


"""simple docstring"""


import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset

A	:  Dict            =     random.Random()


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase=1.0       ,					_UpperCamelCase=None       ,					_UpperCamelCase=None ):

			'''simple docstring'''




			if rng is None:
						__lowerCAmelCase							       =     global_rng

			__lowerCAmelCase							       =     []
			for batch_idx in range(shape[0] ):
						values.append([] )
						for _ in range(shape[1] ):
									values[-1].append(rng.random() * scale )

			return values
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a=7       ,  __a=4_00       ,  __a=20_00       ,  __a=20_48       ,  __a=1_28       ,  __a=1       ,  __a=5_12       ,  __a=30       ,  __a=4_41_00       ,  ):
										__lowerCAmelCase							       =     parent
										__lowerCAmelCase							       =     batch_size
										__lowerCAmelCase							       =     min_seq_length
										__lowerCAmelCase							       =     max_seq_length
										__lowerCAmelCase							       =     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										__lowerCAmelCase							       =     spectrogram_length
										__lowerCAmelCase							       =     feature_size
										__lowerCAmelCase							       =     num_audio_channels
										__lowerCAmelCase							       =     hop_length
										__lowerCAmelCase							       =     chunk_length
										__lowerCAmelCase							       =     sampling_rate

							def      snake_case							(	self      ):
										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}



							def      snake_case							(	self       ,  __a=False       ,  __a=False      ):
										def _flatten(__a      ):
													return list(itertools.chain(*__a      )      )

										if equal_length:
													__lowerCAmelCase							       =     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
										else:
													# make sure that inputs increase in size
													__lowerCAmelCase							       =     [
													    floats_list((x, self.feature_size)      )
													    for x in range(self.min_seq_length       ,  self.max_seq_length       ,  self.seq_length_diff      )
													]
										if numpify:
													__lowerCAmelCase							       =     [np.asarray(__a      ) for x in speech_inputs]
										return speech_inputs
@require_torch
@require_torchaudio
class     _UpperCamelCase    (  lowerCAmelCase__			,unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =TvltFeatureExtractor

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     TvltFeatureExtractionTester(self      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.feature_extraction_class(**self.feat_extract_dict      )
										self.assertTrue(hasattr(__a       ,  "spectrogram_length"      )      )
										self.assertTrue(hasattr(__a       ,  "feature_size"      )      )
										self.assertTrue(hasattr(__a       ,  "num_audio_channels"      )      )
										self.assertTrue(hasattr(__a       ,  "hop_length"      )      )
										self.assertTrue(hasattr(__a       ,  "chunk_length"      )      )
										self.assertTrue(hasattr(__a       ,  "sampling_rate"      )      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.feature_extraction_class(**self.feat_extract_dict      )

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase							       =     feat_extract_first.save_pretrained(__a      )[0]
													check_json_file_has_correct_format(__a      )
													__lowerCAmelCase							       =     self.feature_extraction_class.from_pretrained(__a      )

										__lowerCAmelCase							       =     feat_extract_first.to_dict()
										__lowerCAmelCase							       =     feat_extract_second.to_dict()
										__lowerCAmelCase							       =     dict_first.pop("mel_filters"      )
										__lowerCAmelCase							       =     dict_second.pop("mel_filters"      )
										self.assertTrue(np.allclose(__a       ,  __a      )      )
										self.assertEqual(__a       ,  __a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self.feature_extraction_class(**self.feat_extract_dict      )

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase							       =     os.path.join(__a       ,  "feat_extract.json"      )
													feat_extract_first.to_json_file(__a      )
													__lowerCAmelCase							       =     self.feature_extraction_class.from_json_file(__a      )

										__lowerCAmelCase							       =     feat_extract_first.to_dict()
										__lowerCAmelCase							       =     feat_extract_second.to_dict()
										__lowerCAmelCase							       =     dict_first.pop("mel_filters"      )
										__lowerCAmelCase							       =     dict_second.pop("mel_filters"      )
										self.assertTrue(np.allclose(__a       ,  __a      )      )
										self.assertEqual(__a       ,  __a      )

							def      snake_case							(	self      ):
										# Initialize feature_extractor
										__lowerCAmelCase							       =     self.feature_extraction_class(**self.feat_extract_dict      )

										# create three inputs of length 800, 1000, and 1200
										__lowerCAmelCase							       =     [floats_list((1, x)      )[0] for x in range(8_00       ,  14_00       ,  2_00      )]
										__lowerCAmelCase							       =     [np.asarray(__a      ) for speech_input in speech_inputs]

										# Test not batched input
										__lowerCAmelCase							       =     feature_extractor(np_speech_inputs[0]       ,  return_tensors="np"       ,  sampling_rate=4_41_00      ).audio_values

