
"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase__      :    Tuple    							= random.Random()

if is_torch_available():
					import torch

def lowercase_  (   _snake_case   ,_snake_case=1.0   ,_snake_case=None   ,_snake_case=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



class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=7   ,       SCREAMING_SNAKE_CASE__=4_00   ,       SCREAMING_SNAKE_CASE__=20_00   ,       SCREAMING_SNAKE_CASE__=1   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=1_60_00   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=True   ,       )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   parent
											SCREAMING_SNAKE_CASE__						:       int           =   batch_size
											SCREAMING_SNAKE_CASE__						:       Dict           =   min_seq_length
											SCREAMING_SNAKE_CASE__						:       List[str]           =   max_seq_length
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   feature_size
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   padding_value
											SCREAMING_SNAKE_CASE__						:       Any           =   sampling_rate
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   return_attention_mask
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   do_normalize



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											return {
											    "feature_size": self.feature_size,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "return_attention_mask": self.return_attention_mask,
											    "do_normalize": self.do_normalize,
											}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__=False   ,       SCREAMING_SNAKE_CASE__=False    )					->		List[Any]:



											"""simple docstring"""




											def _flatten(SCREAMING_SNAKE_CASE__    ):
																		return list(itertools.chain(*SCREAMING_SNAKE_CASE__    )    )

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

											return speech_inputs






@require_torch
@require_torchaudio
class 	lowerCAmelCase_						(a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						List[str]					       =   ASTFeatureExtractor



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ASTFeatureExtractionTester(self    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[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__						:       List[Any]           =   [floats_list((1, x)    )[0] for x in range(8_00   ,       14_00   ,       2_00    )]
											SCREAMING_SNAKE_CASE__						:       Dict           =   [np.asarray(SCREAMING_SNAKE_CASE__    ) for speech_input in speech_inputs]

											# Test not batched input
											SCREAMING_SNAKE_CASE__						:       int           =   feat_extract(speech_inputs[0]   ,       return_tensors="""np"""    ).input_values
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   feat_extract(np_speech_inputs[0]   ,       return_tensors="""np"""    ).input_values
											self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-3    )    )

											# Test batched
											SCREAMING_SNAKE_CASE__						:       List[str]           =   feat_extract(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    ).input_values
											SCREAMING_SNAKE_CASE__						:       str           =   feat_extract(SCREAMING_SNAKE_CASE__   ,       padding=SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    ).input_values
											for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-3    )    )

											# Test 2-D numpy arrays are batched.
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [floats_list((1, x)    )[0] for x in (8_00, 8_00, 8_00)]
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   np.asarray(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   feat_extract(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    ).input_values
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   feat_extract(SCREAMING_SNAKE_CASE__   ,       return_tensors="""np"""    ).input_values
											for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-3    )    )



				@require_torch
				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											import torch

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
											SCREAMING_SNAKE_CASE__						:       Any           =   np.random.rand(1_00    ).astype(np.floataa    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   np_speech_inputs.tolist()

											for inputs in [py_speech_inputs, np_speech_inputs]:
																		SCREAMING_SNAKE_CASE__						:       Any           =   feature_extractor.pad([{"""input_values""": inputs}]   ,       return_tensors="""np"""    )
																		self.assertTrue(np_processed.input_values.dtype == np.floataa    )
																		SCREAMING_SNAKE_CASE__						:       Dict           =   feature_extractor.pad([{"""input_values""": inputs}]   ,       return_tensors="""pt"""    )
																		self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											from datasets import load_dataset

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

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



				@require_torch
				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   torch.tensor(
											    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
											     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
											     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
											     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]    )
											# fmt: on

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self._load_datasamples(1    )
											SCREAMING_SNAKE_CASE__						:       int           =   ASTFeatureExtractor()
											SCREAMING_SNAKE_CASE__						:       Dict           =   feature_extractor(SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    ).input_values
											self.assertEquals(input_values.shape   ,       (1, 10_24, 1_28)    )
											self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-4    )    )







