





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


UpperCAmelCase_ 							=       logging.get_logger(__name__)


UpperCAmelCase_ 							=       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase_ 							=       {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

UpperCAmelCase_ 							=       {
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}





@lru_cache()
def   lowerCamelCase__       (       )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			(
					    list(range(ord('!'  )    ,      ord('~'  ) + 1  )  ) + list(range(ord('¡'  )    ,      ord('¬'  ) + 1  )  ) + list(range(ord('®'  )    ,      ord('ÿ'  ) + 1  )  )
					)
					_snake_case													=			bs[:]
					_snake_case													=			0
					for b in range(2**8  ):
										if b not in bs:
															bs.append(UpperCamelCase__  )
															cs.append(2**8 + n  )
															n += 1
					_snake_case													=			[chr(UpperCamelCase__  ) for n in cs]
					return dict(zip(UpperCamelCase__    ,      UpperCamelCase__  )  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple  )     ->	int:



					'''simple docstring'''




					_snake_case													=			set()
					_snake_case													=			word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)  )
										_snake_case													=			char
					return pairs




class      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		VOCAB_FILES_NAMES
					lowerCAmelCase_							=		PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_							=		['''input_ids''', '''attention_mask''']




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_="replace"			,  lowerCAmelCase_="<s>"			,  lowerCAmelCase_="</s>"			,  lowerCAmelCase_="</s>"			,  lowerCAmelCase_="<s>"			,  lowerCAmelCase_="<unk>"			,  lowerCAmelCase_="<pad>"			,  lowerCAmelCase_="<mask>"			,  lowerCAmelCase_=False			,  **lowerCAmelCase_			,  )						->							Tuple:
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else bos_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else eos_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else sep_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else cls_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else unk_token
										_snake_case													=			AddedToken(lowerCAmelCase_			,  lstrip=lowerCAmelCase_			,  rstrip=lowerCAmelCase_					) if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					) else pad_token

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

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

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

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




					@property
					def 		lowerCAmelCase     (						self					)						->							Any:
										return len(self.encoder					)




					def 		lowerCAmelCase     (						self					)						->							Dict:
										return dict(self.encoder			,  **self.added_tokens_encoder					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Dict:
										if token in self.cache:
															return self.cache[token]
										_snake_case													=			tuple(lowerCAmelCase_					)
										_snake_case													=			get_pairs(lowerCAmelCase_					)

										if not pairs:
															return token

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

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							List[Any]:
										_snake_case													=			[]
										for token in re.findall(self.pat			,  lowerCAmelCase_					):
															_snake_case													=			''.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(lowerCAmelCase_					).split(' '					)					)
										return bpe_tokens




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							str:
										return self.encoder.get(lowerCAmelCase_			,  self.encoder.get(self.unk_token					)					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[int]:
										return self.decoder.get(lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										_snake_case													=			''.join(lowerCAmelCase_					)
										_snake_case													=			bytearray([self.byte_decoder[c] for c in text]					).decode('utf-8'			,  errors=self.errors					)
										return text




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							Tuple[str]:
										if not os.path.isdir(lowerCAmelCase_					):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
										_snake_case													=			os.path.join(
										    lowerCAmelCase_			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
										_snake_case													=			os.path.join(
										    lowerCAmelCase_			,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']					)

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

										_snake_case													=			0
										with open(lowerCAmelCase_			,  '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 lowerCAmelCase_					: 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!'					)
																									_snake_case													=			token_index
																				writer.write(' '.join(lowerCAmelCase_					) + '\n'					)
																				index += 1

										return vocab_file, merge_file




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[int]:
										if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_snake_case													=			[self.cls_token_id]
										_snake_case													=			[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None			,  lowerCAmelCase_ = False					)						->							List[int]:
										if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase_			,  token_ids_a=lowerCAmelCase_			,  already_has_special_tokens=lowerCAmelCase_					)

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_ = None					)						->							List[int]:
										_snake_case													=			[self.sep_token_id]
										_snake_case													=			[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 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=False			,  **lowerCAmelCase_					)						->							str:
										_snake_case													=			kwargs.pop('add_prefix_space'			,  self.add_prefix_space					)
										if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase_					) > 0 and not text[0].isspace()):
															_snake_case													=			' ' + 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      UpperCamelCase_						(					_lowerCamelCase							,					_lowerCamelCase   ):




					@register_to_config
					def __init__(						self			,  lowerCAmelCase_ = 128			,  lowerCAmelCase_ = 256			,  lowerCAmelCase_ = 20_00.0			,  lowerCAmelCase_ = 768			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 12			,  lowerCAmelCase_ = 64			,  lowerCAmelCase_ = 2048			,  lowerCAmelCase_ = 0.1			,  )						->							Union[str, Any]:
										super().__init__()