										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

										# Test batched
										__lowerCAmelCase							       =     feature_extractor(__a       ,  return_tensors="np"       ,  sampling_rate=4_41_00      ).audio_values

										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

										# Test audio masking
										__lowerCAmelCase							       =     feature_extractor(
										    __a       ,  return_tensors="np"       ,  sampling_rate=4_41_00       ,  mask_audio=__a      ).audio_values

										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

										# Test 2-D numpy arrays are batched.
										__lowerCAmelCase							       =     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
										__lowerCAmelCase							       =     np.asarray(__a      )
										__lowerCAmelCase							       =     feature_extractor(__a       ,  return_tensors="np"       ,  sampling_rate=4_41_00      ).audio_values
										self.assertTrue(encoded_audios.ndim == 4      )
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

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

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



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     self._load_datasamples(1      )
										__lowerCAmelCase							       =     TvltFeatureExtractor()
										__lowerCAmelCase							       =     feature_extractor(__a       ,  return_tensors="pt"      ).audio_values

										self.assertEquals(audio_values.shape       ,  (1, 1, 1_92, 1_28)      )

										__lowerCAmelCase							       =     torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]      )
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]       ,  __a       ,  atol=1e-4      )      )








"""simple docstring"""


import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


A	:  Dict            =     get_logger(__name__)


A	:  Dict            =     R"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    :
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a      ):
										raise NotImplementedError(
										    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."      )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							@add_start_docstrings(__a      )
							def __call__(	self       ,  __a       ,  __a       ,  __a       ,  **__a      ):
										for processor in self:
													__lowerCAmelCase							       =     inspect.signature(processor.__call__      ).parameters
													if len(__a      ) > 3:
																if not all(arg in kwargs for arg in list(function_args.keys()      )[2:]      ):
																			raise ValueError(
																			    f"Make sure that all the required parameters: {list(function_args.keys()      )} for "
																			    f"{processor.__class__} are passed to the logits processor."      )
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a       ,  **__a      )
													else:
																__lowerCAmelCase							       =     processor(__a       ,  __a       ,  __a      )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or not (temperature > 0):
													raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}"      )

										__lowerCAmelCase							       =     temperature



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores / self.temperature
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) or (top_p < 0 or top_p > 1.0):
													raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}"      )
										if not isinstance(__a       ,  __a      ) or (min_tokens_to_keep < 1):
													raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}"      )

										__lowerCAmelCase							       =     top_p
										__lowerCAmelCase							       =     filter_value
										__lowerCAmelCase							       =     min_tokens_to_keep



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  scores.shape[-1]      )

										__lowerCAmelCase							       =     jnp.full_like(__a       ,  self.filter_value      )
										__lowerCAmelCase							       =     jax.nn.softmax(__a       ,  axis=-1      ).cumsum(axis=-1      )
										__lowerCAmelCase							       =     cumulative_probs < self.top_p

										# include the token that is higher than top_p as well
										__lowerCAmelCase							       =     jnp.roll(__a       ,  1      )
										score_mask |= score_mask.at[:, 0].set(__a      )

										# min tokens to keep
										__lowerCAmelCase							       =     score_mask.at[:, : self.min_tokens_to_keep].set(__a      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jax.lax.sort_key_val(__a       ,  __a      )[-1]

										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a = -float("Inf"      )       ,  __a = 1      ):
										if not isinstance(__a       ,  __a      ) or top_k <= 0:
													raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}"      )

										__lowerCAmelCase							       =     max(__a       ,  __a      )
										__lowerCAmelCase							       =     filter_value



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     scores.shape
										__lowerCAmelCase							       =     jnp.full(batch_size * vocab_size       ,  self.filter_value      )

										__lowerCAmelCase							       =     min(self.top_k       ,  scores.shape[-1]      )  # Safety check
										__lowerCAmelCase       ,      __lowerCAmelCase							       =     lax.top_k(__a       ,  __a      )
										__lowerCAmelCase							       =     jnp.broadcast_to((jnp.arange(__a      ) * vocab_size)[:, None]       ,  (batch_size, topk)      ).flatten()
										__lowerCAmelCase							       =     topk_scores.flatten()
										__lowerCAmelCase							       =     topk_indices.flatten() + shift

										__lowerCAmelCase							       =     next_scores_flat.at[topk_indices_flat].set(__a      )
										__lowerCAmelCase							       =     next_scores_flat.reshape(__a       ,  __a      )
										return next_scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     bos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.bos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     max_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     jnp.full(scores.shape       ,  -float("inf"      )      )

										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.max_length + 1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  new_scores.at[:, self.eos_token_id].set(0      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										if not isinstance(__a       ,  __a      ) or min_length < 0:
													raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}"      )

										if not isinstance(__a       ,  __a      ) or eos_token_id < 0:
													raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}"      )

										__lowerCAmelCase							       =     min_length
										__lowerCAmelCase							       =     eos_token_id



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# create boolean flag to decide if min length penalty should be applied
										__lowerCAmelCase							       =     1 - jnp.clip(cur_len - self.min_length       ,  0       ,  1      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.eos_token_id].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )
										__lowerCAmelCase							       =     begin_index



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     1 - jnp.bool_(cur_len - self.begin_index      )

										__lowerCAmelCase							       =     jnp.where(__a       ,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"      )      )       ,  __a      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     list(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     scores.at[..., self.suppress_tokens].set(-float("inf"      )      )

										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     dict(__a      )
										# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
										# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
										# Indexes without forced tokens will have a negative value.
										__lowerCAmelCase							       =     jnp.ones((max(force_token_map.keys()      ) + 1)       ,  dtype=jnp.intaa      ) * -1
										for index, token in force_token_map.items():
													if token is not None:
																__lowerCAmelCase							       =     force_token_array.at[index].set(__a      )
										__lowerCAmelCase							       =     jnp.intaa(__a      )



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										def _force_token(__a      ):
													__lowerCAmelCase							       =     scores.shape[0]
													__lowerCAmelCase							       =     self.force_token_array[generation_idx]

													__lowerCAmelCase							       =     jnp.ones_like(__a       ,  dtype=scores.dtype      ) * -float("inf"      )
													__lowerCAmelCase							       =     jnp.zeros((batch_size, 1)       ,  dtype=scores.dtype      )
													__lowerCAmelCase							       =     lax.dynamic_update_slice(__a       ,  __a       ,  (0, current_token)      )
													return new_scores

										__lowerCAmelCase							       =     lax.cond(
										    cur_len >= self.force_token_array.shape[0]       ,  lambda: scores       ,  lambda: lax.cond(
										        self.force_token_array[cur_len] >= 0       ,  lambda: _force_token(__a      )       ,  lambda: scores       ,  )       ,  )
										return scores
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     generate_config.eos_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id
										__lowerCAmelCase							       =     generate_config.no_timestamps_token_id + 1

										__lowerCAmelCase							       =     decoder_input_length + 1

										if generate_config.is_multilingual:
													# room for language token and task token
													self.begin_index += 2
										if hasattr(__a       ,  "max_initial_timestamp_index"      ):
													__lowerCAmelCase							       =     generate_config.max_initial_timestamp_index
										else:
													__lowerCAmelCase							       =     model_config.vocab_size
										if self.max_initial_timestamp_index is None:
													__lowerCAmelCase							       =     model_config.vocab_size



							def __call__(	self       ,  __a       ,  __a       ,  __a      ):
										# suppress <|notimestamps|> which is handled by without_timestamps
										__lowerCAmelCase							       =     scores.at[:, self.no_timestamps_token_id].set(-float("inf"      )      )

										def handle_pairs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) >= 1       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 1] >= self.timestamp_begin       ,  True and last_was_timestamp       ,  __a       ,  )

													__lowerCAmelCase							       =     jnp.where((cur_len - self.begin_index) < 2       ,  __a       ,  __a      )
													__lowerCAmelCase							       =     jnp.where(
													    input_ids_k[cur_len - 2] >= self.timestamp_begin       ,  __a       ,  __a       ,  )

													return jnp.where(
													    __a       ,  jnp.where(
													        penultimate_was_timestamp > 0       ,  scores_k.at[self.timestamp_begin :].set(-float("inf"      )      )       ,  scores_k.at[: self.eos_token_id].set(-float("inf"      )      )       ,  )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										__lowerCAmelCase							       =     jnp.where(cur_len == self.begin_index       ,  __a       ,  __a      )
										__lowerCAmelCase							       =     jnp.where(
										    self.max_initial_timestamp_index is not None       ,  True and apply_max_initial_timestamp       ,  __a       ,  )

										__lowerCAmelCase							       =     self.timestamp_begin + self.max_initial_timestamp_index

										__lowerCAmelCase							       =     jnp.where(
										    __a       ,  scores.at[:, last_allowed + 1 :].set(-float("inf"      )      )       ,  __a       ,  )

										# if sum of probability over timestamps is above any other token, sample timestamp
										__lowerCAmelCase							       =     jax.nn.log_softmax(__a       ,  axis=-1      )

										def handle_cumulative_probs(__a       ,  __a      ):
													__lowerCAmelCase							       =     jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]       ,  axis=-1      )
													__lowerCAmelCase							       =     jnp.max(logprobs_k[: self.timestamp_begin]      )
													return jnp.where(
													    timestamp_logprob > max_text_token_logprob       ,  scores_k.at[: self.timestamp_begin].set(-float("inf"      )      )       ,  __a       ,  )

										__lowerCAmelCase							       =     jax.vmap(__a      )(__a       ,  __a      )

										return scores




