

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


lowerCAmelCase							:						Dict     			=		random.Random()







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=1.0       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None					):
						if rng is None:
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= global_rng

						SCREAMING_SNAKE_CASE_:				List[str]							= []
						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 						__lowercase       (				unittest.TestCase			):




			"""simple docstring"""







			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Dict=7     ,		lowerCAmelCase__						:  List[str]=400     ,		lowerCAmelCase__						:  Optional[Any]=2000     ,		lowerCAmelCase__						:  str=1     ,		lowerCAmelCase__						:  Any=0.0     ,		lowerCAmelCase__						:  str=2_4000     ,		lowerCAmelCase__						:  Tuple=True     ,		):
									SCREAMING_SNAKE_CASE_:				int							= parent
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= batch_size
									SCREAMING_SNAKE_CASE_:				Optional[int]							= min_seq_length
									SCREAMING_SNAKE_CASE_:				List[str]							= max_seq_length
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									SCREAMING_SNAKE_CASE_:				List[Any]							= feature_size
									SCREAMING_SNAKE_CASE_:				int							= padding_value
									SCREAMING_SNAKE_CASE_:				Dict							= sampling_rate
									SCREAMING_SNAKE_CASE_:				List[Any]							= return_attention_mask



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									}




			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Dict=False     ,		lowerCAmelCase__						:  List[Any]=False):
									def _flatten(lowerCAmelCase__						:  Union[str, Any]):
															return list(itertools.chain(*lowerCAmelCase__))

									if equal_length:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= floats_list((self.batch_size, self.max_seq_length))
									else:
															# make sure that inputs increase in size
															SCREAMING_SNAKE_CASE_:				Optional[int]							= [
															    _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:
															SCREAMING_SNAKE_CASE_:				Any							= [np.asarray(lowerCAmelCase__) for x in audio_inputs]

									return audio_inputs







@require_torch
class 						__lowercase       (				UpperCAmelCase_	, unittest.TestCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							List[Any]    							=  EncodecFeatureExtractor



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									SCREAMING_SNAKE_CASE_:				str							= EnCodecFeatureExtractionTester(self)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									SCREAMING_SNAKE_CASE_:				Any							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									# create three inputs of length 800, 1000, and 1200
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= [floats_list((1, x))[0] for x in range(800     ,		1400     ,		200)]
									SCREAMING_SNAKE_CASE_:				Dict							= [np.asarray(lowerCAmelCase__) for audio_input in audio_inputs]

									# Test not batched input
									SCREAMING_SNAKE_CASE_:				Any							= feat_extract(audio_inputs[0]     ,		return_tensors="np").input_values
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= feat_extract(np_audio_inputs[0]     ,		return_tensors="np").input_values
									self.assertTrue(np.allclose(lowerCAmelCase__     ,		lowerCAmelCase__     ,		atol=1E-3))

									# Test batched
									SCREAMING_SNAKE_CASE_:				Dict							= feat_extract(lowerCAmelCase__     ,		padding=lowerCAmelCase__     ,		return_tensors="np").input_values
									SCREAMING_SNAKE_CASE_:				int							= 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  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				List[str]							= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
									SCREAMING_SNAKE_CASE_:				Any							= np.random.rand(100).astype(np.floataa)
									SCREAMING_SNAKE_CASE_:				List[str]							= np_audio_inputs.tolist()

									for inputs in [py_audio_inputs, np_audio_inputs]:
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= feature_extractor.pad([{"input_values": inputs}]     ,		return_tensors="np")
															self.assertTrue(np_processed.input_values.dtype == np.floataa)
															SCREAMING_SNAKE_CASE_:				Optional[int]							= feature_extractor.pad([{"input_values": inputs}]     ,		return_tensors="pt")
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple     ,		lowerCAmelCase__						:  int):
									from datasets import load_dataset

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

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									# fmt: off
									SCREAMING_SNAKE_CASE_:				Any							= 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
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self._load_datasamples(1)
									SCREAMING_SNAKE_CASE_:				List[Any]							= EncodecFeatureExtractor()
									SCREAMING_SNAKE_CASE_:				Dict							= feature_extractor(lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(input_values.shape     ,		(1, 1, 9_3680))
									self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,		lowerCAmelCase__     ,		atol=1E-6))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									# fmt: off
									SCREAMING_SNAKE_CASE_:				int							= 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
									SCREAMING_SNAKE_CASE_:				Any							= self._load_datasamples(1)
									SCREAMING_SNAKE_CASE_:				int							= [np.tile(input_audio[0][None]     ,		reps=(2, 1))]
									input_audio[0][1] *= 0.5
									SCREAMING_SNAKE_CASE_:				Any							= EncodecFeatureExtractor(feature_size=2)
									SCREAMING_SNAKE_CASE_:				int							= feature_extractor(lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(input_values.shape     ,		(1, 2, 9_3680))
									self.assertTrue(torch.allclose(input_values[0, 0, :30]     ,		lowerCAmelCase__     ,		atol=1E-6))
									self.assertTrue(torch.allclose(input_values[0, 1, :30]     ,		EXPECTED_INPUT_VALUES * 0.5     ,		atol=1E-6))




