


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
	from huggingface_hub import snapshot_download
	from pyctcdecode import BeamSearchDecoderCTC

	from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
	from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
	from transformers import WavaVecaForCTC






@require_pyctcdecode
class 			UpperCamelCase__(  unittest.TestCase			):




				def 	a__(						self     :					Optional[int] )->	Any:

										"""simple docstring"""




										UpperCAmelCase								=      "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
										UpperCAmelCase								=      dict(zip(lowerCAmelCase ,       range(len(lowerCAmelCase ) ) ) )

										UpperCAmelCase								=      {
										    "unk_token": "<unk>",
										    "bos_token": "<s>",
										    "eos_token": "</s>",
										}
										UpperCAmelCase								=      {
										    "feature_size": 1,
										    "padding_value": 0.0,
										    "sampling_rate": 16000,
										    "return_attention_mask": False,
										    "do_normalize": True,
										}

										UpperCAmelCase								=      tempfile.mkdtemp()
										UpperCAmelCase								=      os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES['''vocab_file'''] )
										UpperCAmelCase								=      os.path.join(self.tmpdirname ,       lowerCAmelCase )
										with open(self.vocab_file ,       '''w''' ,       encoding='''utf-8''' ) as fp:
																fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )

										with open(self.feature_extraction_file ,       '''w''' ,       encoding='''utf-8''' ) as fp:
																fp.write(json.dumps(lowerCAmelCase ) + '''\n''' )

										# load decoder from hub
										UpperCAmelCase								=      "hf-internal-testing/ngram-beam-search-decoder"




				def 	a__(						self     :					Optional[int] ,       **lowerCAmelCase     :					Union[str, Any] )->	Tuple:

										"""simple docstring"""




										UpperCAmelCase								=      self.add_kwargs_tokens_map.copy()
										kwargs.update(lowerCAmelCase )
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,       **lowerCAmelCase )




				def 	a__(						self     :					Optional[int] ,       **lowerCAmelCase     :					int )->	List[str]:

										"""simple docstring"""




										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,       **lowerCAmelCase )




				def 	a__(						self     :					Union[str, Any] ,       **lowerCAmelCase     :					int )->	int:

										"""simple docstring"""




										return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,       **lowerCAmelCase )




				def 	a__(						self     :					Union[str, Any] )->	Any:

										"""simple docstring"""




										shutil.rmtree(self.tmpdirname )




				def 	a__(						self     :					Optional[Any] )->	Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										processor.save_pretrained(self.tmpdirname )
										UpperCAmelCase								=      WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )

										# tokenizer
										self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer.get_vocab() )
										self.assertIsInstance(processor.tokenizer ,       lowerCAmelCase )

										# feature extractor
										self.assertEqual(processor.feature_extractor.to_json_string() ,       feature_extractor.to_json_string() )
										self.assertIsInstance(processor.feature_extractor ,       lowerCAmelCase )

										# decoder
										self.assertEqual(processor.decoder._alphabet.labels ,       decoder._alphabet.labels )
										self.assertEqual(
										    processor.decoder.model_container[decoder._model_key]._unigram_set ,       decoder.model_container[decoder._model_key]._unigram_set ,       )
										self.assertIsInstance(processor.decoder ,       lowerCAmelCase )




				def 	a__(						self     :					Optional[Any] )->	Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase								=      WavaVecaProcessorWithLM(
										    tokenizer=self.get_tokenizer() ,       feature_extractor=self.get_feature_extractor() ,       decoder=self.get_decoder() )
										processor.save_pretrained(self.tmpdirname )

										# make sure that error is thrown when decoder alphabet doesn't match
										UpperCAmelCase								=      WavaVecaProcessorWithLM.from_pretrained(
										    self.tmpdirname ,       alpha=5.0 ,       beta=3.0 ,       score_boundary=-7.0 ,       unk_score_offset=3 )