"""simple docstring"""


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     snake_case	(				__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      List[str]         =  None
				SCREAMING_SNAKE_CASE_   :      Tuple         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      str         =  BloomTokenizerFast
				SCREAMING_SNAKE_CASE_   :      int         =  True
				SCREAMING_SNAKE_CASE_   :      Tuple         =  False
				SCREAMING_SNAKE_CASE_   :      int         =  """tokenizer_file"""
				SCREAMING_SNAKE_CASE_   :      List[str]         =  {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

				def 							lowercase_     (	self							:				List[Any])->   Dict:



						'''simple docstring'''


						super().setUp()
						__lowerCAmelCase:       Optional[Any]          =			BloomTokenizerFast.from_pretrained("bigscience/tokenizer")
						tokenizer.save_pretrained(self.tmpdirname)

				def 							lowercase_     (	self							:				List[Any]     ,  **UpperCamelCase__							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						kwargs.update(self.special_tokens_map)
						return BloomTokenizerFast.from_pretrained(self.tmpdirname     ,  **UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self.get_rust_tokenizer()

						__lowerCAmelCase:       int          =			["The quick brown fox</s>", "jumps over the lazy dog</s>"]
						__lowerCAmelCase:       List[str]          =			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

						__lowerCAmelCase:       List[str]          =			tokenizer.batch_encode_plus(UpperCamelCase__)["input_ids"]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

						__lowerCAmelCase:       List[Any]          =			tokenizer.batch_decode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				Tuple=6)->   Tuple:



						'''simple docstring'''


						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCAmelCase:       Optional[Any]          =			self.rust_tokenizer_class.from_pretrained(UpperCamelCase__     ,  **UpperCamelCase__)
										# tokenizer_r.pad_token = None # Hotfixing padding = None
										# Simple input
										__lowerCAmelCase:       Dict          =			"This is a simple input"
										__lowerCAmelCase:       str          =			["This is a simple input 1", "This is a simple input 2"]
										__lowerCAmelCase:       int          =			("This is a simple input", "This is a pair")
										__lowerCAmelCase:       Union[str, Any]          =			[
										    ("This is a simple input 1", "This is a simple input 2"),
										    ("This is a simple pair 1", "This is a simple pair 2"),
										]

										# Simple input tests
										try:
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)

												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.encode(UpperCamelCase__     ,  max_length=UpperCamelCase__)
												tokenizer_r.batch_encode_plus(UpperCamelCase__     ,  max_length=UpperCamelCase__)
										except ValueError:
												self.fail("Bloom Tokenizer should be able to deal with padding")

										__lowerCAmelCase:       Tuple          =			None  # Hotfixing padding = None
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Simple input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(UpperCamelCase__     ,  tokenizer_r.encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length")

										# Pair input
										self.assertRaises(
										    UpperCamelCase__     ,  tokenizer_r.batch_encode_plus     ,  UpperCamelCase__     ,  max_length=UpperCamelCase__     ,  padding="max_length"     ,  )

				def 							lowercase_     (	self							:				Optional[Any])->   List[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			self.get_rust_tokenizer()
						__lowerCAmelCase:       List[str]          =			load_dataset("xnli"     ,  "all_languages"     ,  split="test"     ,  streaming=UpperCamelCase__)

						__lowerCAmelCase:       Union[str, Any]          =			next(iter(UpperCamelCase__))["premise"]  # pick up one data
						__lowerCAmelCase:       Any          =			list(sample_data.values())

						__lowerCAmelCase:       int          =			list(map(tokenizer.encode     ,  UpperCamelCase__))
						__lowerCAmelCase:       str          =			[tokenizer.decode(UpperCamelCase__     ,  clean_up_tokenization_spaces=UpperCamelCase__) for x in output_tokens]
						self.assertListEqual(UpperCamelCase__     ,  UpperCamelCase__)




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



						'''simple docstring'''


						self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)     ,  1)
						self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])     ,  1)


