import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__A  =				"\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

__A  =				"\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n"


__A  =				"\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"


def    lowercase_   (   _lowerCamelCase:	Any      ) ->      Dict:



							'''simple docstring'''

							def remove_articles(_lowerCamelCase:	Union[str, Any]      ):
														__lowerCamelCase					:		Tuple		   =     re.compile(r"\b(a|an|the)\b"						,   re.UNICODE      )
														return re.sub(_lowerCamelCase						,   " "						,   _lowerCamelCase      )

							def white_space_fix(_lowerCamelCase:	Tuple      ):
														return " ".join(text.split()      )

							def remove_punc(_lowerCamelCase:	Union[str, Any]      ):
														__lowerCamelCase					:		Optional[Any]		   =     set(string.punctuation      )
														return "".join(ch for ch in text if ch not in exclude      )

							def lower(_lowerCamelCase:	Optional[Any]      ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase      )      )      )      )


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Any]      ) ->      Tuple:



							'''simple docstring'''

							return int(normalize_answer(_lowerCamelCase      ) == normalize_answer(_lowerCamelCase      )      )


def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	Tuple      ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     [any(compute_exact(_lowerCamelCase						,   _lowerCamelCase      ) for ref in refs      ) for pred, refs in zip(_lowerCamelCase						,   _lowerCamelCase      )]
							return (sum(_lowerCamelCase      ) / len(_lowerCamelCase      )) * 100


def    lowercase_   (   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]      ) ->      int:



							'''simple docstring'''

							__lowerCamelCase					:		int		   =     [rgram for rgrams in rgramslist for rgram in rgrams]
							__lowerCamelCase					:		int		   =     Counter(_lowerCamelCase      )

							__lowerCamelCase					:		str		   =     Counter(_lowerCamelCase      )
							__lowerCamelCase					:		Union[str, Any]		   =     Counter()
							for sgram, scount in sgramcounter.items():
														__lowerCamelCase					:		List[str]		   =     scount * numref

							__lowerCamelCase					:		Tuple		   =     Counter(_lowerCamelCase      )
							__lowerCamelCase					:		int		   =     Counter()
							for cgram, ccount in cgramcounter.items():
														__lowerCamelCase					:		List[str]		   =     ccount * numref

							# KEEP
							__lowerCamelCase					:		Tuple		   =     sgramcounter_rep & cgramcounter_rep
							__lowerCamelCase					:		Any		   =     keepgramcounter_rep & rgramcounter
							__lowerCamelCase					:		Union[str, Any]		   =     sgramcounter_rep & rgramcounter

							__lowerCamelCase					:		Optional[int]		   =     0
							__lowerCamelCase					:		List[Any]		   =     0
							for keepgram in keepgramcountergood_rep:
														keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
														# Fix an alleged bug [2] in the keep score computation.
														# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
														keeptmpscorea += keepgramcountergood_rep[keepgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							#      a target exactly.
							__lowerCamelCase					:		int		   =     1
							__lowerCamelCase					:		List[Any]		   =     1
							if len(_lowerCamelCase      ) > 0:
														__lowerCamelCase					:		str		   =     keeptmpscorea / len(_lowerCamelCase      )
							if len(_lowerCamelCase      ) > 0:
														# Fix an alleged bug [2] in the keep score computation.
														# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
														__lowerCamelCase					:		List[str]		   =     keeptmpscorea / sum(keepgramcounterall_rep.values()      )
							__lowerCamelCase					:		str		   =     0
							if keepscore_precision > 0 or keepscore_recall > 0:
														__lowerCamelCase					:		Any		   =     2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

							# DELETION
							__lowerCamelCase					:		List[Any]		   =     sgramcounter_rep - cgramcounter_rep
							__lowerCamelCase					:		Tuple		   =     delgramcounter_rep - rgramcounter
							__lowerCamelCase					:		int		   =     sgramcounter_rep - rgramcounter
							__lowerCamelCase					:		Any		   =     0
							__lowerCamelCase					:		List[Any]		   =     0
							for delgram in delgramcountergood_rep:
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
														deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							__lowerCamelCase					:		Union[str, Any]		   =     1
							if len(_lowerCamelCase      ) > 0:
														__lowerCamelCase					:		Dict		   =     deltmpscorea / len(_lowerCamelCase      )

