





'''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			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				Dict		=			{
							    """unk_token""": """<unk>""",
							    """bos_token""": """<s>""",
							    """eos_token""": """</s>""",
							}
							SCREAMING_SNAKE_CASE :				Dict		=			{
							    """feature_size""": 1,
							    """padding_value""": 0.0,
							    """sampling_rate""": 1_60_00,
							    """return_attention_mask""": False,
							    """do_normalize""": True,
							}

							SCREAMING_SNAKE_CASE :				int		=			tempfile.mkdtemp()
							SCREAMING_SNAKE_CASE :				Tuple		=			os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["""vocab_file"""]						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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
							SCREAMING_SNAKE_CASE :				Any		=			"""hf-internal-testing/ngram-beam-search-decoder"""


					def lowerCamelCase_			(						self				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.add_kwargs_tokens_map.copy()
							kwargs.update(lowerCamelCase_						)
							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[Any]      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							shutil.rmtree(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				Optional[int]		=			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_      ,						feature_extractor=lowerCamelCase_      ,						decoder=lowerCamelCase_						)

							processor.save_pretrained(self.tmpdirname						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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 lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			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
							SCREAMING_SNAKE_CASE :				List[Any]		=			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 lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			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 lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				Any		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				Optional[int]		=			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_      ,						feature_extractor=lowerCamelCase_      ,						decoder=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			floats_list((3, 10_00)						)

							SCREAMING_SNAKE_CASE :				str		=			feature_extractor(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				Dict		=			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 lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				Any		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_      ,						feature_extractor=lowerCamelCase_      ,						decoder=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			"""This is a test string"""

							SCREAMING_SNAKE_CASE :				Tuple		=			processor(text=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			tokenizer(lowerCamelCase_						)

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


					def lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			Union[str, Any]=(2, 10, 16)      ,						lowerCamelCase_				:			Any=77						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				str		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				int		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				List[str]		=			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_      ,						feature_extractor=lowerCamelCase_      ,						decoder=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			self._get_dummy_logits(shape=(10, 16)      ,						seed=13						)

							SCREAMING_SNAKE_CASE :				Dict		=			processor.decode(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Tuple		=			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 lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				str		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				str		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				Tuple		=			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_      ,						feature_extractor=lowerCamelCase_      ,						decoder=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			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:
									SCREAMING_SNAKE_CASE :				Tuple		=			processor.batch_decode(lowerCamelCase_						)
							else:
									with get_context(lowerCamelCase_						).Pool() as pool:
											SCREAMING_SNAKE_CASE :				Any		=			processor.batch_decode(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			list(lowerCamelCase_						)

							with get_context("""fork"""						).Pool() as p:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			decoder.decode_beams_batch(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				List[Any]		=			[], [], []
							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 lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				Any		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				int		=			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_      ,						feature_extractor=lowerCamelCase_      ,						decoder=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			self._get_dummy_logits()

							SCREAMING_SNAKE_CASE :				str		=			15
							SCREAMING_SNAKE_CASE :				Tuple		=			-20.0
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			-4.0

							SCREAMING_SNAKE_CASE :				List[str]		=			processor.batch_decode(
							    lowerCamelCase_      ,						beam_width=lowerCamelCase_      ,						beam_prune_logp=lowerCamelCase_      ,						token_min_logp=lowerCamelCase_      ,						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			decoded_processor_out.text

							SCREAMING_SNAKE_CASE :				int		=			list(lowerCamelCase_						)

							with get_context("""fork"""						).Pool() as pool:
									SCREAMING_SNAKE_CASE :				List[Any]		=			decoder.decode_beams_batch(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						beam_width=lowerCamelCase_      ,						beam_prune_logp=lowerCamelCase_      ,						token_min_logp=lowerCamelCase_      ,						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			[d[0][0] for d in decoded_decoder_out]
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			[d[0][2] for d in decoded_decoder_out]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[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, -13.9_474]      ,						lowerCamelCase_      ,						atol=1e-3						)						)


