


'''simple docstring'''





import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


_lowercase       :      Any	=	random.Random()





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Optional[Any]=1.0							,					UpperCamelCase__:     Any=None							,					UpperCamelCase__:     Optional[Any]=None						) ->   List[str]:





	"""simple docstring"""







	if rng is None:
		A								=				global_rng

	A								=				[]
	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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=7				,   a__=400				,   a__=2000				,   a__=1				,   a__=0.0				,   a__=1_6000				,   a__=True				,   a__=80				,   a__=16				,   a__=64				,   a__="hann_window"				,   a__=80				,   a__=7600				,   a__=1e-10				,   a__=True				,   )	->				List[str]:
					A								=				parent
					A								=				batch_size
					A								=				min_seq_length
					A								=				max_seq_length
					A								=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					A								=				feature_size
					A								=				padding_value
					A								=				sampling_rate
					A								=				do_normalize
					A								=				num_mel_bins
					A								=				hop_length
					A								=				win_length
					A								=				win_function
					A								=				fmin
					A								=				fmax
					A								=				mel_floor
					A								=				return_attention_mask

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "do_normalize": self.do_normalize,
					    "num_mel_bins": self.num_mel_bins,
					    "hop_length": self.hop_length,
					    "win_length": self.win_length,
					    "win_function": self.win_function,
					    "fmin": self.fmin,
					    "fmax": self.fmax,
					    "mel_floor": self.mel_floor,
					    "return_attention_mask": self.return_attention_mask,
					}

				def      _UpperCAmelCase					(						self				,   a__=False				,   a__=False   )	->				List[Any]:
					def _flatten(a__   ):
						return list(itertools.chain(*a__   )   )

					if equal_length:
						A								=				floats_list((self.batch_size, self.max_seq_length)   )
					else:
						# make sure that inputs increase in size
						A								=				[
						    _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:
						A								=				[np.asarray(a__   ) for x in speech_inputs]

					return speech_inputs

				def      _UpperCAmelCase					(						self				,   a__=False				,   a__=False   )	->				Union[str, Any]:
					if equal_length:
						A								=				[floats_list((self.max_seq_length, self.num_mel_bins)   ) for _ in range(self.batch_size   )]
					else:
						# make sure that inputs increase in size
						A								=				[
						    floats_list((x, self.num_mel_bins)   )
						    for x in range(self.min_seq_length				,   self.max_seq_length				,   self.seq_length_diff   )
						]

					if numpify:
						A								=				[np.asarray(a__   ) for x in speech_inputs]

					return speech_inputs






@require_torch
class       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= SpeechTaFeatureExtractor

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				SpeechTaFeatureExtractionTester(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				int:
					self.assertTrue(np.all(np.mean(a__				,   axis=0   ) < 1e-3   )   )
					self.assertTrue(np.all(np.abs(np.var(a__				,   axis=0   ) - 1   ) < 1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				List[str]:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					# create three inputs of length 800, 1000, and 1200
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				[np.asarray(a__   ) for speech_input in speech_inputs]

					# Test not batched input
					A								=				feat_extract(speech_inputs[0]				,   return_tensors="""np"""   ).input_values
					A								=				feat_extract(np_speech_inputs[0]				,   return_tensors="""np"""   ).input_values
					self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

					# Test batched
					A								=				feat_extract(a__				,   return_tensors="""np"""   ).input_values
					A								=				feat_extract(a__				,   return_tensors="""np"""   ).input_values
					for enc_seq_a, enc_seq_a in zip(a__				,   a__   ):
						self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 1600, None]
					for max_length, padding in zip(a__				,   a__   ):
						A								=				feat_extract(a__				,   padding=a__				,   max_length=a__				,   return_tensors="""np"""   )
						A								=				processed.input_values

						self._check_zero_mean_unit_variance(input_values[0][:800]   )
						self.assertTrue(input_values[0][800:].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_values[1][:1000]   )
						self.assertTrue(input_values[0][1000:].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_values[2][:1200]   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				range(800				,   1400				,   200   )
					A								=				[floats_list((1, x)   )[0] for x in lengths]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 1600, None]

					for max_length, padding in zip(a__				,   a__   ):
						A								=				feat_extract(a__				,   max_length=a__				,   padding=a__   )
						A								=				processed.input_values

						self._check_zero_mean_unit_variance(input_values[0][:800]   )
						self._check_zero_mean_unit_variance(input_values[1][:1000]   )
						self._check_zero_mean_unit_variance(input_values[2][:1200]   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feat_extract(
					    a__				,   truncation=a__				,   max_length=1000				,   padding="""max_length"""				,   return_tensors="""np"""   )
					A								=				processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :800]   )
					self._check_zero_mean_unit_variance(input_values[1]   )
					self._check_zero_mean_unit_variance(input_values[2]   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feat_extract(
					    a__				,   truncation=a__				,   max_length=1000				,   padding="""longest"""				,   return_tensors="""np"""   )
					A								=				processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :800]   )
					self._check_zero_mean_unit_variance(input_values[1, :1000]   )
					self._check_zero_mean_unit_variance(input_values[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertTrue(input_values.shape == (3, 1000)   )

					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feat_extract(
					    a__				,   truncation=a__				,   max_length=2000				,   padding="""longest"""				,   return_tensors="""np"""   )
					A								=				processed.input_values

					self._check_zero_mean_unit_variance(input_values[0, :800]   )
					self._check_zero_mean_unit_variance(input_values[1, :1000]   )
					self._check_zero_mean_unit_variance(input_values[2]   )

					# make sure that if max_length > longest -> then pad to longest
					self.assertTrue(input_values.shape == (3, 1200)   )

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				np.random.rand(100   ).astype(np.floataa   )
					A								=				np_speech_inputs.tolist()

