'''simple docstring'''

from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
							import importlib_metadata
else:
							import importlib.metadata as importlib_metadata


lowercase_     		= ''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
							class   UpperCamelCase_			(						tr.AbstractTransform):







									"""simple docstring"""






									def __init__(   self		,						__A = " "				) ->     str:
															_lowerCAmelCase										=sentence_delimiter






									def 		UpperCamelCase__       (   self		,						__A				) ->     Optional[Any]:
															return list(__A				)






									def 		UpperCamelCase__       (   self		,						__A				) ->     Any:
															_lowerCAmelCase										=[]
															for sent_idx, sentence in enumerate(__A				):
																					chars.extend(self.process_string(__A				)				)
																					if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__A				) - 1:
																											chars.append(self.sentence_delimiter				)
															return chars

							lowercase_     		= tr.Compose(
							    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
							)
else:
							lowercase_     		= tr.Compose(
							    [
							        tr.RemoveMultipleSpaces(),
							        tr.Strip(),
							        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
							        tr.ReduceToListOfListOfChars(),
							    ]
							)


lowercase_     		= '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

lowercase_     		= '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

lowercase_     		= '''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION)
class   UpperCamelCase_			(						datasets.Metric):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION		,						citation=_CITATION		,						inputs_description=_KWARGS_DESCRIPTION		,						features=datasets.Features(
								        {
								            'predictions': datasets.Value('string'		,						id='sequence'				),
								            'references': datasets.Value('string'		,						id='sequence'				),
								        }				)		,						codebase_urls=['https://github.com/jitsi/jiwer/']		,						reference_urls=[
								        'https://en.wikipedia.org/wiki/Word_error_rate',
								        'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
								    ]		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A=False				) ->     str:
								if concatenate_texts:
														return jiwer.compute_measures(
														    __A		,						__A		,						truth_transform=__A		,						hypothesis_transform=__A		,						)["wer"]

								_lowerCAmelCase										=0
								_lowerCAmelCase										=0
								for prediction, reference in zip(__A		,						__A				):
														_lowerCAmelCase										=jiwer.compute_measures(
														    __A		,						__A		,						truth_transform=__A		,						hypothesis_transform=__A		,						)
														incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
														total += measures["substitutions"] + measures["deletions"] + measures["hits"]

								return incorrect / total






'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase):







		"""simple docstring"""






		@register_to_config
		def __init__(   self		,						__A = 128		,						__A = 256		,						__A = 2_000.0		,						__A = 768		,						__A = 12		,						__A = 12		,						__A = 64		,						__A = 2048		,						__A = 0.1		,						) ->     str:
								super().__init__()

								_lowerCAmelCase										=nn.Sequential(
								    nn.Linear(__A		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						nn.Linear(d_model * 4		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						)

								_lowerCAmelCase										=nn.Embedding(__A		,						__A				)
								_lowerCAmelCase										=False

								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)

								_lowerCAmelCase										=nn.ModuleList()
								for lyr_num in range(__A				):
														# FiLM conditional T5 decoder
														_lowerCAmelCase										=DecoderLayer(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						d_ff=__A		,						dropout_rate=__A				)
														self.decoders.append(__A				)

								_lowerCAmelCase										=TaLayerNorm(__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Any:
								_lowerCAmelCase										=torch.mul(query_input.unsqueeze(-1				)		,						key_input.unsqueeze(-2				)				)
								return mask.unsqueeze(-3				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								_lowerCAmelCase										=get_timestep_embedding(
								    decoder_noise_time * self.config.max_decoder_noise_time		,						embedding_dim=self.config.d_model		,						max_period=self.config.max_decoder_noise_time		,						).to(dtype=self.dtype				)

								_lowerCAmelCase										=self.conditioning_emb(__A				).unsqueeze(1				)

								assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

								_lowerCAmelCase										=decoder_input_tokens.shape[1]

								# If we want to use relative positions for audio context, we can just offset
								# this sequence by the length of encodings_and_masks.
								_lowerCAmelCase										=torch.broadcast_to(
								    torch.arange(__A		,						device=decoder_input_tokens.device				)		,						(batch, seq_length)		,						)

								_lowerCAmelCase										=self.position_encoding(__A				)

								_lowerCAmelCase										=self.continuous_inputs_projection(__A				)
								inputs += position_encodings
								_lowerCAmelCase										=self.dropout(__A				)

								# decoder: No padding present.
								_lowerCAmelCase										=torch.ones(
								    decoder_input_tokens.shape[:2]		,						device=decoder_input_tokens.device		,						dtype=inputs.dtype				)