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				List[Any]		=			WavaVecaProcessorWithLM(tokenizer=lowerCamelCase_      ,						feature_extractor=lowerCamelCase_      ,						decoder=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			self._get_dummy_logits()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			2.0
							SCREAMING_SNAKE_CASE :				Dict		=			5.0
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			-20.0
							SCREAMING_SNAKE_CASE :				Tuple		=			True

							SCREAMING_SNAKE_CASE :				int		=			processor.batch_decode(
							    lowerCamelCase_      ,						alpha=lowerCamelCase_      ,						beta=lowerCamelCase_      ,						unk_score_offset=lowerCamelCase_      ,						lm_score_boundary=lowerCamelCase_      ,						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			decoded_processor_out.text

							SCREAMING_SNAKE_CASE :				Any		=			list(lowerCamelCase_						)
							decoder.reset_params(
							    alpha=lowerCamelCase_      ,						beta=lowerCamelCase_      ,						unk_score_offset=lowerCamelCase_      ,						lm_score_boundary=lowerCamelCase_      ,						)

							with get_context("""fork"""						).Pool() as pool:
									SCREAMING_SNAKE_CASE :				Dict		=			decoder.decode_beams_batch(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						)

							SCREAMING_SNAKE_CASE :				Dict		=			[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_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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 lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			processor.decoder.model_container[processor.decoder._model_key]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			Path(language_model._kenlm_model.path.decode("""utf-8"""						)						).parent.parent.absolute()

							SCREAMING_SNAKE_CASE :				List[str]		=			os.listdir(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			["""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 lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			snapshot_download("""hf-internal-testing/processor_with_lm"""						)

							SCREAMING_SNAKE_CASE :				int		=			WavaVecaProcessorWithLM.from_pretrained(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Tuple		=			processor.decoder.model_container[processor.decoder._model_key]
							SCREAMING_SNAKE_CASE :				Any		=			Path(language_model._kenlm_model.path.decode("""utf-8"""						)						).parent.parent.absolute()

							SCREAMING_SNAKE_CASE :				Dict		=			os.listdir(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			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 lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""						)

							SCREAMING_SNAKE_CASE :				List[str]		=			floats_list((3, 10_00)						)

							SCREAMING_SNAKE_CASE :				int		=			processor_wavaveca(lowerCamelCase_      ,						return_tensors="""np"""						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			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						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			self._get_dummy_logits()

							SCREAMING_SNAKE_CASE :				int		=			processor_wavaveca.batch_decode(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			processor_auto.batch_decode(lowerCamelCase_						)

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


					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_feature_extractor()
							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				int		=			self.get_decoder()

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			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 lowerCamelCase_			(						lowerCamelCase_				:			Optional[Any]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			[d[key] for d in offsets]
							return retrieved_list


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self._get_dummy_logits()[0]

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			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 lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""						)
							SCREAMING_SNAKE_CASE :				str		=			self._get_dummy_logits()

							SCREAMING_SNAKE_CASE :				Optional[int]		=			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 lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							import torch

							SCREAMING_SNAKE_CASE :				str		=			load_dataset("""common_voice"""      ,						"""en"""      ,						split="""train"""      ,						streaming=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			ds.cast_column("""audio"""      ,						datasets.Audio(sampling_rate=1_60_00						)						)
							SCREAMING_SNAKE_CASE :				Any		=			iter(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			next(lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""						)
							SCREAMING_SNAKE_CASE :				Tuple		=			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
							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor(sample["""audio"""]["""array"""]      ,						return_tensors="""pt"""						).input_values

							with torch.no_grad():
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_						).logits.cpu().numpy()

							SCREAMING_SNAKE_CASE :				Optional[int]		=			processor.decode(logits[0]      ,						output_word_offsets=lowerCamelCase_						)

