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

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

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

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

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






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,**A   :					Optional[int]					):
					__A       =					self.add_kwargs_tokens_map.copy()
					kwargs.update(A					)
					return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,**A					)






	def 							UpperCamelCase_    (			self   :					str				,**A   :					Dict					):
					return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,**A					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,**A   :					Optional[Any]					):
					return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name				,**A					)






	def 							UpperCamelCase_    (			self   :					List[str]					):
					shutil.rmtree(self.tmpdirname					)






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self.get_tokenizer()
					__A       =					self.get_feature_extractor()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

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

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

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

					# 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				,A					)






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					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
					__A       =					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 							UpperCamelCase_    (			self   :					Optional[int]					):
					__A       =					self.get_tokenizer()
					# add token to trigger raise
					tokenizer.add_tokens(["xx"]					)
					with self.assertRaisesRegex(A				,"include"					):
									WavaVecaProcessorWithLM(
									    tokenizer=A				,feature_extractor=self.get_feature_extractor()				,decoder=self.get_decoder()					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					self.get_feature_extractor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

					__A       =					floats_list((3, 10_00)					)

					__A       =					feature_extractor(A				,return_tensors="np"					)
					__A       =					processor(A				,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   :					Optional[int]					):
					__A       =					self.get_feature_extractor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

					__A       =					"This is a test string"

					__A       =					processor(text=A					)

					__A       =					tokenizer(A					)

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






	def 							UpperCamelCase_    (			self   :					List[str]				,A   :					str=(2, 10, 16)				,A   :					Tuple=77					):
					np.random.seed(A					)
					return np.random.rand(*A					)






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					self.get_feature_extractor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

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

					__A       =					processor.decode(A					)

					__A       =					decoder.decode_beams(A					)[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 							UpperCamelCase_    (			self   :					Any				,A   :					Optional[Any]					):
					__A       =					self.get_feature_extractor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

					__A       =					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:
									__A       =					processor.batch_decode(A					)
					else:
									with get_context(A					).Pool() as pool:
													__A       =					processor.batch_decode(A				,A					)

					__A       =					list(A					)

					with get_context("fork"					).Pool() as p:
									__A       =					decoder.decode_beams_batch(A				,A					)

					__A     ,				__A     ,				__A       =					[], [], []
					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(A				,decoded_processor.text					)
					self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]				,decoded_processor.text					)
					self.assertListEqual(A				,decoded_processor.logit_score					)
					self.assertListEqual(A				,decoded_processor.lm_score					)






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					self.get_feature_extractor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

					__A       =					self._get_dummy_logits()

					__A       =					15
					__A       =					-20.0
					__A       =					-4.0

					__A       =					processor.batch_decode(
					    A				,beam_width=A				,beam_prune_logp=A				,token_min_logp=A				,)
					__A       =					decoded_processor_out.text

					__A       =					list(A					)

					with get_context("fork"					).Pool() as pool:
									__A       =					decoder.decode_beams_batch(
									    A				,A				,beam_width=A				,beam_prune_logp=A				,token_min_logp=A				,)

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

					self.assertListEqual(A				,A					)
					self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]				,A					)

					self.assertTrue(np.array_equal(A				,decoded_processor_out.logit_score					)					)
					self.assertTrue(np.allclose([-20.0_54, -18.4_47]				,A				,atol=1E-3					)					)

					self.assertTrue(np.array_equal(A				,decoded_processor_out.lm_score					)					)
					self.assertTrue(np.allclose([-15.5_54, -13.94_74]				,A				,atol=1E-3					)					)






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					self.get_feature_extractor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

					__A       =					self._get_dummy_logits()

					__A       =					2.0
					__A       =					5.0
					__A       =					-20.0
					__A       =					True

					__A       =					processor.batch_decode(
					    A				,alpha=A				,beta=A				,unk_score_offset=A				,lm_score_boundary=A				,)
					__A       =					decoded_processor_out.text

					__A       =					list(A					)
					decoder.reset_params(
					    alpha=A				,beta=A				,unk_score_offset=A				,lm_score_boundary=A				,)

					with get_context("fork"					).Pool() as pool:
									__A       =					decoder.decode_beams_batch(
									    A				,A				,)

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

					self.assertListEqual(A				,A					)
					self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]				,A					)
					__A       =					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				,A					)






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"					)

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

					__A       =					os.listdir(A					)
					__A       =					["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(A				,A					)






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					snapshot_download("hf-internal-testing/processor_with_lm"					)

					__A       =					WavaVecaProcessorWithLM.from_pretrained(A					)

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

					__A       =					os.listdir(A					)
					__A       =					os.listdir(A					)

					local_decoder_files.sort()
					expected_decoder_files.sort()

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






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"					)
					__A       =					AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"					)

					__A       =					floats_list((3, 10_00)					)

					__A       =					processor_wavaveca(A				,return_tensors="np"					)
					__A       =					processor_auto(A				,return_tensors="np"					)

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

					__A       =					self._get_dummy_logits()

					__A       =					processor_wavaveca.batch_decode(A					)
					__A       =					processor_auto.batch_decode(A					)

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






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					self.get_feature_extractor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_decoder()

					__A       =					WavaVecaProcessorWithLM(tokenizer=A				,feature_extractor=A				,decoder=A					)

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






	@staticmethod
	def 							UpperCamelCase_    (			A   :					Optional[Any]				,A   :					Tuple					):
					__A       =					[d[key] for d in offsets]
					return retrieved_list






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"					)
					__A       =					self._get_dummy_logits()[0]

					__A       =					processor.decode(A				,output_word_offsets=A					)
					# 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(A				,A					)					)

					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 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"					)
					__A       =					self._get_dummy_logits()

					__A       =					processor.batch_decode(A				,output_word_offsets=A					)

					# 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(A				,A					)					)

					self.assertListEqual(
					    [" ".join(self.get_from_offsets(A				,"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 							UpperCamelCase_    (			self   :					Tuple					):
					import torch

					__A       =					load_dataset("common_voice"				,"en"				,split="train"				,streaming=A					)
					__A       =					ds.cast_column("audio"				,datasets.Audio(sampling_rate=1_60_00					)					)
					__A       =					iter(A					)
					__A       =					next(A					)

					__A       =					AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"					)
					__A       =					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
					__A       =					processor(sample["audio"]["array"]				,return_tensors="pt"					).input_values

					with torch.no_grad():
									__A       =					model(A					).logits.cpu().numpy()

					__A       =					processor.decode(logits[0]				,output_word_offsets=A					)

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

					__A       =					"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(A				,"word"					)					)				,A					)
					self.assertEqual(" ".join(self.get_from_offsets(A				,"word"					)					)				,output.text					)

					# output times
					__A       =					torch.tensor(self.get_from_offsets(A				,"start_time"					)					)
					__A       =					torch.tensor(self.get_from_offsets(A				,"end_time"					)					)

					# fmt: off
					__A       =					torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]					)
					__A       =					torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]					)
					# fmt: on

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





"""simple docstring"""






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__snake_case				:  Optional[Any] =       pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__snake_case				:  int =       dataset.iloc[:, 1:2].values
__snake_case				:  Tuple =       dataset.iloc[:, 2].values


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			plt.scatter(__snake_case   ,__snake_case   ,color="red"      )
			plt.plot(__snake_case   ,pol_reg.predict(poly_reg.fit_transform(__snake_case      )      )   ,color="blue"      )
			plt.title("Truth or Bluff (Linear Regression)"      )
			plt.xlabel("Position level"      )
			plt.ylabel("Salary"      )
			plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003