										# decoder
										self.assertEqual(processor.language_model.alpha ,       5.0 )
										self.assertEqual(processor.language_model.beta ,       3.0 )
										self.assertEqual(processor.language_model.score_boundary ,       -7.0 )
										self.assertEqual(processor.language_model.unk_score_offset ,       3 )




				def 	a__(						self     :					str )->	Optional[int]:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_tokenizer()
										# add token to trigger raise
										tokenizer.add_tokens(['''xx'''] )
										with self.assertRaisesRegex(lowerCAmelCase ,       '''include''' ):
																WavaVecaProcessorWithLM(
																    tokenizer=lowerCAmelCase ,       feature_extractor=self.get_feature_extractor() ,       decoder=self.get_decoder() )




				def 	a__(						self     :					Dict )->	Optional[int]:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										UpperCAmelCase								=      floats_list((3, 1000) )

										UpperCAmelCase								=      feature_extractor(lowerCAmelCase ,       return_tensors='''np''' )
										UpperCAmelCase								=      processor(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 	a__(						self     :					Optional[Any] )->	Tuple:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										UpperCAmelCase								=      "This is a test string"

										UpperCAmelCase								=      processor(text=lowerCAmelCase )

										UpperCAmelCase								=      tokenizer(lowerCAmelCase )

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




				def 	a__(						self     :					Optional[int] ,       lowerCAmelCase     :					Dict=(2, 10, 16) ,       lowerCAmelCase     :					Optional[Any]=77 )->	Optional[int]:

										"""simple docstring"""




										np.random.seed(lowerCAmelCase )
										return np.random.rand(*lowerCAmelCase )




				def 	a__(						self     :					Dict )->	str:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										UpperCAmelCase								=      self._get_dummy_logits(shape=(10, 16) ,       seed=13 )

										UpperCAmelCase								=      processor.decode(lowerCAmelCase )

										UpperCAmelCase								=      decoder.decode_beams(lowerCAmelCase )[0]

										self.assertEqual(decoded_decoder[0] ,       decoded_processor.text )
										self.assertEqual('''</s> <s> </s>''' ,       decoded_processor.text )
										self.assertEqual(decoded_decoder[-2] ,       decoded_processor.logit_score )
										self.assertEqual(decoded_decoder[-1] ,       decoded_processor.lm_score )




				@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
				def 	a__(						self     :					List[str] ,       lowerCAmelCase     :					Any )->	Optional[int]:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										UpperCAmelCase								=      self._get_dummy_logits()

										# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
										#       otherwise, the LM won't be available to the pool's sub-processes.
										# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
										if pool_context is None:
																UpperCAmelCase								=      processor.batch_decode(lowerCAmelCase )
										else:
																with get_context(lowerCAmelCase ).Pool() as pool:
																						UpperCAmelCase								=      processor.batch_decode(lowerCAmelCase ,       lowerCAmelCase )

										UpperCAmelCase								=      list(lowerCAmelCase )

										with get_context('''fork''' ).Pool() as p:
																UpperCAmelCase								=      decoder.decode_beams_batch(lowerCAmelCase ,       lowerCAmelCase )

										UpperCAmelCase								=      [], [], []
										for beams in decoded_beams:
																texts_decoder.append(beams[0][0] )
																logit_scores_decoder.append(beams[0][-2] )
																lm_scores_decoder.append(beams[0][-1] )

										self.assertListEqual(lowerCAmelCase ,       decoded_processor.text )
										self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] ,       decoded_processor.text )
										self.assertListEqual(lowerCAmelCase ,       decoded_processor.logit_score )
										self.assertListEqual(lowerCAmelCase ,       decoded_processor.lm_score )




				def 	a__(						self     :					Any )->	List[Any]:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										UpperCAmelCase								=      self._get_dummy_logits()

										UpperCAmelCase								=      15
										UpperCAmelCase								=      -20.0
										UpperCAmelCase								=      -4.0

										UpperCAmelCase								=      processor.batch_decode(
										    lowerCAmelCase ,       beam_width=lowerCAmelCase ,       beam_prune_logp=lowerCAmelCase ,       token_min_logp=lowerCAmelCase ,       )
										UpperCAmelCase								=      decoded_processor_out.text

										UpperCAmelCase								=      list(lowerCAmelCase )

										with get_context('''fork''' ).Pool() as pool:
																UpperCAmelCase								=      decoder.decode_beams_batch(
																    lowerCAmelCase ,       lowerCAmelCase ,       beam_width=lowerCAmelCase ,       beam_prune_logp=lowerCAmelCase ,       token_min_logp=lowerCAmelCase ,       )