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

							SCREAMING_SNAKE_CASE :				Dict		=			"""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
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.tensor(self.get_from_offsets(lowerCamelCase_      ,						"""start_time"""						)						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.tensor(self.get_from_offsets(lowerCamelCase_      ,						"""end_time"""						)						)

							# fmt: off
							SCREAMING_SNAKE_CASE :				List[str]		=			torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]						)
							# fmt: on

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










'''simple docstring'''




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__UpperCAmelCase            =       {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}




class      UpperCamelCase__					(					nn.Module			):




					"""simple docstring"""


					def __init__(						self				:			Dict      ,						lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							super().__init__()
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torchvision.models.resnetaaa(pretrained=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			list(model.children()						)[:-2]
							SCREAMING_SNAKE_CASE :				Any		=			nn.Sequential(*lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]						)



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			self.pool(self.model(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.flatten(lowerCamelCase_      ,						start_dim=2						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			out.transpose(1      ,						2						).contiguous()
							return out  # BxNx2048







class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def __init__(						self				:			str      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			[json.loads(lowerCamelCase_						) for l in open(lowerCamelCase_						)]
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			os.path.dirname(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			labels
							SCREAMING_SNAKE_CASE :				str		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			max_seq_length

							SCREAMING_SNAKE_CASE :				List[Any]		=			transforms


					def __len__(						self				:			Any						):

							'''simple docstring'''



							return len(self.data						)


					def __getitem__(						self				:			Optional[int]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]      ,						add_special_tokens=lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Tuple		=			sentence[0], sentence[1:-1], sentence[-1]
							SCREAMING_SNAKE_CASE :				int		=			sentence[: self.max_seq_length]

							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.zeros(self.n_classes						)
							SCREAMING_SNAKE_CASE :				List[str]		=			1

							SCREAMING_SNAKE_CASE :				int		=			Image.open(os.path.join(self.data_dir      ,						self.data[index]["""img"""]						)						).convert("""RGB"""						)
							SCREAMING_SNAKE_CASE :				str		=			self.transforms(lowerCamelCase_						)

							return {
							    "image_start_token": start_token,
							    "image_end_token": end_token,
							    "sentence": sentence,
							    "image": image,
							    "label": label,
							}



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			Counter()
							for row in self.data:
									label_freqs.update(row["""label"""]						)
							return label_freqs







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			[len(row["""sentence"""]	) for row in batch]
		SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				Any		=			len(lowerCamelCase_	), max(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				int		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.zeros(lowerCamelCase_			,			lowerCamelCase_			,			dtype=torch.long	)

		for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase_			,			lowerCamelCase_	)	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			input_row["""sentence"""]
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			1

		SCREAMING_SNAKE_CASE :				List[str]		=			torch.stack([row["""image"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				int		=			torch.stack([row["""label"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				str		=			torch.stack([row["""image_start_token"""] for row in batch]	)
		SCREAMING_SNAKE_CASE :				Any		=			torch.stack([row["""image_end_token"""] for row in batch]	)

		return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor



def 		__A		(  ):


		"""simple docstring"""



		return [
		    "Crime",
		    "Drama",
		    "Thriller",
		    "Action",
		    "Comedy",
		    "Romance",
		    "Documentary",
		    "Short",
		    "Mystery",
		    "History",
		    "Family",
		    "Adventure",
		    "Fantasy",
		    "Sci-Fi",
		    "Western",
		    "Horror",
		    "Sport",
		    "War",
		    "Music",
		    "Musical",
		    "Animation",
		    "Biography",
		    "Film-Noir",
		]



def 		__A		(  ):


		"""simple docstring"""



		return transforms.Compose(
		    [
		        transforms.Resize(2_56	),
		        transforms.CenterCrop(2_24	),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]			,			std=[0.12_221_994, 0.12_145_835, 0.14_380_469]			,			),
		    ]	)



