
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase        =  logging.get_logger(__name__)


lowercase        =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

lowercase        =  {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

lowercase        =  {
    '''allenai/longformer-base-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_0_9_6,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_0_9_6,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_0_9_6,
}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      __lowerCAmelCase						(							)  ->    Optional[Any]:
							lowerCamelCase_							  =					(
							    list(range(ord("""!"""       )			,				ord("""~"""       ) + 1       )       ) + list(range(ord("""¡"""       )			,				ord("""¬"""       ) + 1       )       ) + list(range(ord("""®"""       )			,				ord("""ÿ"""       ) + 1       )       )
							)
							lowerCamelCase_							  =					bs[:]
							lowerCamelCase_							  =					0
							for b in range(2**8       ):
														if b not in bs:
																					bs.append(UpperCAmelCase__       )
																					cs.append(2**8 + n       )
																					n += 1
							lowerCamelCase_							  =					[chr(UpperCAmelCase__       ) for n in cs]
							return dict(zip(UpperCAmelCase__			,				UpperCAmelCase__       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    Union[str, Any]:
							lowerCamelCase_							  =					set()
							lowerCamelCase_							  =					word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)       )
														lowerCamelCase_							  =					char
							return pairs





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    ['''input_ids''', '''attention_mask''']

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[Any]="replace"   ,					__UpperCamelCase					:     str="<s>"   ,					__UpperCamelCase					:     str="</s>"   ,					__UpperCamelCase					:     Optional[int]="</s>"   ,					__UpperCamelCase					:     str="<s>"   ,					__UpperCamelCase					:     List[str]="<unk>"   ,					__UpperCamelCase					:     List[Any]="<pad>"   ,					__UpperCamelCase					:     str="<mask>"   ,					__UpperCamelCase					:     List[str]=False   ,					**__UpperCamelCase					:     List[Any]   ,					):
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else bos_token
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else eos_token
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else sep_token
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else cls_token
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else unk_token
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else pad_token

												# Mask token behave like a normal word, i.e. include the space before it
												lowerCamelCase_							  =					AddedToken(__UpperCamelCase   ,					lstrip=__UpperCamelCase   ,					rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ) else mask_token

												super().__init__(
												    errors=__UpperCamelCase   ,					bos_token=__UpperCamelCase   ,					eos_token=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					sep_token=__UpperCamelCase   ,					cls_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					mask_token=__UpperCamelCase   ,					add_prefix_space=__UpperCamelCase   ,					**__UpperCamelCase   ,					)

												with open(__UpperCamelCase   ,					encoding="""utf-8"""   ) as vocab_handle:
																			lowerCamelCase_							  =					json.load(__UpperCamelCase   )
												lowerCamelCase_							  =					{v: k for k, v in self.encoder.items()}
												lowerCamelCase_							  =					errors  # how to handle errors in decoding
												lowerCamelCase_							  =					bytes_to_unicode()
												lowerCamelCase_							  =					{v: k for k, v in self.byte_encoder.items()}
												with open(__UpperCamelCase   ,					encoding="""utf-8"""   ) as merges_handle:
																			lowerCamelCase_							  =					merges_handle.read().split("""\n"""   )[1:-1]
												lowerCamelCase_							  =					[tuple(merge.split()   ) for merge in bpe_merges]
												lowerCamelCase_							  =					dict(zip(__UpperCamelCase   ,					range(len(__UpperCamelCase   )   )   )   )
												lowerCamelCase_							  =					{}
												lowerCamelCase_							  =					add_prefix_space

												# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
												lowerCamelCase_							  =					re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""   )

					@property
					def    lowercase__      (	self					:     List[Any]   ):
												return len(self.encoder   )

					def    lowercase__      (	self					:     Dict   ):
												return dict(self.encoder   ,					**self.added_tokens_encoder   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ):
												if token in self.cache:
																			return self.cache[token]
												lowerCamelCase_							  =					tuple(__UpperCamelCase   )
												lowerCamelCase_							  =					get_pairs(__UpperCamelCase   )

												if not pairs:
																			return token

												while True:
																			lowerCamelCase_							  =					min(__UpperCamelCase   ,					key=lambda __UpperCamelCase   : self.bpe_ranks.get(__UpperCamelCase   ,					float("""inf"""   )   )   )
																			if bigram not in self.bpe_ranks:
																										break
																			lowerCamelCase_  ,   lowerCamelCase_							  =					bigram
																			lowerCamelCase_							  =					[]
																			lowerCamelCase_							  =					0
																			while i < len(__UpperCamelCase   ):
																										try:
																																	lowerCamelCase_							  =					word.index(__UpperCamelCase   ,					__UpperCamelCase   )
																										except ValueError:
																																	new_word.extend(word[i:]   )
																																	break
																										else:
																																	new_word.extend(word[i:j]   )
																																	lowerCamelCase_							  =					j