										UpperCAmelCase								=      [d[0][0] for d in decoded_decoder_out]
										UpperCAmelCase								=      [d[0][2] for d in decoded_decoder_out]
										UpperCAmelCase								=      [d[0][3] for d in decoded_decoder_out]

										self.assertListEqual(lowerCAmelCase ,       lowerCAmelCase )
										self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] ,       lowerCAmelCase )

										self.assertTrue(np.array_equal(lowerCAmelCase ,       decoded_processor_out.logit_score ) )
										self.assertTrue(np.allclose([-20.054, -18.447] ,       lowerCAmelCase ,       atol=1E-3 ) )

										self.assertTrue(np.array_equal(lowerCAmelCase ,       decoded_processor_out.lm_score ) )
										self.assertTrue(np.allclose([-15.554, -13.9474] ,       lowerCAmelCase ,       atol=1E-3 ) )




				def 	a__(						self     :					Union[str, Any] )->	List[str]:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										UpperCAmelCase								=      self._get_dummy_logits()

										UpperCAmelCase								=      2.0
										UpperCAmelCase								=      5.0
										UpperCAmelCase								=      -20.0
										UpperCAmelCase								=      True

										UpperCAmelCase								=      processor.batch_decode(
										    lowerCAmelCase ,       alpha=lowerCAmelCase ,       beta=lowerCAmelCase ,       unk_score_offset=lowerCAmelCase ,       lm_score_boundary=lowerCAmelCase ,       )
										UpperCAmelCase								=      decoded_processor_out.text

										UpperCAmelCase								=      list(lowerCAmelCase )
										decoder.reset_params(
										    alpha=lowerCAmelCase ,       beta=lowerCAmelCase ,       unk_score_offset=lowerCAmelCase ,       lm_score_boundary=lowerCAmelCase ,       )

										with get_context('''fork''' ).Pool() as pool:
																UpperCAmelCase								=      decoder.decode_beams_batch(
																    lowerCAmelCase ,       lowerCAmelCase ,       )

										UpperCAmelCase								=      [d[0][0] for d in decoded_decoder_out]

										self.assertListEqual(lowerCAmelCase ,       lowerCAmelCase )
										self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] ,       lowerCAmelCase )
										UpperCAmelCase								=      processor.decoder.model_container[processor.decoder._model_key]
										self.assertEqual(lm_model.alpha ,       2.0 )
										self.assertEqual(lm_model.beta ,       5.0 )
										self.assertEqual(lm_model.unk_score_offset ,       -20.0 )
										self.assertEqual(lm_model.score_boundary ,       lowerCAmelCase )




				def 	a__(						self     :					Optional[int] )->	int:

										"""simple docstring"""




										UpperCAmelCase								=      WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )

										UpperCAmelCase								=      processor.decoder.model_container[processor.decoder._model_key]
										UpperCAmelCase								=      Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()

										UpperCAmelCase								=      os.listdir(lowerCAmelCase )
										UpperCAmelCase								=      ["alphabet.json", "language_model"]

										downloaded_decoder_files.sort()
										expected_decoder_files.sort()

										# test that only decoder relevant files from
										# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
										# are downloaded and none of the rest (e.g. README.md, ...)
										self.assertListEqual(lowerCAmelCase ,       lowerCAmelCase )




				def 	a__(						self     :					Any )->	Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase								=      snapshot_download('''hf-internal-testing/processor_with_lm''' )

										UpperCAmelCase								=      WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase )

										UpperCAmelCase								=      processor.decoder.model_container[processor.decoder._model_key]
										UpperCAmelCase								=      Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()

										UpperCAmelCase								=      os.listdir(lowerCAmelCase )
										UpperCAmelCase								=      os.listdir(lowerCAmelCase )

										local_decoder_files.sort()
										expected_decoder_files.sort()

										# test that both decoder form hub and local files in cache are the same
										self.assertListEqual(lowerCAmelCase ,       lowerCAmelCase )




				def 	a__(						self     :					Optional[int] )->	List[Any]:

										"""simple docstring"""




										UpperCAmelCase								=      WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
										UpperCAmelCase								=      AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )

										UpperCAmelCase								=      floats_list((3, 1000) )