							# ADDITION
							__lowerCamelCase					:		List[Any]		   =     set(_lowerCamelCase      ) - set(_lowerCamelCase      )
							__lowerCamelCase					:		List[Any]		   =     set(_lowerCamelCase      ) & set(_lowerCamelCase      )
							__lowerCamelCase					:		Tuple		   =     set(_lowerCamelCase      ) - set(_lowerCamelCase      )

							__lowerCamelCase					:		List[str]		   =     0
							for addgram in addgramcountergood:
														addtmpscore += 1

							# Define 0/0=1 instead of 0 to give higher scores for predictions that match
							# a target exactly.
							__lowerCamelCase					:		Optional[Any]		   =     1
							__lowerCamelCase					:		int		   =     1
							if len(_lowerCamelCase      ) > 0:
														__lowerCamelCase					:		Tuple		   =     addtmpscore / len(_lowerCamelCase      )
							if len(_lowerCamelCase      ) > 0:
														__lowerCamelCase					:		str		   =     addtmpscore / len(_lowerCamelCase      )
							__lowerCamelCase					:		Tuple		   =     0
							if addscore_precision > 0 or addscore_recall > 0:
														__lowerCamelCase					:		Tuple		   =     2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

							return (keepscore, delscore_precision, addscore)


def    lowercase_   (   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Optional[Any]      ) ->      str:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[Any]		   =     len(_lowerCamelCase      )

							__lowerCamelCase					:		List[str]		   =     ssent.split(" "      )
							__lowerCamelCase					:		List[Any]		   =     csent.split(" "      )
							__lowerCamelCase					:		int		   =     []
							__lowerCamelCase					:		Optional[int]		   =     []
							__lowerCamelCase					:		str		   =     []
							__lowerCamelCase					:		Dict		   =     []
							__lowerCamelCase					:		Optional[Any]		   =     []
							__lowerCamelCase					:		List[str]		   =     []

							__lowerCamelCase					:		int		   =     []
							__lowerCamelCase					:		Optional[Any]		   =     []
							__lowerCamelCase					:		Optional[Any]		   =     []
							__lowerCamelCase					:		Optional[Any]		   =     []
							for rsent in rsents:
														__lowerCamelCase					:		List[Any]		   =     rsent.split(" "      )
														__lowerCamelCase					:		List[str]		   =     []
														__lowerCamelCase					:		Optional[int]		   =     []
														__lowerCamelCase					:		Dict		   =     []
														ragramslist.append(_lowerCamelCase      )
														for i in range(0						,   len(_lowerCamelCase      ) - 1      ):
																					if i < len(_lowerCamelCase      ) - 1:
																												__lowerCamelCase					:		int		   =     ragrams[i] + ''' ''' + ragrams[i + 1]
																												ragrams.append(_lowerCamelCase      )
																					if i < len(_lowerCamelCase      ) - 2:
																												__lowerCamelCase					:		str		   =     ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2]
																												ragrams.append(_lowerCamelCase      )
																					if i < len(_lowerCamelCase      ) - 3:
																												__lowerCamelCase					:		List[Any]		   =     ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3]
																												ragrams.append(_lowerCamelCase      )
														ragramslist.append(_lowerCamelCase      )
														ragramslist.append(_lowerCamelCase      )
														ragramslist.append(_lowerCamelCase      )

							for i in range(0						,   len(_lowerCamelCase      ) - 1      ):
														if i < len(_lowerCamelCase      ) - 1:
																					__lowerCamelCase					:		List[Any]		   =     sagrams[i] + ''' ''' + sagrams[i + 1]
																					sagrams.append(_lowerCamelCase      )
														if i < len(_lowerCamelCase      ) - 2:
																					__lowerCamelCase					:		Optional[int]		   =     sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
																					sagrams.append(_lowerCamelCase      )
														if i < len(_lowerCamelCase      ) - 3:
																					__lowerCamelCase					:		Any		   =     sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
																					sagrams.append(_lowerCamelCase      )

