






"""simple docstring"""



import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


_UpperCamelCase						:    Optional[Any]		=						random.Random()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Union[str, Any]=1.0						,   __snake_case     :    Optional[int]=None						,   __snake_case     :    Dict=None       ):





  '''simple docstring'''





  if rng is None:
    lowercase				       =			global_rng

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

  return values

@require_torch
class 			a	(  unittest.TestCase      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=7				,						_lowerCamelCase=4_0_0				,						_lowerCamelCase=2_0_0_0				,						_lowerCamelCase=1				,						_lowerCamelCase=0.0				,						_lowerCamelCase=2_4_0_0_0				,						_lowerCamelCase=True				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			min_seq_length
      lowercase				       =			max_seq_length
      lowercase				       =			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      lowercase				       =			feature_size
      lowercase				       =			padding_value
      lowercase				       =			sampling_rate
      lowercase				       =			return_attention_mask







    def      UpperCamelCase_ (     self			):
      return {
          "feature_size": self.feature_size,
          "padding_value": self.padding_value,
          "sampling_rate": self.sampling_rate,
          "return_attention_mask": self.return_attention_mask,
      }







    def      UpperCamelCase_ (     self				,						_lowerCamelCase=False				,						_lowerCamelCase=False			):
      def _flatten(_lowerCamelCase			):
        return list(itertools.chain(*_lowerCamelCase			)			)

      if equal_length:
        lowercase				       =			floats_list((self.batch_size, self.max_seq_length)			)
      else:
        # make sure that inputs increase in size
        lowercase				       =			[
            _flatten(floats_list((x, self.feature_size)			)			)
            for x in range(self.min_seq_length				,						self.max_seq_length				,						self.seq_length_diff			)
        ]

      if numpify:
        lowercase				       =			[np.asarray(_lowerCamelCase			) for x in audio_inputs]

      return audio_inputs


@require_torch
class 			a	(  a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Optional[int]      	=EncodecFeatureExtractor







    def      UpperCamelCase_ (     self			):
      lowercase				       =			EnCodecFeatureExtractionTester(self			)







    def      UpperCamelCase_ (     self			):
      # Tests that all call wrap to encode_plus and batch_encode_plus
      lowercase				       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      # create three inputs of length 800, 1000, and 1200
      lowercase				       =			[floats_list((1, x)			)[0] for x in range(8_0_0				,						1_4_0_0				,						2_0_0			)]
      lowercase				       =			[np.asarray(_lowerCamelCase			) for audio_input in audio_inputs]

      # Test not batched input
      lowercase				       =			feat_extract(audio_inputs[0]				,						return_tensors='np'			).input_values
      lowercase				       =			feat_extract(np_audio_inputs[0]				,						return_tensors='np'			).input_values
      self.assertTrue(np.allclose(_lowerCamelCase				,						_lowerCamelCase				,						atol=1e-3			)			)

      # Test batched
      lowercase				       =			feat_extract(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='np'			).input_values
      lowercase				       =			feat_extract(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='np'			).input_values
      for enc_seq_a, enc_seq_a in zip(_lowerCamelCase				,						_lowerCamelCase			):
        self.assertTrue(np.allclose(_lowerCamelCase				,						_lowerCamelCase				,						atol=1e-3			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
      lowercase				       =			np.random.rand(1_0_0			).astype(np.floataa			)
      lowercase				       =			np_audio_inputs.tolist()

      for inputs in [py_audio_inputs, np_audio_inputs]:
        lowercase				       =			feature_extractor.pad([{'input_values': inputs}]				,						return_tensors='np'			)
        self.assertTrue(np_processed.input_values.dtype == np.floataa			)
        lowercase				       =			feature_extractor.pad([{'input_values': inputs}]				,						return_tensors='pt'			)
        self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      from datasets import load_dataset

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

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







    def      UpperCamelCase_ (     self			):
      # fmt: off
      lowercase				       =			torch.tensor(
          [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
           3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
           2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
           4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
           7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
           4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
      # fmt: on
      lowercase				       =			self._load_datasamples(1			)
      lowercase				       =			EncodecFeatureExtractor()
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(input_values.shape				,						(1, 1, 9_3_6_8_0)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]				,						_lowerCamelCase				,						atol=1e-6			)			)







    def      UpperCamelCase_ (     self			):
      # fmt: off
      lowercase				       =			torch.tensor(
          [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
           3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
           2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
           4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
           7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
           4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]			)
      # fmt: on
      lowercase				       =			self._load_datasamples(1			)
      lowercase				       =			[np.tile(input_audio[0][None]				,						reps=(2, 1)			)]
      input_audio[0][1] *= 0.5
      lowercase				       =			EncodecFeatureExtractor(feature_size=2			)
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(input_values.shape				,						(1, 2, 9_3_6_8_0)			)
      self.assertTrue(torch.allclose(input_values[0, 0, :3_0]				,						_lowerCamelCase				,						atol=1e-6			)			)
      self.assertTrue(torch.allclose(input_values[0, 1, :3_0]				,						EXPECTED_INPUT_VALUES * 0.5				,						atol=1e-6			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self._load_datasamples(2			)
      # would be easier if the stride was like
      lowercase				       =			EncodecFeatureExtractor(feature_size=1				,						chunk_length_s=1				,						overlap=0.0_1			)