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

				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					# create three inputs of length 800, 1000, and 1200
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				[np.asarray(a__   ) for speech_input in speech_inputs]

					# Test feature size
					A								=				feature_extractor(audio_target=a__				,   padding=a__				,   return_tensors="""np"""   ).input_values
					self.assertTrue(input_values.ndim == 3   )
					self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins   )

					# Test not batched input
					A								=				feature_extractor(speech_inputs[0]				,   return_tensors="""np"""   ).input_values
					A								=				feature_extractor(np_speech_inputs[0]				,   return_tensors="""np"""   ).input_values
					self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

					# Test batched
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_values
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_values
					for enc_seq_a, enc_seq_a in zip(a__				,   a__   ):
						self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

					# Test 2-D numpy arrays are batched.
					A								=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
					A								=				np.asarray(a__   )
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_values
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_values
					for enc_seq_a, enc_seq_a in zip(a__				,   a__   ):
						self.assertTrue(np.allclose(a__				,   a__				,   atol=1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				self.feat_extract_tester.prepare_inputs_for_target()
					A								=				self.feature_extraction_class(**self.feat_extract_dict   )
					A								=				feat_extract.model_input_names[0]

					A								=				BatchFeature({input_name: speech_inputs}   )

					self.assertTrue(all(len(a__   ) == len(a__   ) for x, y in zip(a__				,   processed_features[input_name]   )   )   )

					A								=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=a__   )
					A								=				BatchFeature({input_name: speech_inputs}				,   tensor_type="""np"""   )

					A								=				processed_features[input_name]

					if len(batch_features_input.shape   ) < 3:
						A								=				batch_features_input[:, :, None]

					self.assertTrue(
					    batch_features_input.shape
					    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )

				@require_torch
				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=a__   )
					A								=				self.feature_extraction_class(**self.feat_extract_dict   )
					A								=				feat_extract.model_input_names[0]

					A								=				BatchFeature({input_name: speech_inputs}				,   tensor_type="""pt"""   )

					A								=				processed_features[input_name]

					if len(batch_features_input.shape   ) < 3:
						A								=				batch_features_input[:, :, None]

					self.assertTrue(
					    batch_features_input.shape
					    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )

				@require_torch
				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A								=				self.feature_extraction_class(**self.feat_extract_dict   )
					A								=				self.feat_extract_tester.prepare_inputs_for_target()
					A								=				feat_extract.model_input_names[0]

					A								=				BatchFeature({input_name: speech_inputs}   )

					A								=				feat_extract.num_mel_bins  # hack!

					A								=				feat_extract.pad(a__				,   padding="""longest"""				,   return_tensors="""np"""   )[input_name]
					A								=				feat_extract.pad(a__				,   padding="""longest"""				,   return_tensors="""pt"""   )[input_name]

					self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1e-2   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				self.feat_extract_dict
					A								=				True
					A								=				self.feature_extraction_class(**a__   )
					A								=				self.feat_extract_tester.prepare_inputs_for_target()
					A								=				[len(a__   ) for x in speech_inputs]
					A								=				feat_extract.model_input_names[0]

					A								=				BatchFeature({input_name: speech_inputs}   )

					A								=				feat_extract.num_mel_bins  # hack!

					A								=				feat_extract.pad(a__				,   padding="""longest"""				,   return_tensors="""np"""   )
					self.assertIn("""attention_mask"""				,   a__   )
					self.assertListEqual(list(processed.attention_mask.shape   )				,   list(processed[input_name].shape[:2]   )   )
					self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()				,   a__   )

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				self.feat_extract_dict
					A								=				True
					A								=				self.feature_extraction_class(**a__   )
					A								=				self.feat_extract_tester.prepare_inputs_for_target()
					A								=				[len(a__   ) for x in speech_inputs]
					A								=				feat_extract.model_input_names[0]

					A								=				BatchFeature({input_name: speech_inputs}   )
					A								=				min(a__   )

					A								=				feat_extract.num_mel_bins  # hack!

					A								=				feat_extract.pad(
					    a__				,   padding="""max_length"""				,   max_length=a__				,   truncation=a__				,   return_tensors="""np"""   )
					self.assertIn("""attention_mask"""				,   a__   )
					self.assertListEqual(
					    list(processed_pad.attention_mask.shape   )				,   [processed_pad[input_name].shape[0], max_length]   )
					self.assertListEqual(
					    processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()				,   [max_length for x in speech_inputs]   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Union[str, Any]:
					from datasets import load_dataset

					A								=				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,   """clean"""				,   split="""validation"""   )
					# automatic decoding with librispeech
					A								=				ds.sort("""id"""   ).select(range(a__   )   )[:num_samples]["""audio"""]

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

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					# fmt: off
					A								=				torch.tensor(
					    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
					     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
					     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
					     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
					     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
					     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03]   )
					# fmt: on

					A								=				self._load_datasamples(1   )
					A								=				SpeechTaFeatureExtractor()
					A								=				feature_extractor(a__				,   return_tensors="""pt"""   ).input_values
					self.assertEquals(input_values.shape				,   (1, 9_3680)   )
					self.assertTrue(torch.allclose(input_values[0, :30]				,   a__				,   atol=1e-6   )   )

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					# fmt: off
					A								=				torch.tensor(
					    [-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
					     -3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
					     -3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
					     -3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98]   )
					# fmt: on

					A								=				self._load_datasamples(1   )
					A								=				SpeechTaFeatureExtractor()
					A								=				feature_extractor(audio_target=a__				,   return_tensors="""pt"""   ).input_values
					self.assertEquals(input_values.shape				,   (1, 366, 80)   )
					self.assertTrue(torch.allclose(input_values[0, 0, :30]				,   a__				,   atol=1e-4   )   )







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



