerCAmelCase						,							collections.abc.Iterable	):
  return x
 return (x, x)
@require_flax
class 			_UpperCamelCase      :
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Any      )     ->			List[str]:
       pass
      def 	UpperCAmelCase_						(			self   :str      )     ->			Tuple:
       pass
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Optional[int]:
       pass
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :float      )     ->			Optional[int]:
       UpperCAmelCase__							 =						np.abs((a - b)      ).max()
       self.assertLessEqual(lowerCamelCase ,      lowerCamelCase ,      f'''Difference between torch and flax is {diff} (>= {tol}).'''      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :str=None ,      **lowerCamelCase   :str      )     ->			Tuple:
       UpperCAmelCase__							 =						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase ,      lowerCamelCase      )

       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel(lowerCamelCase      )

       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :int ,      lowerCamelCase   :Tuple ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Optional[int]=None ,      **lowerCamelCase   :Optional[Any]      )     ->			Optional[int]:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.get_vision_text_model(lowerCamelCase ,      lowerCamelCase      )
       UpperCAmelCase__							 =						{"vision_model": vision_model, "text_model": text_model}
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase      )

       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Dict ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]=None ,      **lowerCamelCase   :int      )     ->			Tuple:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.get_vision_text_model(lowerCamelCase ,      lowerCamelCase      )
       UpperCAmelCase__							 =						{"vision_model": vision_model, "text_model": text_model}
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase      )

       UpperCAmelCase__							 =						model(input_ids=lowerCamelCase ,      pixel_values=lowerCamelCase ,      attention_mask=lowerCamelCase      )
       UpperCAmelCase__							 =						output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(lowerCamelCase      )
        UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase      )

        UpperCAmelCase__							 =						model(input_ids=lowerCamelCase ,      pixel_values=lowerCamelCase ,      attention_mask=lowerCamelCase      )
        UpperCAmelCase__							 =						after_output[0]
        UpperCAmelCase__							 =						np.amax(np.abs(out_a - out_a      )      )
        self.assertLessEqual(lowerCamelCase ,      1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :List[Any]=None ,      **lowerCamelCase   :Tuple      )     ->			Tuple:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.get_vision_text_model(lowerCamelCase ,      lowerCamelCase      )
       UpperCAmelCase__							 =						{"vision_model": vision_model, "text_model": text_model}
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase      )

       UpperCAmelCase__							 =						model(
           input_ids=lowerCamelCase ,      pixel_values=lowerCamelCase ,      attention_mask=lowerCamelCase ,      output_attentions=lowerCamelCase      )

       UpperCAmelCase__							 =						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)
       UpperCAmelCase__							 =						to_atuple(vision_model.config.image_size      )
       UpperCAmelCase__							 =						to_atuple(vision_model.config.patch_size      )
       UpperCAmelCase__							 =						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       UpperCAmelCase__							 =						num_patches + 1
       self.assertEqual(vision_attentions[0].shape[-3:] ,      (vision_config.num_attention_heads, seq_len, seq_len)      )

       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :str ,      lowerCamelCase   :List[str]      )     ->			Dict:
       pt_model.to(lowerCamelCase      )
       pt_model.eval()

       # prepare inputs
       UpperCAmelCase__							 =						inputs_dict
       UpperCAmelCase__							 =						{k: torch.tensor(v.tolist()      ) for k, v in flax_inputs.items()}

       with torch.no_grad():
        UpperCAmelCase__							 =						pt_model(**lowerCamelCase      ).to_tuple()

       UpperCAmelCase__							 =						fx_model(**lowerCamelCase      ).to_tuple()
       self.assertEqual(len(lowerCamelCase      ) ,      len(lowerCamelCase      ) ,      "Output lengths differ between Flax and PyTorch"      )
       for fx_output, pt_output in zip(fx_outputs[:4] ,      pt_outputs[:4]      ):
        self.assert_almost_equals(lowerCamelCase ,      pt_output.numpy() ,      4e-2      )

       # PT -> Flax
       with tempfile.TemporaryDirectory() as tmpdirname:
        pt_model.save_pretrained(lowerCamelCase      )
        UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase ,      from_pt=lowerCamelCase      )

       UpperCAmelCase__							 =						fx_model_loaded(**lowerCamelCase      ).to_tuple()
       self.assertEqual(len(lowerCamelCase      ) ,      len(lowerCamelCase      ) ,      "Output lengths differ between Flax and PyTorch"      )
       for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] ,      pt_outputs[:4]      ):
        self.assert_almost_equals(lowerCamelCase ,      pt_output.numpy() ,      4e-2      )

       # Flax -> PT
       with tempfile.TemporaryDirectory() as tmpdirname:
        fx_model.save_pretrained(lowerCamelCase      )
        UpperCAmelCase__							 =						VisionTextDualEncoderModel.from_pretrained(lowerCamelCase ,      from_flax=lowerCamelCase      )

       pt_model_loaded.to(lowerCamelCase      )
       pt_model_loaded.eval()

       with torch.no_grad():
        UpperCAmelCase__							 =						pt_model_loaded(**lowerCamelCase      ).to_tuple()

       self.assertEqual(len(lowerCamelCase      ) ,      len(lowerCamelCase      ) ,      "Output lengths differ between Flax and PyTorch"      )
       for fx_output, pt_output_loaded in zip(fx_outputs[:4] ,      pt_outputs_loaded[:4]      ):
        self.assert_almost_equals(lowerCamelCase ,      pt_output_loaded.numpy() ,      4e-2      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase ,      lowerCamelCase      )

       UpperCAmelCase__							 =						VisionTextDualEncoderModel(lowerCamelCase      )
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel(lowerCamelCase      )

       UpperCAmelCase__							 =						convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,      lowerCamelCase      )
       UpperCAmelCase__							 =						fx_state

       self.check_pt_flax_equivalence(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :int ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :List[Any]      )     ->			str:
       UpperCAmelCase__							 =						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase ,      lowerCamelCase      )

       UpperCAmelCase__							 =						VisionTextDualEncoderModel(lowerCamelCase      )
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel(lowerCamelCase      )

       UpperCAmelCase__							 =						load_flax_weights_in_pytorch_model(lowerCamelCase ,      fx_model.params      )

       self.check_pt_flax_equivalence(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			List[str]:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			Dict:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       self.check_save_load(**lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			str:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**lowerCamelCase      )
      @is_pt_flax_cross_test
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			str:
       UpperCAmelCase__							 =						self.prepare_config_and_inputs()
       UpperCAmelCase__							 =						config_inputs_dict.pop("vision_config"      )
       UpperCAmelCase__							 =						config_inputs_dict.pop("text_config"      )

       UpperCAmelCase__							 =						config_inputs_dict

       self.check_equivalence_pt_to_flax(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
       self.check_equivalence_flax_to_pt(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )







      @slow
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			List[str]:
       UpperCAmelCase__ , UpperCAmelCase__							 =						self.get_pretrained_model_and_inputs()

       UpperCAmelCase__							 =						model_a(**lowerCamelCase      )
       UpperCAmelCase__							 =						outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
        model_a.save_pretrained(lowerCamelCase      )
        UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase      )

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



@require_flax
class 			_UpperCamelCase      (				lowerCAmelCase	,       unittest.TestCase		):
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Tuple:
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           "hf-internal-testing/tiny-random-vit" ,      "hf-internal-testing/tiny-bert" ,      vision_from_pt=lowerCamelCase ,      text_from_pt=lowerCamelCase ,      )
       UpperCAmelCase__							 =						13
       UpperCAmelCase__							 =						floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]      )
       UpperCAmelCase__							 =						ids_tensor([batch_size, 4] ,      model.config.text_config.vocab_size      )
       UpperCAmelCase__							 =						random_attention_mask([batch_size, 4]      )
       UpperCAmelCase__							 =						{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

       return model, inputs
      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						FlaxViTModel(lowerCamelCase      )
       UpperCAmelCase__							 =						FlaxBertModel(lowerCamelCase      )
       return vision_model, text_model







      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Tuple:
       UpperCAmelCase__							 =						FlaxViTModelTester(self      )
       UpperCAmelCase__							 =						FlaxBertModelTester(self      )
       UpperCAmelCase__							 =						vit_model_tester.prepare_config_and_inputs()
       UpperCAmelCase__							 =						bert_model_tester.prepare_config_and_inputs()

       UpperCAmelCase__ , UpperCAmelCase__							 =						vision_config_and_inputs

       UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }



@require_torch
class 			_UpperCamelCase      (				lowerCAmelCase	,       unittest.TestCase		):
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           "hf-internal-testing/tiny-random-clip" ,      "hf-internal-testing/tiny-bert" ,      vision_from_pt=lowerCamelCase ,      text_from_pt=lowerCamelCase ,      )
       UpperCAmelCase__							 =						13
       UpperCAmelCase__							 =						floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]      )
       UpperCAmelCase__							 =						ids_tensor([batch_size, 4] ,      model.config.text_config.vocab_size      )
       UpperCAmelCase__							 =						random_attention_mask([batch_size, 4]      )
       UpperCAmelCase__							 =						{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

       return model, inputs
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :List[str]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						FlaxCLIPVisionModel(lowerCamelCase      )
       UpperCAmelCase__							 =						FlaxBertModel(lowerCamelCase      )
       return vision_model, text_model







      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[str]:
       UpperCAmelCase__							 =						FlaxCLIPVisionModelTester(self      )
       UpperCAmelCase__							 =						FlaxBertModelTester(self      )
       UpperCAmelCase__							 =						clip_model_tester.prepare_config_and_inputs()
       UpperCAmelCase__							 =						bert_model_tester.prepare_config_and_inputs()

       UpperCAmelCase__ , UpperCAmelCase__							 =						vision_config_and_inputs

       UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__							 =						text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }



@require_flax
@require_vision
class 			_UpperCamelCase      (				unittest.TestCase		):
      @slow
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			List[str]:
       UpperCAmelCase__							 =						FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian" ,      logit_scale_init_value=1.0      )
       UpperCAmelCase__							 =						VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"      )

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

       UpperCAmelCase__							 =						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]) ,      )

       UpperCAmelCase__							 =						np.array([[1.2_28_47_27, 0.3_10_41_22]]      )

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











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


_lowerCAmelCase						:   Any		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""input_values""", """padding_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :int = 1 ,      lowerCamelCase   :int = 2_4000 ,      lowerCamelCase   :float = 0.0 ,      lowerCamelCase   :float = None ,      lowerCamelCase   :float = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			str:
       super().__init__(feature_size=lowerCamelCase ,      sampling_rate=lowerCamelCase ,      padding_value=lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						chunk_length_s
       UpperCAmelCase__							 =						overlap
      @property
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       if self.chunk_length_s is None:
        return None
       else:
        return int(self.chunk_length_s * self.sampling_rate      )
      @property
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       if self.chunk_length_s is None or self.overlap is None:
        return None
       else:
        return max(1 ,      int((1.0 - self.overlap) * self.chunk_length      )      )







      def __call__(			self   :List[Any] ,      lowerCamelCase   :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCamelCase   :Optional[Union[bool, str, PaddingStrategy]] = None ,      lowerCamelCase   :Optional[bool] = False ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :Optional[int] = None ,      )     ->			BatchFeature:
       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 audio 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."      )

       if padding and truncation:
        raise ValueError("Both padding and truncation were set. Make sure you only set one."      )
       elif padding is None:
        # by default let's pad the inputs
        UpperCAmelCase__							 =						True

       UpperCAmelCase__							 =						bool(
           isinstance(lowerCamelCase ,      (list, tuple)      ) and (isinstance(raw_audio[0] ,      (np.ndarray, tuple, list)      ))      )

       if is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ).T for audio in raw_audio]
       elif not is_batched and not isinstance(lowerCamelCase ,      np.ndarray      ):
        UpperCAmelCase__							 =						np.asarray(lowerCamelCase ,      dtype=np.floataa      )
       elif isinstance(lowerCamelCase ,      np.ndarray      ) and raw_audio.dtype is np.dtype(np.floataa      ):
        UpperCAmelCase__							 =						raw_audio.astype(np.floataa      )

       # always return batch
       if not is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase      ).T]

       # verify inputs are valid
       for idx, example in enumerate(lowerCamelCase      ):
        if example.ndim > 2:
         raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''      )
        if self.feature_size == 1 and example.ndim != 1:
         raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''      )
        if self.feature_size == 2 and example.shape[-1] != 2:
         raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						BatchFeature({"input_values": raw_audio}      )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
        if truncation:
         UpperCAmelCase__							 =						min(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.floor(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
         UpperCAmelCase__							 =						max(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.ceil(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
         UpperCAmelCase__							 =						"max_length"
        else:
         UpperCAmelCase__							 =						input_values

        # normal padding on batch
       if padded_inputs is None:
        UpperCAmelCase__							 =						self.pad(
            lowerCamelCase ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      padding=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      )
        if padding:
         UpperCAmelCase__							 =						padded_inputs.pop("attention_mask"      )

       UpperCAmelCase__							 =						[]
       for example in padded_inputs.pop("input_values"      ):
        if self.feature_size == 1:
         UpperCAmelCase__							 =						example[..., None]
        input_values.append(example.T      )

       UpperCAmelCase__							 =						input_values
       if return_tensors is not None:
        UpperCAmelCase__							 =						padded_inputs.convert_to_tensors(lowerCamelCase      )

       return padded_inputs