							for i in range(0						,   len(_lowerCamelCase      ) - 1      ):
														if i < len(_lowerCamelCase      ) - 1:
																					__lowerCamelCase					:		Dict		   =     cagrams[i] + ''' ''' + cagrams[i + 1]
																					cagrams.append(_lowerCamelCase      )
														if i < len(_lowerCamelCase      ) - 2:
																					__lowerCamelCase					:		str		   =     cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
																					cagrams.append(_lowerCamelCase      )
														if i < len(_lowerCamelCase      ) - 3:
																					__lowerCamelCase					:		str		   =     cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
																					cagrams.append(_lowerCamelCase      )

							(__lowerCamelCase)					:		Union[str, Any]		   =     SARIngram(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
							(__lowerCamelCase)					:		Optional[Any]		   =     SARIngram(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
							(__lowerCamelCase)					:		Dict		   =     SARIngram(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
							(__lowerCamelCase)					:		Tuple		   =     SARIngram(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      )
							__lowerCamelCase					:		Union[str, Any]		   =     sum([keepascore, keepascore, keepascore, keepascore]      ) / 4
							__lowerCamelCase					:		int		   =     sum([delascore, delascore, delascore, delascore]      ) / 4
							__lowerCamelCase					:		Optional[int]		   =     sum([addascore, addascore, addascore, addascore]      ) / 4
							__lowerCamelCase					:		List[Any]		   =     (avgkeepscore + avgdelscore + avgaddscore) / 3
							return finalscore


def    lowercase_   (   _lowerCamelCase:	Dict						,   _lowerCamelCase:	bool = True						,   _lowerCamelCase:	str = "13a"						,   _lowerCamelCase:	bool = True      ) ->      Optional[int]:



							'''simple docstring'''

							if lowercase:
														__lowerCamelCase					:		Optional[Any]		   =     sentence.lower()

							if tokenizer in ["13a", "intl"]:
														if version.parse(sacrebleu.__version__      ).major >= 2:
																					__lowerCamelCase					:		List[str]		   =     sacrebleu.metrics.bleu._get_tokenizer(_lowerCamelCase      )()(_lowerCamelCase      )
														else:
																					__lowerCamelCase					:		Optional[Any]		   =     sacrebleu.TOKENIZERS[tokenizer]()(_lowerCamelCase      )
							elif tokenizer == "moses":
														__lowerCamelCase					:		Tuple		   =     sacremoses.MosesTokenizer().tokenize(_lowerCamelCase						,   return_str=_lowerCamelCase						,   escape=_lowerCamelCase      )
							elif tokenizer == "penn":
														__lowerCamelCase					:		Dict		   =     sacremoses.MosesTokenizer().penn_tokenize(_lowerCamelCase						,   return_str=_lowerCamelCase      )
							else:
														__lowerCamelCase					:		List[Any]		   =     sentence

							if not return_str:
														__lowerCamelCase					:		str		   =     normalized_sent.split()

							return normalized_sent


def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	str						,   _lowerCamelCase:	List[Any]      ) ->      Any:



							'''simple docstring'''

							if not (len(_lowerCamelCase      ) == len(_lowerCamelCase      ) == len(_lowerCamelCase      )):
														raise ValueError("Sources length must match predictions and references lengths."      )
							__lowerCamelCase					:		List[Any]		   =     0
							for src, pred, refs in zip(_lowerCamelCase						,   _lowerCamelCase						,   _lowerCamelCase      ):
														sari_score += SARIsent(normalize(_lowerCamelCase      )						,   normalize(_lowerCamelCase      )						,   [normalize(_lowerCamelCase      ) for sent in refs]      )
							__lowerCamelCase					:		List[Any]		   =     sari_score / len(_lowerCamelCase      )
							return 100 * sari_score