										_snake_case													=			nn.Sequential(
										    nn.Linear(lowerCAmelCase_			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  nn.Linear(d_model * 4			,  d_model * 4			,  bias=lowerCAmelCase_					)			,  nn.SiLU()			,  )

										_snake_case													=			nn.Embedding(lowerCAmelCase_			,  lowerCAmelCase_					)
										_snake_case													=			False

										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)

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

										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)

										_snake_case													=			nn.Dropout(p=lowerCAmelCase_					)
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  lowerCAmelCase_			,  bias=lowerCAmelCase_					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			torch.mul(query_input.unsqueeze(-1					)			,  key_input.unsqueeze(-2					)					)
										return mask.unsqueeze(-3					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Optional[Any]:
										_snake_case							,       _snake_case							,       _snake_case													=			decoder_input_tokens.shape
										assert decoder_noise_time.shape == (batch,)

										# decoder_noise_time is in [0, 1), so rescale to expected timing range.
										_snake_case													=			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					)

										_snake_case													=			self.conditioning_emb(lowerCAmelCase_					).unsqueeze(1					)

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

										_snake_case													=			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.
										_snake_case													=			torch.broadcast_to(
										    torch.arange(lowerCAmelCase_			,  device=decoder_input_tokens.device					)			,  (batch, seq_length)			,  )

										_snake_case													=			self.position_encoding(lowerCAmelCase_					)

										_snake_case													=			self.continuous_inputs_projection(lowerCAmelCase_					)
										inputs += position_encodings
										_snake_case													=			self.dropout(lowerCAmelCase_					)

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

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

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

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

										_snake_case													=			self.decoder_norm(lowerCAmelCase_					)
										_snake_case													=			self.post_dropout(lowerCAmelCase_					)

										_snake_case													=			self.spec_out(lowerCAmelCase_					)
										return spec_out





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							Tuple:
										super().__init__()
										_snake_case													=			nn.ModuleList()

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

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

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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Tuple:
										_snake_case													=			self.layer[0](
										    lowerCAmelCase_			,  conditioning_emb=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

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

															_snake_case													=			self.layer[1](
															    lowerCAmelCase_			,  key_value_states=lowerCAmelCase_			,  attention_mask=lowerCAmelCase_			,  )

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

										return (hidden_states,)





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Tuple:
										super().__init__()
										_snake_case													=			TaLayerNorm(lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							str:
										# pre_self_attention_layer_norm
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)

										if conditioning_emb is not None:
															_snake_case													=			self.FiLMLayer(lowerCAmelCase_			,  lowerCAmelCase_					)

										# Self-attention block
										_snake_case													=			self.attention(lowerCAmelCase_					)

										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)

										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							str:
										super().__init__()
										_snake_case													=			Attention(query_dim=lowerCAmelCase_			,  heads=lowerCAmelCase_			,  dim_head=lowerCAmelCase_			,  out_bias=lowerCAmelCase_			,  scale_qk=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							Dict:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										_snake_case													=			self.attention(
										    lowerCAmelCase_			,  encoder_hidden_states=lowerCAmelCase_			,  attention_mask=attention_mask.squeeze(1					)			,  )
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return layer_output





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										super().__init__()
										_snake_case													=			TaDenseGatedActDense(d_model=lowerCAmelCase_			,  d_ff=lowerCAmelCase_			,  dropout_rate=lowerCAmelCase_					)
										_snake_case													=			TaFiLMLayer(in_features=d_model * 4			,  out_features=lowerCAmelCase_					)
										_snake_case													=			TaLayerNorm(lowerCAmelCase_			,  eps=lowerCAmelCase_					)
										_snake_case													=			nn.Dropout(lowerCAmelCase_					)






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Union[str, Any]:
										_snake_case													=			self.layer_norm(lowerCAmelCase_					)
										if conditioning_emb is not None:
															_snake_case													=			self.film(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			self.DenseReluDense(lowerCAmelCase_					)
										_snake_case													=			hidden_states + self.dropout(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.act(self.wi_a(lowerCAmelCase_					)					)
										_snake_case													=			self.wi_a(lowerCAmelCase_					)
										_snake_case													=			hidden_gelu * hidden_linear
										_snake_case													=			self.dropout(lowerCAmelCase_					)

										_snake_case													=			self.wo(lowerCAmelCase_					)
										return hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_=1E-6					)						->							str:
										super().__init__()
										_snake_case													=			nn.Parameter(torch.ones(lowerCAmelCase_					)					)
										_snake_case													=			eps






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										# 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

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

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

										return self.weight * hidden_states





class      UpperCamelCase_						(					nn.Module   ):




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							torch.Tensor:
										return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.04_47_15 * torch.pow(lowerCAmelCase_			,  3.0					))					))





class      UpperCamelCase_						(					nn.Module   ):




					def __init__(						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										super().__init__()
										_snake_case													=			nn.Linear(lowerCAmelCase_			,  out_features * 2			,  bias=lowerCAmelCase_					)






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