																										if word[i] == first and i < len(__UpperCamelCase   ) - 1 and word[i + 1] == second:
																																	new_word.append(first + second   )
																																	i += 2
																										else:
																																	new_word.append(word[i]   )
																																	i += 1
																			lowerCamelCase_							  =					tuple(__UpperCamelCase   )
																			lowerCamelCase_							  =					new_word
																			if len(__UpperCamelCase   ) == 1:
																										break
																			else:
																										lowerCamelCase_							  =					get_pairs(__UpperCamelCase   )
												lowerCamelCase_							  =					""" """.join(__UpperCamelCase   )
												lowerCamelCase_							  =					word
												return word

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[]
												for token in re.findall(self.pat   ,					__UpperCamelCase   ):
																			lowerCamelCase_							  =					"""""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCamelCase   ).split(""" """   )   )
												return bpe_tokens

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												return self.encoder.get(__UpperCamelCase   ,					self.encoder.get(self.unk_token   )   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Dict   ):
												return self.decoder.get(__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Dict   ):
												lowerCamelCase_							  =					"""""".join(__UpperCamelCase   )
												lowerCamelCase_							  =					bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""   ,					errors=self.errors   )
												return text

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												if not os.path.isdir(__UpperCamelCase   ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
																			return
												lowerCamelCase_							  =					os.path.join(
												    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
												lowerCamelCase_							  =					os.path.join(
												    __UpperCamelCase   ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]   )

												with open(__UpperCamelCase   ,					"""w"""   ,					encoding="""utf-8"""   ) as f:
																			f.write(json.dumps(self.encoder   ,					indent=2   ,					sort_keys=__UpperCamelCase   ,					ensure_ascii=__UpperCamelCase   ) + """\n"""   )

												lowerCamelCase_							  =					0
												with open(__UpperCamelCase   ,					"""w"""   ,					encoding="""utf-8"""   ) as writer:
																			writer.write("""#version: 0.2\n"""   )
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,					key=lambda __UpperCamelCase   : kv[1]   ):
																										if index != token_index:
																																	logger.warning(
																																	    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																																	    """ Please check that the tokenizer is not corrupted!"""   )
																																	lowerCamelCase_							  =					token_index
																										writer.write(""" """.join(__UpperCamelCase   ) + """\n"""   )
																										index += 1

												return vocab_file, merge_file

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ):
												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCamelCase_							  =					[self.cls_token_id]
												lowerCamelCase_							  =					[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep

					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ,					__UpperCamelCase					:     bool = False   ):
												if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__UpperCamelCase   ,					token_ids_a=__UpperCamelCase   ,					already_has_special_tokens=__UpperCamelCase   )

												if token_ids_a is None:
																			return [1] + ([0] * len(__UpperCamelCase   )) + [1]
												return [1] + ([0] * len(__UpperCamelCase   )) + [1, 1] + ([0] * len(__UpperCamelCase   )) + [1]

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ):
												lowerCamelCase_							  =					[self.sep_token_id]
												lowerCamelCase_							  =					[self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep   ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     str=False   ,					**__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					kwargs.pop("""add_prefix_space"""   ,					self.add_prefix_space   )
												if (is_split_into_words or add_prefix_space) and (len(__UpperCamelCase   ) > 0 and not text[0].isspace()):
																			lowerCamelCase_							  =					""" """ + text
												return (text, kwargs)







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       __A(				UpperCAmelCase     ,		UpperCAmelCase       ):