										UpperCAmelCase								=      processor_wavaveca(lowerCAmelCase ,       return_tensors='''np''' )
										UpperCAmelCase								=      processor_auto(lowerCAmelCase ,       return_tensors='''np''' )

										for key in input_wavaveca.keys():
																self.assertAlmostEqual(input_wavaveca[key].sum() ,       input_auto[key].sum() ,       delta=1E-2 )

										UpperCAmelCase								=      self._get_dummy_logits()

										UpperCAmelCase								=      processor_wavaveca.batch_decode(lowerCAmelCase )
										UpperCAmelCase								=      processor_auto.batch_decode(lowerCAmelCase )

										self.assertListEqual(decoded_wavaveca.text ,       decoded_auto.text )




				def 	a__(						self     :					int )->	Optional[int]:

										"""simple docstring"""




										UpperCAmelCase								=      self.get_feature_extractor()
										UpperCAmelCase								=      self.get_tokenizer()
										UpperCAmelCase								=      self.get_decoder()

										UpperCAmelCase								=      WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase ,       feature_extractor=lowerCAmelCase ,       decoder=lowerCAmelCase )

										self.assertListEqual(
										    processor.model_input_names ,       feature_extractor.model_input_names ,       msg='''`processor` and `feature_extractor` model input names do not match''' ,       )




				@staticmethod
				def 	a__(						lowerCAmelCase     :					Dict ,       lowerCAmelCase     :					Dict )->	int:

										"""simple docstring"""




										UpperCAmelCase								=      [d[key] for d in offsets]
										return retrieved_list




				def 	a__(						self     :					Optional[int] )->	Any:

										"""simple docstring"""




										UpperCAmelCase								=      WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
										UpperCAmelCase								=      self._get_dummy_logits()[0]

										UpperCAmelCase								=      processor.decode(lowerCAmelCase ,       output_word_offsets=lowerCAmelCase )
										# check Wav2Vec2CTCTokenizerOutput keys for word
										self.assertEqual(len(outputs.keys() ) ,       4 )
										self.assertTrue('''text''' in outputs )
										self.assertTrue('''word_offsets''' in outputs )
										self.assertTrue(isinstance(lowerCAmelCase ,       lowerCAmelCase ) )

										self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] ,       '''word''' ) ) ,       outputs.text )
										self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] ,       '''word''' ) ,       ['''<s>''', '''<s>''', '''</s>'''] )
										self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] ,       '''start_offset''' ) ,       [0, 2, 4] )
										self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] ,       '''end_offset''' ) ,       [1, 3, 5] )




				def 	a__(						self     :					str )->	Tuple:

										"""simple docstring"""




										UpperCAmelCase								=      WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
										UpperCAmelCase								=      self._get_dummy_logits()

										UpperCAmelCase								=      processor.batch_decode(lowerCAmelCase ,       output_word_offsets=lowerCAmelCase )

										# check Wav2Vec2CTCTokenizerOutput keys for word
										self.assertEqual(len(outputs.keys() ) ,       4 )
										self.assertTrue('''text''' in outputs )
										self.assertTrue('''word_offsets''' in outputs )
										self.assertTrue(isinstance(lowerCAmelCase ,       lowerCAmelCase ) )

										self.assertListEqual(
										    [''' '''.join(self.get_from_offsets(lowerCAmelCase ,       '''word''' ) ) for o in outputs['''word_offsets''']] ,       outputs.text )
										self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] ,       '''word''' ) ,       ['''<s>''', '''<s>''', '''</s>'''] )
										self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] ,       '''start_offset''' ) ,       [0, 2, 4] )
										self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] ,       '''end_offset''' ) ,       [1, 3, 5] )





				@slow
				@require_torch
				@require_torchaudio
				def 	a__(						self     :					int )->	str:

										"""simple docstring"""




										import torch

										UpperCAmelCase								=      load_dataset('''common_voice''' ,       '''en''' ,       split='''train''' ,       streaming=lowerCAmelCase )
										UpperCAmelCase								=      ds.cast_column('''audio''' ,       datasets.Audio(sampling_rate=16000 ) )
										UpperCAmelCase								=      iter(lowerCAmelCase )
										UpperCAmelCase								=      next(lowerCAmelCase )