      # pad and trunc raise an error ?
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Both padding and truncation were set. Make sure you only set one.$'				,						):
        lowercase				       =			feature_extractor(
            _lowerCamelCase				,						padding='max_length'				,						truncation=_lowerCamelCase				,						return_tensors='pt'			).input_values

      # truncate to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						truncation=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 7_1_5_2_0)			)  # 2 chunks

      # force truncate to max_length
      lowercase				       =			feature_extractor(
          _lowerCamelCase				,						truncation=_lowerCamelCase				,						max_length=4_8_0_0_0				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 4_8_0_0_0)			)

      # pad to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(padded_outputs.shape				,						(2, 1, 9_5_2_8_0)			)

      # pad to chunk
      lowercase				       =			feature_extractor(_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 9_5_2_8_0)			)

      # force pad to max length
      lowercase				       =			feature_extractor(
          _lowerCamelCase				,						padding='max_length'				,						max_length=1_0_0_0_0_0				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(2, 1, 1_0_0_0_0_0)			)

      # force no pad
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'				,						):
        lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)

      # no pad if no chunk_length_s
      lowercase				       =			None
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'				,						):
        lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)

      # no pad if no overlap
      lowercase				       =			2
      lowercase				       =			None
      with self.assertRaisesRegex(
          _lowerCamelCase				,						'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'				,						):
        lowercase				       =			feature_extractor(_lowerCamelCase				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values

      lowercase				       =			feature_extractor(input_audio[0]				,						padding=_lowerCamelCase				,						return_tensors='pt'			).input_values
      self.assertEquals(truncated_outputs.shape				,						(1, 1, 9_3_6_8_0)			)










"""simple docstring"""



import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import FlavaImageProcessor, FlavaProcessor
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )

@require_vision
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			tempfile.mkdtemp()

      # fmt: off
      lowercase				       =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)

      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(''.join([x + '\n' for x in vocab_tokens]			)			)

      lowercase				       =			{
          'image_mean': FLAVA_IMAGE_MEAN,
          'image_std': FLAVA_IMAGE_STD,
          'do_normalize': True,
          'do_resize': True,
          'size': 2_2_4,
          'do_center_crop': True,
          'crop_size': 2_2_4,
          'input_size_patches': 1_4,
          'total_mask_patches': 7_5,
          'mask_group_max_patches': None,
          'mask_group_min_patches': 1_6,
          'mask_group_min_aspect_ratio': 0.3,
          'mask_group_max_aspect_ratio': None,
          'codebook_do_resize': True,
          'codebook_size': 1_1_2,
          'codebook_do_center_crop': True,
          'codebook_crop_size': 1_1_2,
          'codebook_do_map_pixels': True,
          'codebook_do_normalize': True,
          'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
          'codebook_image_std': FLAVA_CODEBOOK_STD,
      }

      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.image_processor_file				,						'w'				,						encoding='utf-8'			) as fp:
        json.dump(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizerFast.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return FlavaImageProcessor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta			)]

      lowercase				       =			[Image.fromarray(np.moveaxis(_lowerCamelCase				,						0				,						-1			)			) for x in image_inputs]

      return image_inputs







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_rust_tokenizer()
      lowercase				       =			self.get_image_processor()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_slow.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname				,						use_fast=_lowerCamelCase			)

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_fast.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname			)

      self.assertEqual(processor_slow.tokenizer.get_vocab()				,						tokenizer_slow.get_vocab()			)
      self.assertEqual(processor_fast.tokenizer.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertEqual(tokenizer_slow.get_vocab()				,						tokenizer_fast.get_vocab()			)
      self.assertIsInstance(processor_slow.tokenizer				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor_slow.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertEqual(processor_fast.image_processor.to_json_string()				,						image_processor.to_json_string()			)
      self.assertIsInstance(processor_slow.image_processor				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			FlavaProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_image_processor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			FlavaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      self.assertEqual(processor.tokenizer.get_vocab()				,						tokenizer_add_kwargs.get_vocab()			)
      self.assertIsInstance(processor.tokenizer				,						_lowerCamelCase			)

      self.assertEqual(processor.image_processor.to_json_string()				,						image_processor_add_kwargs.to_json_string()			)
      self.assertIsInstance(processor.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			image_processor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)

      # With rest of the args
      random.seed(1_2_3_4			)
      lowercase				       =			image_processor(
          _lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)
      random.seed(1_2_3_4			)
      lowercase				       =			processor(
          images=_lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()				,						input_processor[key].sum()				,						delta=1e-2			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]				,						encoded_processor[key]			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']			)

      # add extra args
      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_image_mask=_lowerCamelCase			)

      self.assertListEqual(
          list(inputs.keys()			)				,						[
              'input_ids',
              'token_type_ids',
              'attention_mask',
              'pixel_values',
              'codebook_pixel_values',
              'bool_masked_pos',
          ]				,						)

      # test if it raises when no input is passed
      with pytest.raises(_lowerCamelCase			):
        processor()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

      self.assertListEqual(list(inputs.keys()			)				,						processor.model_input_names			)


