



import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				List[str] ,		__a     :				Any ,		__a     :				Optional[int] ,		__a     :				Any=True ,		__a     :				Tuple="pt"		):

				"""simple docstring"""
				a__				      =		{"""add_prefix_space""": True} if isinstance(__a ,		__a		) and not line.startswith(""" """		) else {}
				a__				      =		padding_side
				return tokenizer(
				    [line] ,		max_length=__a ,		padding="""max_length""" if pad_to_max_length else None ,		truncation=__a ,		return_tensors=__a ,		add_special_tokens=__a ,		**__a ,		)

def 			A_      (	__a     :				Any ,		__a     :				Dict ,		__a     :				List[Any]=None ,		):

				"""simple docstring"""
				a__				      =		input_ids.ne(__a		).any(dim=0		)
				if attention_mask is None:
								return input_ids[:, keep_column_mask]
				else:
								return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_="train"  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=""  ,  ):
											super().__init__()
											a__				      =		Path(a_		).joinpath(type_path + """.source"""		)
											a__				      =		Path(a_		).joinpath(type_path + """.target"""		)
											a__				      =		self.get_char_lens(self.src_file		)
											a__				      =		max_source_length
											a__				      =		max_target_length
											assert min(self.src_lens		) > 0, F'''found empty line in {self.src_file}'''
											a__				      =		tokenizer
											a__				      =		prefix
											if n_obs is not None:
															a__				      =		self.src_lens[:n_obs]
											a__				      =		src_lang
											a__				      =		tgt_lang


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


							def __getitem__(							self  ,  a_		):
											a__				      =		index + 1  # linecache starts at 1
											a__				      =		self.prefix + linecache.getline(str(self.src_file		)  ,  a_		).rstrip("""\n"""		)
											a__				      =		linecache.getline(str(self.tgt_file		)  ,  a_		).rstrip("""\n"""		)
											assert source_line, F'''empty source line for index {index}'''
											assert tgt_line, F'''empty tgt line for index {index}'''

											# Need to add eos token manually for T5
											if isinstance(self.tokenizer  ,  a_		):
															source_line += self.tokenizer.eos_token
															tgt_line += self.tokenizer.eos_token

											# Pad source and target to the right
											a__				      =		(
											    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,  a_		) else self.tokenizer
											)
											a__				      =		self.tokenizer.generator if isinstance(self.tokenizer  ,  a_		) else self.tokenizer

											a__				      =		encode_line(a_  ,  a_  ,  self.max_source_length  ,  """right"""		)
											a__				      =		encode_line(a_  ,  a_  ,  self.max_target_length  ,  """right"""		)

											a__				      =		source_inputs["""input_ids"""].squeeze()
											a__				      =		target_inputs["""input_ids"""].squeeze()
											a__				      =		source_inputs["""attention_mask"""].squeeze()
											return {
											    "input_ids": source_ids,
											    "attention_mask": src_mask,
											    "decoder_input_ids": target_ids,
											}


							@staticmethod
							def _a      (							a_		):
											return [len(a_		) for x in Path(a_		).open().readlines()]







							def _a      (							self  ,  a_		):
											a__				      =		torch.stack([x["""input_ids"""] for x in batch]		)
											a__				      =		torch.stack([x["""attention_mask"""] for x in batch]		)
											a__				      =		torch.stack([x["""decoder_input_ids"""] for x in batch]		)
											a__				      =		(
											    self.tokenizer.generator.pad_token_id
											    if isinstance(self.tokenizer  ,  a_		)
											    else self.tokenizer.pad_token_id
											)
											a__				      =		(
											    self.tokenizer.question_encoder.pad_token_id
											    if isinstance(self.tokenizer  ,  a_		)
											    else self.tokenizer.pad_token_id
											)
											a__				      =		trim_batch(a_  ,  a_		)
											a__      ,			a__				      =		trim_batch(a_  ,  a_  ,  attention_mask=a_		)
											a__				      =		{
											    """input_ids""": source_ids,
											    """attention_mask""": source_mask,
											    """decoder_input_ids""": y,
											}
											return batch




UpperCAmelCase							 =					getLogger(__name__)

def 			A_      (	__a     :				List[List]		):

				"""simple docstring"""
				return list(itertools.chain.from_iterable(__a		)		)

def 			A_      (	__a     :				str		):