										UpperCAmelCase								=      AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
										UpperCAmelCase								=      WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )

										# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
										UpperCAmelCase								=      processor(sample['''audio''']['''array'''] ,       return_tensors='''pt''' ).input_values

										with torch.no_grad():
																UpperCAmelCase								=      model(lowerCAmelCase ).logits.cpu().numpy()

										UpperCAmelCase								=      processor.decode(logits[0] ,       output_word_offsets=lowerCAmelCase )

										UpperCAmelCase								=      model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
										UpperCAmelCase								=      [
										    {
										        "start_time": d["start_offset"] * time_offset,
										        "end_time": d["end_offset"] * time_offset,
										        "word": d["word"],
										    }
										    for d in output["word_offsets"]
										]

										UpperCAmelCase								=      "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

										# output words
										self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase ,       '''word''' ) ) ,       lowerCAmelCase )
										self.assertEqual(''' '''.join(self.get_from_offsets(lowerCAmelCase ,       '''word''' ) ) ,       output.text )

										# output times
										UpperCAmelCase								=      torch.tensor(self.get_from_offsets(lowerCAmelCase ,       '''start_time''' ) )
										UpperCAmelCase								=      torch.tensor(self.get_from_offsets(lowerCAmelCase ,       '''end_time''' ) )

										# fmt: off
										UpperCAmelCase								=      torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
										UpperCAmelCase								=      torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
										# fmt: on

										self.assertTrue(torch.allclose(lowerCAmelCase ,       lowerCAmelCase ,       atol=0.01 ) )
										self.assertTrue(torch.allclose(lowerCAmelCase ,       lowerCAmelCase ,       atol=0.01 ) )







from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_						:     Any	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: Dict      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,	**snake_case				: Optional[int]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	str           =   size if size is not None else {"shortest_edge": 224}
							_lowercase       :	List[str]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Optional[int]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	List[Any]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Optional[int]           =   do_resize
							_lowercase       :	List[str]           =   size
							_lowercase       :	Tuple           =   resample
							_lowercase       :	Union[str, Any]           =   do_center_crop
							_lowercase       :	List[str]           =   crop_size
							_lowercase       :	Tuple           =   do_rescale
							_lowercase       :	Union[str, Any]           =   rescale_factor
							_lowercase       :	str           =   do_normalize
							_lowercase       :	List[Any]           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_lowercase       :	List[Any]           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: str      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
													_lowercase       :	List[str]           =   int((256 / 224) * size["shortest_edge"]    )
													_lowercase       :	Dict           =   get_resize_output_image_size(snake_case      ,	size=snake_case      ,	default_to_square=snake_case    )
													_lowercase       :	int           =   {"height": output_size[0], "width": output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
													raise ValueError(
													    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""    )
							return resize(
							    snake_case      ,	size=(size_dict["height"], size_dict["width"])      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Any      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case    )
							if "height" not in size or "width" not in size:
													raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""    )
							return center_crop(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: np.ndarray      ,	snake_case				: Union[int, float]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[str]      ,	) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Optional[int]      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: ImageInput      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[float] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[TensorType] = None      ,	snake_case				: ChannelDimension = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			BatchFeature:
							_lowercase       :	Optional[Any]           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	Any           =   resample if resample is not None else self.resample
							_lowercase       :	Optional[Any]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	Union[str, Any]           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	Optional[Any]           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	str           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Dict           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	Dict           =   image_std if image_std is not None else self.image_std

							_lowercase       :	Optional[int]           =   size if size is not None else self.size
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Dict           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Dict           =   make_list_of_images(snake_case    )

							if not valid_images(snake_case    ):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."    )

							if do_resize and size is None:
													raise ValueError("Size must be specified if do_resize is True."    )

							if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."    )

							if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."    )

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."    )

							# All transformations expect numpy arrays.
							_lowercase       :	Dict           =   [to_numpy_array(snake_case    ) for image in images]

							if do_resize:
													_lowercase       :	List[Any]           =   [self.resize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							if do_center_crop:
													_lowercase       :	Union[str, Any]           =   [self.center_crop(snake_case      ,	snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	List[str]           =   [self.rescale(snake_case      ,	snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	str           =   [self.normalize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Any           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	str           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )




