





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger

class     __SCREAMING_SNAKE_CASE   (       lowerCAmelCase__    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     params

														UpperCamelCase__			       =     np.array(_SCREAMING_SNAKE_CASE					)
														UpperCamelCase__			       =     np.array([len(_SCREAMING_SNAKE_CASE					) for t in data]					)

														self.check()
														self.remove_long_sequences()
														self.remove_empty_sequences()
														self.remove_unknown_sequences()
														self.check()
														self.print_statistics()






							def __getitem__(  self					,	__lowerCAmelCase					):
														return (self.token_ids[index], self.lengths[index])






							def __len__(  self					):
														return len(self.lengths					)






							def 			_lowerCamelCase						(  self					):
														assert len(self.token_ids					) == len(self.lengths					)
														assert all(self.lengths[i] == len(self.token_ids[i]					) for i in range(len(self.lengths					)					)					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.params.max_model_input_size
														UpperCamelCase__			       =     self.lengths > max_len
														logger.info(f"""Splitting {sum(_SCREAMING_SNAKE_CASE					)} too long sequences."""					)

														def divide_chunks(__lowerCAmelCase					,	__lowerCAmelCase					):
																					return [l[i : i + n] for i in range(0					,	len(_SCREAMING_SNAKE_CASE					)					,	_SCREAMING_SNAKE_CASE					)]

														UpperCamelCase__			       =     []
														UpperCamelCase__			       =     []
														if self.params.mlm:
																					UpperCamelCase__       ,       UpperCamelCase__			       =     self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
														else:
																					UpperCamelCase__       ,       UpperCamelCase__			       =     self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

														for seq_, len_ in zip(self.token_ids					,	self.lengths					):
																					assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																					if len_ <= max_len:
																												new_tok_ids.append(seq_					)
																												new_lengths.append(len_					)
																					else:
																												UpperCamelCase__			       =     []
																												for sub_s in divide_chunks(seq_					,	max_len - 2					):
																																			if sub_s[0] != cls_id:
																																										UpperCamelCase__			       =     np.insert(_SCREAMING_SNAKE_CASE					,	0					,	_SCREAMING_SNAKE_CASE					)
																																			if sub_s[-1] != sep_id:
																																										UpperCamelCase__			       =     np.insert(_SCREAMING_SNAKE_CASE					,	len(_SCREAMING_SNAKE_CASE					)					,	_SCREAMING_SNAKE_CASE					)
																																			assert len(_SCREAMING_SNAKE_CASE					) <= max_len
																																			assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																			sub_seqs.append(_SCREAMING_SNAKE_CASE					)

																												new_tok_ids.extend(_SCREAMING_SNAKE_CASE					)
																												new_lengths.extend([len(_SCREAMING_SNAKE_CASE					) for l in sub_seqs]					)

														UpperCamelCase__			       =     np.array(_SCREAMING_SNAKE_CASE					)
														UpperCamelCase__			       =     np.array(_SCREAMING_SNAKE_CASE					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     len(self					)
														UpperCamelCase__			       =     self.lengths > 11
														UpperCamelCase__			       =     self.token_ids[indices]
														UpperCamelCase__			       =     self.lengths[indices]
														UpperCamelCase__			       =     len(self					)
														logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""					)






							def 			_lowerCamelCase						(  self					):
														if "unk_token" not in self.params.special_tok_ids:
																					return
														else:
																					UpperCamelCase__			       =     self.params.special_tok_ids["""unk_token"""]
														UpperCamelCase__			       =     len(self					)
														UpperCamelCase__			       =     np.array([np.count_nonzero(a == unk_token_id					) for a in self.token_ids]					)
														UpperCamelCase__			       =     (unk_occs / self.lengths) < 0.5
														UpperCamelCase__			       =     self.token_ids[indices]
														UpperCamelCase__			       =     self.lengths[indices]
														UpperCamelCase__			       =     len(self					)
														logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""					)






							def 			_lowerCamelCase						(  self					):
														if not self.params.is_master:
																					return
														logger.info(f"""{len(self					)} sequences"""					)
														# data_len = sum(self.lengths)
														# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
														# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

														# unk_idx = self.params.special_tok_ids['unk_token']
														# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
														# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     [t[0] for t in batch]
														UpperCamelCase__			       =     [t[1] for t in batch]
														assert len(_SCREAMING_SNAKE_CASE					) == len(_SCREAMING_SNAKE_CASE					)

														# Max for paddings
														UpperCamelCase__			       =     max(_SCREAMING_SNAKE_CASE					)

														# Pad token ids
														if self.params.mlm:
																					UpperCamelCase__			       =     self.params.special_tok_ids["""pad_token"""]
														else:
																					UpperCamelCase__			       =     self.params.special_tok_ids["""unk_token"""]
														UpperCamelCase__			       =     [list(t.astype(_SCREAMING_SNAKE_CASE					)					) + [pad_idx] * (max_seq_len_ - len(_SCREAMING_SNAKE_CASE					)) for t in token_ids]
														assert len(tk_					) == len(_SCREAMING_SNAKE_CASE					)
														assert all(len(_SCREAMING_SNAKE_CASE					) == max_seq_len_ for t in tk_					)

														UpperCamelCase__			       =     torch.tensor(tk_					)  # (bs, max_seq_len_)
														UpperCamelCase__			       =     torch.tensor(_SCREAMING_SNAKE_CASE					)  # (bs)
														return tk_t, lg_t











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


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{
    "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     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Dict         =    """speech_to_text_2"""
							snake_case       : List[Any]         =    ["""past_key_values"""]
							snake_case       : List[str]         =    {"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}






							def __init__(  self					,	__lowerCAmelCase=10000					,	__lowerCAmelCase=6					,	__lowerCAmelCase=2048					,	__lowerCAmelCase=4					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=True					,	__lowerCAmelCase="relu"					,	__lowerCAmelCase=256					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.0					,	__lowerCAmelCase=0.02					,	__lowerCAmelCase=2					,	__lowerCAmelCase=True					,	__lowerCAmelCase=1					,	__lowerCAmelCase=0					,	__lowerCAmelCase=2					,	__lowerCAmelCase=1024					,	**__lowerCAmelCase					,	):
														UpperCamelCase__			       =     vocab_size
														UpperCamelCase__			       =     d_model
														UpperCamelCase__			       =     decoder_ffn_dim
														UpperCamelCase__			       =     decoder_layers
														UpperCamelCase__			       =     decoder_attention_heads
														UpperCamelCase__			       =     dropout
														UpperCamelCase__			       =     attention_dropout
														UpperCamelCase__			       =     activation_dropout
														UpperCamelCase__			       =     activation_function
														UpperCamelCase__			       =     init_std
														UpperCamelCase__			       =     decoder_layerdrop
														UpperCamelCase__			       =     use_cache
														UpperCamelCase__			       =     decoder_layers
														UpperCamelCase__			       =     scale_embedding  # scale factor will be sqrt(d_model) if True
														UpperCamelCase__			       =     max_target_positions

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




