






"""simple docstring"""






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset

UpperCAmelCase_     :				Dict         =				random.Random()



def       _A    (__a					,       __a=1.0					,       __a=None					,       __a=None							)				-> List[Any]:
    """simple docstring"""






    if rng is None:
        SCREAMING_SNAKE_CASE_   :						List[Any]							=	global_rng

    SCREAMING_SNAKE_CASE_   :						Tuple							=	[]
    for batch_idx in range(shape[0]							):
        values.append([]							)
        for _ in range(shape[1]							):
            values[-1].append(rng.random() * scale							)

    return values




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def __init__(		self  :						Tuple		,			lowercase_  :						Optional[int]		,			lowercase_  :						str=7		,			lowercase_  :						List[str]=400		,			lowercase_  :						List[Any]=2000		,			lowercase_  :						Dict=2048		,			lowercase_  :						Tuple=128		,			lowercase_  :						List[str]=1		,			lowercase_  :						Dict=512		,			lowercase_  :						Optional[int]=30		,			lowercase_  :						Tuple=44100		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	parent
          SCREAMING_SNAKE_CASE_   :						List[str]							=	batch_size
          SCREAMING_SNAKE_CASE_   :						List[str]							=	min_seq_length
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	max_seq_length
          SCREAMING_SNAKE_CASE_   :						int							=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	spectrogram_length
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	feature_size
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	num_audio_channels
          SCREAMING_SNAKE_CASE_   :						Dict							=	hop_length
          SCREAMING_SNAKE_CASE_   :						str							=	chunk_length
          SCREAMING_SNAKE_CASE_   :						int							=	sampling_rate
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          return {
              "spectrogram_length": self.spectrogram_length,
              "feature_size": self.feature_size,
              "num_audio_channels": self.num_audio_channels,
              "hop_length": self.hop_length,
              "chunk_length": self.chunk_length,
              "sampling_rate": self.sampling_rate,
          }



      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						Union[str, Any]=False		,			lowercase_  :						Tuple=False):




          '''simple docstring'''


          def _flatten(lowercase_  :						Tuple):
              return list(itertools.chain(*lowercase_))

          if equal_length:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
          else:
              # make sure that inputs increase in size
              SCREAMING_SNAKE_CASE_   :						Dict							=	[
                  floats_list((x, self.feature_size))
                  for x in range(self.min_seq_length		,			self.max_seq_length		,			self.seq_length_diff)
              ]
          if numpify:
              SCREAMING_SNAKE_CASE_   :						str							=	[np.asarray(lowercase_) for x in speech_inputs]
          return speech_inputs




@require_torch
@require_torchaudio
class 							lowerCAmelCase__       (    snake_case__       ,	unittest.TestCase				):






      '''simple docstring'''

      __UpperCamelCase							=						TvltFeatureExtractor
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	TvltFeatureExtractionTester(self)
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.feature_extraction_class(**self.feat_extract_dict)
          self.assertTrue(hasattr(lowercase_		,			'''spectrogram_length'''))
          self.assertTrue(hasattr(lowercase_		,			'''feature_size'''))
          self.assertTrue(hasattr(lowercase_		,			'''num_audio_channels'''))
          self.assertTrue(hasattr(lowercase_		,			'''hop_length'''))
          self.assertTrue(hasattr(lowercase_		,			'''chunk_length'''))
          self.assertTrue(hasattr(lowercase_		,			'''sampling_rate'''))
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	self.feature_extraction_class(**self.feat_extract_dict)

          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	feat_extract_first.save_pretrained(lowercase_)[0]
              check_json_file_has_correct_format(lowercase_)
              SCREAMING_SNAKE_CASE_   :						Dict							=	self.feature_extraction_class.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	feat_extract_first.to_dict()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	feat_extract_second.to_dict()
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	dict_first.pop('''mel_filters''')
          SCREAMING_SNAKE_CASE_   :						str							=	dict_second.pop('''mel_filters''')
          self.assertTrue(np.allclose(lowercase_		,			lowercase_))
          self.assertEqual(lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.feature_extraction_class(**self.feat_extract_dict)

          with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	os.path.join(lowercase_		,			'''feat_extract.json''')
              feat_extract_first.to_json_file(lowercase_)
              SCREAMING_SNAKE_CASE_   :						Dict							=	self.feature_extraction_class.from_json_file(lowercase_)

          SCREAMING_SNAKE_CASE_   :						str							=	feat_extract_first.to_dict()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	feat_extract_second.to_dict()
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	dict_first.pop('''mel_filters''')
          SCREAMING_SNAKE_CASE_   :						int							=	dict_second.pop('''mel_filters''')
          self.assertTrue(np.allclose(lowercase_		,			lowercase_))
          self.assertEqual(lowercase_		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.feature_extraction_class(**self.feat_extract_dict)

          # create three inputs of length 800, 1000, and 1200
          SCREAMING_SNAKE_CASE_   :						Any							=	[floats_list((1, x))[0] for x in range(800		,			1400		,			200)]
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[np.asarray(lowercase_) for speech_input in speech_inputs]

          # Test not batched input
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	feature_extractor(np_speech_inputs[0]		,			return_tensors='''np'''		,			sampling_rate=44100).audio_values

          self.assertTrue(encoded_audios.ndim == 4)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

          # Test batched
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	feature_extractor(lowercase_		,			return_tensors='''np'''		,			sampling_rate=44100).audio_values

          self.assertTrue(encoded_audios.ndim == 4)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

          # Test audio masking
          SCREAMING_SNAKE_CASE_   :						Tuple							=	feature_extractor(
              lowercase_		,			return_tensors='''np'''		,			sampling_rate=44100		,			mask_audio=lowercase_).audio_values

          self.assertTrue(encoded_audios.ndim == 4)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

          # Test 2-D numpy arrays are batched.
          SCREAMING_SNAKE_CASE_   :						Tuple							=	[floats_list((1, x))[0] for x in (800, 800, 800)]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.asarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						str							=	feature_extractor(lowercase_		,			return_tensors='''np'''		,			sampling_rate=44100).audio_values
          self.assertTrue(encoded_audios.ndim == 4)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)
      def _SCREAMING_SNAKE_CASE				(		self  :						str		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,			'''clean'''		,			split='''validation''')
          # automatic decoding with librispeech
          SCREAMING_SNAKE_CASE_   :						int							=	ds.sort('''id''').select(range(lowercase_))[:num_samples]["audio"]

          return [x["array"] for x in speech_samples]



      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self._load_datasamples(1)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	TvltFeatureExtractor()
          SCREAMING_SNAKE_CASE_   :						int							=	feature_extractor(lowercase_		,			return_tensors='''pt''').audio_values

          self.assertEquals(audio_values.shape		,			(1, 1, 192, 128))

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]])
          self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,			lowercase_		,			atol=1e-4))










"""simple docstring"""






from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

UpperCAmelCase_     :				Optional[Any]         =				logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__				)
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			*lowercase_  :						Any		,			**lowercase_  :						Any):




          '''simple docstring'''


          super().__init__(*lowercase_		,			**lowercase_)
          requires_backends(self		,			'''vision''')
          self.check_model_type(lowercase_)
      def __call__(		self  :						int		,			lowercase_  :						Union[str, List[str], "Image.Image", List["Image.Image"]]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


          return super().__call__(lowercase_		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			**lowercase_  :						List[str]):




          '''simple docstring'''


          return {}, {}, {}
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	load_image(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	image.size
          SCREAMING_SNAKE_CASE_   :						int							=	self.image_processor(images=lowercase_		,			return_tensors=self.framework)
          return model_inputs
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	self.model(**lowercase_)
          return model_outputs



      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	model_outputs.predicted_depth
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1)		,			size=self.image_size[::-1]		,			mode='''bicubic'''		,			align_corners=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Any							=	prediction.squeeze().cpu().numpy()
          SCREAMING_SNAKE_CASE_   :						List[str]							=	(output * 255 / np.max(lowercase_)).astype('''uint8''')
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	Image.fromarray(lowercase_)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	{}
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	predicted_depth
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	depth
          return output_dict