			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									SCREAMING_SNAKE_CASE_:				str							= self._load_datasamples(2)
									# would be easier if the stride was like
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= EncodecFeatureExtractor(feature_size=1     ,		chunk_length_s=1     ,		overlap=0.01)

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

									# truncate to chunk
									SCREAMING_SNAKE_CASE_:				Any							= feature_extractor(lowerCAmelCase__     ,		truncation=lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(truncated_outputs.shape     ,		(2, 1, 7_1520))  # 2 chunks

									# force truncate to max_length
									SCREAMING_SNAKE_CASE_:				Tuple							= feature_extractor(
									    lowerCAmelCase__     ,		truncation=lowerCAmelCase__     ,		max_length=4_8000     ,		return_tensors="pt").input_values
									self.assertEquals(truncated_outputs.shape     ,		(2, 1, 4_8000))

									# pad to chunk
									SCREAMING_SNAKE_CASE_:				List[Any]							= feature_extractor(lowerCAmelCase__     ,		padding=lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(padded_outputs.shape     ,		(2, 1, 9_5280))

									# pad to chunk
									SCREAMING_SNAKE_CASE_:				Tuple							= feature_extractor(lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(truncated_outputs.shape     ,		(2, 1, 9_5280))

									# force pad to max length
									SCREAMING_SNAKE_CASE_:				int							= feature_extractor(
									    lowerCAmelCase__     ,		padding="max_length"     ,		max_length=10_0000     ,		return_tensors="pt").input_values
									self.assertEquals(truncated_outputs.shape     ,		(2, 1, 10_0000))

									# 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.$"     ,		):
															SCREAMING_SNAKE_CASE_:				Tuple							= feature_extractor(lowerCAmelCase__     ,		padding=lowerCAmelCase__     ,		return_tensors="pt").input_values

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= feature_extractor(input_audio[0]     ,		padding=lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(truncated_outputs.shape     ,		(1, 1, 9_3680))

									# no pad if no chunk_length_s
									SCREAMING_SNAKE_CASE_:				List[Any]							= 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.$"     ,		):
															SCREAMING_SNAKE_CASE_:				Tuple							= feature_extractor(lowerCAmelCase__     ,		padding=lowerCAmelCase__     ,		return_tensors="pt").input_values

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= feature_extractor(input_audio[0]     ,		padding=lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(truncated_outputs.shape     ,		(1, 1, 9_3680))

									# no pad if no overlap
									SCREAMING_SNAKE_CASE_:				Any							= 2
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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.$"     ,		):
															SCREAMING_SNAKE_CASE_:				Tuple							= feature_extractor(lowerCAmelCase__     ,		padding=lowerCAmelCase__     ,		return_tensors="pt").input_values

									SCREAMING_SNAKE_CASE_:				Dict							= feature_extractor(input_audio[0]     ,		padding=lowerCAmelCase__     ,		return_tensors="pt").input_values
									self.assertEquals(truncated_outputs.shape     ,		(1, 1, 9_3680))









import io
import json
import subprocess


lowerCAmelCase							:						Dict     			=		[
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

lowerCAmelCase							:						List[str]     			=		8







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Dict							= {}
						for src, tgt in pairs:
												SCREAMING_SNAKE_CASE_:				Tuple							= f"{src}-{tgt}"
												SCREAMING_SNAKE_CASE_:				List[str]							= f"sacrebleu -t wmt19 -l {pair} --echo src".split()
												SCREAMING_SNAKE_CASE_:				int							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
												SCREAMING_SNAKE_CASE_:				Dict							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Tuple							= {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
						return text


lowerCAmelCase							:						Tuple     			=		get_all_data(pairs, n_objs)
lowerCAmelCase							:						Optional[Any]     			=		"""./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
						lowerCAmelCase							:						List[str]     			=		json.dump(text, f, indent=2, ensure_ascii=False)