def    lowercase_   (   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Tuple="exp"						,   _lowerCamelCase:	int=None						,   _lowerCamelCase:	Dict=False						,   _lowerCamelCase:	str=False						,   _lowerCamelCase:	int=False						,   ) ->      List[Any]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     len(references[0]      )
							if any(len(_lowerCamelCase      ) != references_per_prediction for refs in references      ):
														raise ValueError("Sacrebleu requires the same number of references for each prediction"      )
							__lowerCamelCase					:		str		   =     [[refs[i] for refs in references] for i in range(_lowerCamelCase      )]
							__lowerCamelCase					:		Any		   =     sacrebleu.corpus_bleu(
							    _lowerCamelCase						,   _lowerCamelCase						,   smooth_method=_lowerCamelCase						,   smooth_value=_lowerCamelCase						,   force=_lowerCamelCase						,   lowercase=_lowerCamelCase						,   use_effective_order=_lowerCamelCase						,   )
							return output.score







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				, _KWARGS_DESCRIPTION		)
class  _snake_case     (					datasets.Metric		):






						def       lowerCamelCase__     (       self      :			List[str]    ):
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,					citation=_CITATION							,					inputs_description=_KWARGS_DESCRIPTION							,					features=datasets.Features(
													        {
													            "predictions": datasets.Value("string"							,					id="sequence"    ),
													            "references": datasets.Sequence(datasets.Value("string"							,					id="sequence"    )							,					id="references"    ),
													        }    )							,					codebase_urls=[
													        "https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
													        "https://github.com/cocoxu/simplification/blob/master/SARI.py",
													        "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
													        "https://github.com/mjpost/sacreBLEU",
													    ]							,					reference_urls=[
													        "https://www.aclweb.org/anthology/Q16-1029.pdf",
													        "https://github.com/mjpost/sacreBLEU",
													        "https://en.wikipedia.org/wiki/BLEU",
													        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
													    ]							,					)







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[Any]    ):
													__lowerCamelCase					:		List[Any]		   =     {}
													result.update({"sari": compute_sari(sources=lowerCamelCase__							,					predictions=lowerCamelCase__							,					references=lowerCamelCase__    )}    )
													result.update({"sacrebleu": compute_sacrebleu(predictions=lowerCamelCase__							,					references=lowerCamelCase__    )}    )
													result.update({"exact": compute_em(predictions=lowerCamelCase__							,					references=lowerCamelCase__    )}    )
													return result
"""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  _snake_case     (					a__				, a__		):






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 128							,					UpperCAmelCase      :			int = 256							,					UpperCAmelCase      :			float = 2_0_0_0.0							,					UpperCAmelCase      :			int = 768							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			int = 2048							,					UpperCAmelCase      :			float = 0.1							,					):
													super().__init__()

													__lowerCamelCase					:		List[Any]		   =     nn.Sequential(
													    nn.Linear(UpperCAmelCase							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					nn.Linear(d_model * 4							,					d_model * 4							,					bias=UpperCAmelCase    )							,					nn.SiLU()							,					)

													__lowerCamelCase					:		str		   =     nn.Embedding(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     False

													__lowerCamelCase					:		Optional[int]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(p=UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     nn.ModuleList()
													for lyr_num in range(UpperCAmelCase    ):
																				# FiLM conditional T5 decoder
																				__lowerCamelCase					:		List[str]		   =     DecoderLayer(d_model=UpperCAmelCase							,					d_kv=UpperCAmelCase							,					num_heads=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )
																				self.decoders.append(UpperCAmelCase    )

													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     nn.Dropout(p=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		List[Any]		   =     torch.mul(query_input.unsqueeze(-1    )							,					key_input.unsqueeze(-2    )    )
													return mask.unsqueeze(-3    )







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     decoder_input_tokens.shape
													assert decoder_noise_time.shape == (batch,)

													# decoder_noise_time is in [0, 1), so rescale to expected timing range.
													__lowerCamelCase					:		str		   =     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					:		Optional[int]		   =     self.conditioning_emb(UpperCAmelCase    ).unsqueeze(1    )