								# Translate encoding masks to encoder-decoder masks.
								_lowerCAmelCase										=[(x, self.encoder_decoder_mask(__A		,						__A				)) for x, y in encodings_and_masks]

								# cross attend style: concat encodings
								_lowerCAmelCase										=torch.cat([x[0] for x in encodings_and_encdec_masks]		,						dim=1				)
								_lowerCAmelCase										=torch.cat([x[1] for x in encodings_and_encdec_masks]		,						dim=-1				)

								for lyr in self.decoders:
														_lowerCAmelCase										=lyr(
														    __A		,						conditioning_emb=__A		,						encoder_hidden_states=__A		,						encoder_attention_mask=__A		,						)[0]

								_lowerCAmelCase										=self.decoder_norm(__A				)
								_lowerCAmelCase										=self.post_dropout(__A				)

								_lowerCAmelCase										=self.spec_out(__A				)
								return spec_out

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A=1E-6				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.ModuleList()

								# cond self attention: layer 0
								self.layer.append(
								    TaLayerSelfAttentionCond(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A				)				)

								# cross attention: layer 1
								self.layer.append(
								    TaLayerCrossAttention(
								        d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A		,						)				)

								# Film Cond MLP + dropout: last layer
								self.layer.append(
								    TaLayerFFCond(d_model=__A		,						d_ff=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A				)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						) ->     Any:
								_lowerCAmelCase										=self.layer[0](
								    __A		,						conditioning_emb=__A		,						attention_mask=__A		,						)

								if encoder_hidden_states is not None:
														_lowerCAmelCase										=torch.where(encoder_attention_mask > 0		,						0		,						-1E10				).to(
														    encoder_hidden_states.dtype				)

														_lowerCAmelCase										=self.layer[1](
														    __A		,						key_value_states=__A		,						attention_mask=__A		,						)

								# Apply Film Conditional Feed Forward layer
								_lowerCAmelCase										=self.layer[-1](__A		,						__A				)

								return (hidden_states,)

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaLayerNorm(__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     List[Any]:
								# pre_self_attention_layer_norm
								_lowerCAmelCase										=self.layer_norm(__A				)

								if conditioning_emb is not None:
														_lowerCAmelCase										=self.FiLMLayer(__A		,						__A				)

								# Self-attention block
								_lowerCAmelCase										=self.attention(__A				)

								_lowerCAmelCase										=hidden_states + self.dropout(__A				)

								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								super().__init__()
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     Tuple:
								_lowerCAmelCase										=self.layer_norm(__A				)
								_lowerCAmelCase										=self.attention(
								    __A		,						encoder_hidden_states=__A		,						attention_mask=attention_mask.squeeze(1				)		,						)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return layer_output

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaDenseGatedActDense(d_model=__A		,						d_ff=__A		,						dropout_rate=__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     List[Any]:
								_lowerCAmelCase										=self.layer_norm(__A				)
								if conditioning_emb is not None:
														_lowerCAmelCase										=self.film(__A		,						__A				)

								_lowerCAmelCase										=self.DenseReluDense(__A				)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)
								_lowerCAmelCase										=NewGELUActivation()






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=self.act(self.wi_a(__A				)				)
								_lowerCAmelCase										=self.wi_a(__A				)
								_lowerCAmelCase										=hidden_gelu * hidden_linear
								_lowerCAmelCase										=self.dropout(__A				)

								_lowerCAmelCase										=self.wo(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=1E-6				) ->     int:
								super().__init__()
								_lowerCAmelCase										=nn.Parameter(torch.ones(__A				)				)
								_lowerCAmelCase										=eps






		def 		UpperCamelCase__       (   self		,						__A				) ->     Dict:
								# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
								# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
								# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
								# half-precision inputs is done in fp32

								_lowerCAmelCase										=hidden_states.to(torch.floataa				).pow(2				).mean(-1		,						keepdim=__A				)
								_lowerCAmelCase										=hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

								# convert into half-precision if necessary
								if self.weight.dtype in [torch.floataa, torch.bfloataa]:
														_lowerCAmelCase										=hidden_states.to(self.weight.dtype				)

								return self.weight * hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A				) ->     torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.044_715 * torch.pow(__A		,						3.0				))				))

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						out_features * 2		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase										=self.scale_bias(__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=torch.chunk(__A		,						2		,						-1				)
								_lowerCAmelCase										=x * (1 + scale) + shift
								return x





