


import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_snake_case					:		str      =    random.Random()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=1.0							,     __lowerCamelCase=None							,     __lowerCamelCase=None     ):
							if rng is None:
														__snake_case		:      Tuple										=       global_rng

							__snake_case		:      Union[str, Any]										=       []
							for batch_idx in range(shape[0]     ):
														values.append([]     )
														for _ in range(shape[1]     ):
																					values[-1].append(rng.random() * scale     )

							return values

class     a		(unittest.TestCase				):




							"""simple docstring"""


							def __init__(							self	:  Optional[Any]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  Union[str, Any]=7   ,     lowerCamelCase	:  Dict=400   ,     lowerCamelCase	:  List[Any]=2000   ,     lowerCamelCase	:  List[Any]=1   ,     lowerCamelCase	:  Dict=0.0   ,     lowerCamelCase	:  str=16000   ,     lowerCamelCase	:  List[Any]=True   ,     lowerCamelCase	:  Optional[Any]=True   ,     )     ->   Optional[Any]:
														__snake_case		:      str										=       parent
														__snake_case		:      int										=       batch_size
														__snake_case		:      List[str]										=       min_seq_length
														__snake_case		:      Optional[int]										=       max_seq_length
														__snake_case		:      Dict										=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														__snake_case		:      int										=       feature_size
														__snake_case		:      List[Any]										=       padding_value
														__snake_case		:      Tuple										=       sampling_rate
														__snake_case		:      Optional[int]										=       return_attention_mask
														__snake_case		:      List[str]										=       do_normalize

							def    __snake_case (							self	:  Tuple    )     ->   Any:
														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    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  str=False   ,     lowerCamelCase	:  List[str]=False    )     ->   Optional[Any]:







														def _flatten(lowerCamelCase	:  Any    ):
																					return list(itertools.chain(*_lowerCamelCase    )    )

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

														return speech_inputs

class     a		(a__					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  WavaVecaFeatureExtractor

							def    __snake_case (							self	:  Optional[int]    )     ->   List[Any]:
														__snake_case		:      str										=       WavaVecaFeatureExtractionTester(self    )

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Union[str, Any]    )     ->   Tuple:
														self.assertTrue(np.all(np.mean(_lowerCamelCase   ,     axis=0    ) < 1E-3    )    )
														self.assertTrue(np.all(np.abs(np.var(_lowerCamelCase   ,     axis=0    ) - 1    ) < 1E-3    )    )

							def    __snake_case (							self	:  Optional[int]    )     ->   str:
														__snake_case		:      List[str]										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
														# create three inputs of length 800, 1000, and 1200
														__snake_case		:      Optional[Any]										=       [floats_list((1, x)    )[0] for x in range(800   ,     1400   ,     200    )]
														__snake_case		:      List[Any]										=       [np.asarray(_lowerCamelCase    ) for speech_input in speech_inputs]

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

														# Test batched
														__snake_case		:      Dict										=       feat_extract(_lowerCamelCase   ,     return_tensors="np"    ).input_values
														__snake_case		:      Dict										=       feat_extract(_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    )    )

														# Test 2-D numpy arrays are batched.
														__snake_case		:      Optional[Any]										=       [floats_list((1, x)    )[0] for x in (800, 800, 800)]
														__snake_case		:      List[str]										=       np.asarray(_lowerCamelCase    )
														__snake_case		:      Dict										=       feat_extract(_lowerCamelCase   ,     return_tensors="np"    ).input_values
														__snake_case		:      Optional[int]										=       feat_extract(_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    __snake_case (							self	:  Dict    )     ->   Optional[Any]:
														__snake_case		:      str										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
														__snake_case		:      int										=       [floats_list((1, x)    )[0] for x in range(800   ,     1400   ,     200    )]

														__snake_case		:      Dict										=       ['''longest''', '''max_length''', '''do_not_pad''']
														__snake_case		:      Dict										=       [None, 1600, None]
														for max_length, padding in zip(_lowerCamelCase   ,     _lowerCamelCase    ):
																					__snake_case		:      Dict										=       feat_extract(_lowerCamelCase   ,     padding=_lowerCamelCase   ,     max_length=_lowerCamelCase   ,     return_tensors="np"    )
																					__snake_case		:      Tuple										=       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    __snake_case (							self	:  List[Any]    )     ->   List[Any]:
														__snake_case		:      Optional[int]										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
														__snake_case		:      Optional[int]										=       range(800   ,     1400   ,     200    )
														__snake_case		:      Optional[Any]										=       [floats_list((1, x)    )[0] for x in lengths]