					@register_to_config
					def __init__(	self					:     int   ,					__UpperCamelCase					:     int = 1_2_8   ,					__UpperCamelCase					:     int = 2_5_6   ,					__UpperCamelCase					:     float = 2000.0   ,					__UpperCamelCase					:     int = 7_6_8   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 1_2   ,					__UpperCamelCase					:     int = 6_4   ,					__UpperCamelCase					:     int = 2_0_4_8   ,					__UpperCamelCase					:     float = 0.1   ,					):
												super().__init__()

												lowerCamelCase_							  =					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_							  =					nn.Embedding(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					False

												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )

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

												lowerCamelCase_							  =					TaLayerNorm(__UpperCamelCase   )

												lowerCamelCase_							  =					nn.Dropout(p=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )

					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ):
												lowerCamelCase_							  =					torch.mul(query_input.unsqueeze(-1   )   ,					key_input.unsqueeze(-2   )   )
												return mask.unsqueeze(-3   )



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[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(__UpperCamelCase   ).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(__UpperCamelCase   ,					device=decoder_input_tokens.device   )   ,					(batch, seq_length)   ,					)

												lowerCamelCase_							  =					self.position_encoding(__UpperCamelCase   )

												lowerCamelCase_							  =					self.continuous_inputs_projection(__UpperCamelCase   )
												inputs += position_encodings
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												# 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(__UpperCamelCase   ,					__UpperCamelCase   )) 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(
																			    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					encoder_attention_mask=__UpperCamelCase   ,					)[0]

												lowerCamelCase_							  =					self.decoder_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.post_dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.spec_out(__UpperCamelCase   )
												return spec_out





class       __A(				nn.Module       ):

					def __init__(	self					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Dict=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					__UpperCamelCase					:     Tuple=None   ,					):
												lowerCamelCase_							  =					self.layer[0](
												    __UpperCamelCase   ,					conditioning_emb=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

												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](
																			    __UpperCamelCase   ,					key_value_states=__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					)

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

												return (hidden_states,)





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												# pre_self_attention_layer_norm
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )

												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.FiLMLayer(__UpperCamelCase   ,					__UpperCamelCase   )

												# Self-attention block
												lowerCamelCase_							  =					self.attention(__UpperCamelCase   )

												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )

												return hidden_states





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=None   ,					__UpperCamelCase					:     int=None   ,					):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												lowerCamelCase_							  =					self.attention(
												    __UpperCamelCase   ,					encoder_hidden_states=__UpperCamelCase   ,					attention_mask=attention_mask.squeeze(1   )   ,					)
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return layer_output





class       __A(				nn.Module       ):

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



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					self.layer_norm(__UpperCamelCase   )
												if conditioning_emb is not None:
																			lowerCamelCase_							  =					self.film(__UpperCamelCase   ,					__UpperCamelCase   )

												lowerCamelCase_							  =					self.DenseReluDense(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_states + self.dropout(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					__UpperCamelCase   ,					bias=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Dropout(__UpperCamelCase   )
												lowerCamelCase_							  =					NewGELUActivation()



					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.act(self.wi_a(__UpperCamelCase   )   )
												lowerCamelCase_							  =					self.wi_a(__UpperCamelCase   )
												lowerCamelCase_							  =					hidden_gelu * hidden_linear
												lowerCamelCase_							  =					self.dropout(__UpperCamelCase   )

												lowerCamelCase_							  =					self.wo(__UpperCamelCase   )
												return hidden_states





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=1E-6   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Parameter(torch.ones(__UpperCamelCase   )   )
												lowerCamelCase_							  =					eps



					def    lowercase__      (	self					:     Dict   ,					__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_							  =					hidden_states.to(torch.floataa   ).pow(2   ).mean(-1   ,					keepdim=__UpperCamelCase   )
												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       __A(				nn.Module       ):

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     torch.Tensor   ):
												return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__UpperCamelCase   ,					3.0   ))   ))





class       __A(				nn.Module       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ):
												super().__init__()
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					out_features * 2   ,					bias=__UpperCamelCase   )



					def    lowercase__      (	self					:     int   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str   ):
												lowerCamelCase_							  =					self.scale_bias(__UpperCamelCase   )
												lowerCamelCase_  ,   lowerCamelCase_							  =					torch.chunk(__UpperCamelCase   ,					2   ,					-1   )
												lowerCamelCase_							  =					x * (1 + scale) + shift
												return x