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

													__lowerCamelCase					:		Tuple		   =     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					:		int		   =     torch.broadcast_to(
													    torch.arange(UpperCAmelCase							,					device=decoder_input_tokens.device    )							,					(batch, seq_length)							,					)

													__lowerCamelCase					:		Optional[Any]		   =     self.position_encoding(UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.continuous_inputs_projection(UpperCAmelCase    )
													inputs += position_encodings
													__lowerCamelCase					:		List[Any]		   =     self.dropout(UpperCAmelCase    )

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

													# Translate encoding masks to encoder-decoder masks.
													__lowerCamelCase					:		Optional[Any]		   =     [(x, self.encoder_decoder_mask(UpperCAmelCase							,					UpperCAmelCase    )) for x, y in encodings_and_masks]

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

													for lyr in self.decoders:
																				__lowerCamelCase					:		List[Any]		   =     lyr(
																				    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					)[0]

													__lowerCamelCase					:		Dict		   =     self.decoder_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.post_dropout(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     self.spec_out(UpperCAmelCase    )
													return spec_out

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     nn.ModuleList()

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

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

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







						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple=None							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			Dict=None							,					):
													__lowerCamelCase					:		Union[str, Any]		   =     self.layer[0](
													    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

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

																				__lowerCamelCase					:		Dict		   =     self.layer[1](
																				    UpperCAmelCase							,					key_value_states=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													# Apply Film Conditional Feed Forward layer
													__lowerCamelCase					:		List[Any]		   =     self.layer[-1](UpperCAmelCase							,					UpperCAmelCase    )

													return (hidden_states,)

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Dict    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     TaFiLMLayer(in_features=d_model * 4							,					out_features=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     Attention(query_dim=UpperCAmelCase							,					heads=UpperCAmelCase							,					dim_head=UpperCAmelCase							,					out_bias=UpperCAmelCase							,					scale_qk=UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			str=None							,					):
													# pre_self_attention_layer_norm
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )

													if conditioning_emb is not None:
																				__lowerCamelCase					:		Optional[Any]		   =     self.FiLMLayer(UpperCAmelCase							,					UpperCAmelCase    )

													# Self-attention block
													__lowerCamelCase					:		Optional[Any]		   =     self.attention(UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     hidden_states + self.dropout(UpperCAmelCase    )

													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     Attention(query_dim=UpperCAmelCase							,					heads=UpperCAmelCase							,					dim_head=UpperCAmelCase							,					out_bias=UpperCAmelCase							,					scale_qk=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			Optional[int]=None							,					):
													__lowerCamelCase					:		str		   =     self.layer_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     self.attention(
													    UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					attention_mask=attention_mask.squeeze(1    )							,					)
													__lowerCamelCase					:		List[str]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return layer_output

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     TaDenseGatedActDense(d_model=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     TaFiLMLayer(in_features=d_model * 4							,					out_features=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any=None    ):
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )
													if conditioning_emb is not None:
																				__lowerCamelCase					:		Union[str, Any]		   =     self.film(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.DenseReluDense(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     NewGELUActivation()







						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.act(self.wi_a(UpperCAmelCase    )    )
													__lowerCamelCase					:		Any		   =     self.wi_a(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     hidden_gelu * hidden_linear
													__lowerCamelCase					:		Any		   =     self.dropout(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     self.wo(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Parameter(torch.ones(UpperCAmelCase    )    )
													__lowerCamelCase					:		Tuple		   =     eps







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			Any    ):
													# 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					:		int		   =     hidden_states.to(torch.floataa    ).pow(2    ).mean(-1							,					keepdim=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

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

													return self.weight * hidden_states

class  _snake_case     (					nn.Module		):






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			torch.Tensor    ):
													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.0_4_4_7_1_5 * torch.pow(UpperCAmelCase							,					3.0    ))    ))

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]    ):
													super().__init__()
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					out_features * 2							,					bias=UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.scale_bias(UpperCAmelCase    )
													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     torch.chunk(UpperCAmelCase							,					2							,					-1    )
													__lowerCamelCase					:		List[Any]		   =     x * (1 + scale) + shift
													return x