														__snake_case		:      int										=       ['''longest''', '''max_length''', '''do_not_pad''']
														__snake_case		:      Dict										=       [None, 1600, None]

														for max_length, padding in zip(_lowerCamelCase   ,     _lowerCamelCase    ):
																					__snake_case		:      List[str]										=       feat_extract(_lowerCamelCase   ,     max_length=_lowerCamelCase   ,     padding=_lowerCamelCase    )
																					__snake_case		:      str										=       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    __snake_case (							self	:  str    )     ->   str:
														__snake_case		:      Optional[int]										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
														__snake_case		:      Optional[int]										=       [floats_list((1, x)    )[0] for x in range(800   ,     1400   ,     200    )]
														__snake_case		:      Optional[Any]										=       feat_extract(
														    _lowerCamelCase   ,     truncation=_lowerCamelCase   ,     max_length=1000   ,     padding="max_length"   ,     return_tensors="np"    )
														__snake_case		:      Tuple										=       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    __snake_case (							self	:  List[str]    )     ->   str:
														__snake_case		:      Optional[int]										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
														__snake_case		:      List[Any]										=       [floats_list((1, x)    )[0] for x in range(800   ,     1400   ,     200    )]
														__snake_case		:      Dict										=       feat_extract(
														    _lowerCamelCase   ,     truncation=_lowerCamelCase   ,     max_length=1000   ,     padding="longest"   ,     return_tensors="np"    )
														__snake_case		:      List[str]										=       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)    )

														__snake_case		:      Optional[Any]										=       [floats_list((1, x)    )[0] for x in range(800   ,     1400   ,     200    )]
														__snake_case		:      List[str]										=       feat_extract(
														    _lowerCamelCase   ,     truncation=_lowerCamelCase   ,     max_length=2000   ,     padding="longest"   ,     return_tensors="np"    )
														__snake_case		:      Optional[int]										=       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)    )

							@require_torch
							def    __snake_case (							self	:  int    )     ->   List[str]:
														import torch

														__snake_case		:      Union[str, Any]										=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
														__snake_case		:      List[str]										=       np.random.rand(100    ).astype(np.floataa    )
														__snake_case		:      Optional[Any]										=       np_speech_inputs.tolist()

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






							@slow
							@require_torch
							def    __snake_case (							self	:  List[str]    )     ->   Dict:







														for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
																					__snake_case		:      Optional[int]										=       WavaVecaConfig.from_pretrained(_lowerCamelCase    )
																					__snake_case		:      Any										=       WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase    )

																					# only "layer" feature extraction norm should make use of
																					# attention_mask
																					self.assertEqual(feat_extract.return_attention_mask   ,     config.feat_extract_norm == "layer"    )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Any      =    logging.get_logger(__name__)

_snake_case					:		Dict      =    {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  "speech_to_text_2"
							__UpperCAmelCase   :							Dict					  =  ["past_key_values"]
							__UpperCAmelCase   :							str					  =  {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}

							def __init__(							self	:  str   ,     lowerCamelCase	:  List[Any]=10000   ,     lowerCamelCase	:  Tuple=6   ,     lowerCamelCase	:  Optional[Any]=2048   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  str="relu"   ,     lowerCamelCase	:  Tuple=256   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  Any=0.0   ,     lowerCamelCase	:  Optional[int]=0.02   ,     lowerCamelCase	:  Dict=2   ,     lowerCamelCase	:  List[str]=True   ,     lowerCamelCase	:  str=1   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  List[Any]=1024   ,     **lowerCamelCase	:  List[str]   ,     )     ->   Any:
														__snake_case		:      Tuple										=       vocab_size
														__snake_case		:      int										=       d_model
														__snake_case		:      List[str]										=       decoder_ffn_dim
														__snake_case		:      Union[str, Any]										=       decoder_layers
														__snake_case		:      Dict										=       decoder_attention_heads
														__snake_case		:      Any										=       dropout
														__snake_case		:      List[str]										=       attention_dropout
														__snake_case		:      Optional[Any]										=       activation_dropout
														__snake_case		:      Union[str, Any]										=       activation_function
														__snake_case		:      Union[str, Any]										=       init_std
														__snake_case		:      Union[str, Any]										=       decoder_layerdrop
														__snake_case		:      Optional[int]										=       use_cache
														__snake_case		:      Optional[int]										=       decoder_layers
														__snake_case		:      str										=       scale_embedding  # scale factor will be sqrt(d_model) if True
														__snake_case		:      Dict										=       max_target_positions

														super().__init__(
														    pad_token_id=lowerCamelCase   ,     bos_token_id=lowerCamelCase   ,     eos_token_id=lowerCamelCase   ,     decoder_start_token_id=lowerCamelCase   ,     **lowerCamelCase   ,     )