				"""simple docstring"""
				a__				      =		get_git_info()
				save_json(__a ,		os.path.join(__a ,		"""git_log.json"""		)		)

def 			A_      (	__a     :				Optional[int] ,		__a     :				Union[str, Any] ,		__a     :				str=4 ,		**__a     :				Optional[Any]		):

				"""simple docstring"""
				with open(__a ,		"""w"""		) as f:
								json.dump(__a ,		__a ,		indent=__a ,		**__a		)

def 			A_      (	__a     :				Any		):

				"""simple docstring"""
				with open(__a		) as f:
								return json.load(__a		)

def 			A_      (	):

				"""simple docstring"""
				a__				      =		git.Repo(search_parent_directories=__a		)
				a__				      =		{
				    """repo_id""": str(__a		),
				    """repo_sha""": str(repo.head.object.hexsha		),
				    """repo_branch""": str(repo.active_branch		),
				    """hostname""": str(socket.gethostname()		),
				}
				return repo_infos

def 			A_      (	__a     :				Callable ,		__a     :				Iterable		):

				"""simple docstring"""
				return list(map(__a ,		__a		)		)

def 			A_      (	__a     :				Optional[int] ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				with open(__a ,		"""wb"""		) as f:
								return pickle.dump(__a ,		__a		)

def 			A_      (	__a     :				Optional[Any]		):

				"""simple docstring"""

				def remove_articles(__a     :				List[Any]		):
								return re.sub(r"""\b(a|an|the)\b""" ,		""" """ ,		__a		)

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

				def remove_punc(__a     :				Optional[Any]		):
								a__				      =		set(string.punctuation		)
								return "".join(ch for ch in text if ch not in exclude		)

				def lower(__a     :				int		):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(__a		)		)		)		)

def 			A_      (	__a     :				Tuple ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		normalize_answer(__a		).split()
				a__				      =		normalize_answer(__a		).split()
				a__				      =		Counter(__a		) & Counter(__a		)
				a__				      =		sum(common.values()		)
				if num_same == 0:
								return 0
				a__				      =		1.0 * num_same / len(__a		)
				a__				      =		1.0 * num_same / len(__a		)
				a__				      =		(2 * precision * recall) / (precision + recall)
				return fa

def 			A_      (	__a     :				str ,		__a     :				int		):

				"""simple docstring"""
				return normalize_answer(__a		) == normalize_answer(__a		)

def 			A_      (	__a     :				List[str] ,		__a     :				List[str]		):

				"""simple docstring"""
				assert len(__a		) == len(__a		)
				a__				      =		0
				for hypo, pred in zip(__a ,		__a		):
								em += exact_match_score(__a ,		__a		)
				if len(__a		) > 0:
								em /= len(__a		)
				return {"em": em}

def 			A_      (	__a     :				str		):

				"""simple docstring"""
				return model_prefix.startswith("""rag"""		)




def 			A_      (	__a     :				Dict ,		__a     :				int ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		{p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				a__				      =		"""dropout_rate"""
				for p in extra_params:
								if getattr(__a ,		__a ,		__a		):
												if not hasattr(__a ,		__a		) and not hasattr(__a ,		equivalent_param[p]		):
																logger.info("""config doesn't have a `{}` attribute""".format(__a		)		)
																delattr(__a ,		__a		)
																continue
												a__				      =		p if hasattr(__a ,		__a		) else equivalent_param[p]
												setattr(__a ,		__a ,		getattr(__a ,		__a		)		)
												delattr(__a ,		__a		)
				return hparams, config






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      (   SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@register_to_config
							def __init__(							self  ,  a_ = 128  ,  a_ = 256  ,  a_ = 2_000.0  ,  a_ = 768  ,  a_ = 12  ,  a_ = 12  ,  a_ = 64  ,  a_ = 2_048  ,  a_ = 0.1  ,  ):
											super().__init__()

											a__				      =		nn.Sequential(
											    nn.Linear(a_  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  nn.Linear(d_model * 4  ,  d_model * 4  ,  bias=a_		)  ,  nn.SiLU()  ,  )

											a__				      =		nn.Embedding(a_  ,  a_		)
											a__				      =		False

											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)

											a__				      =		nn.Dropout(p=a_		)

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

											a__				      =		TaLayerNorm(a_		)

											a__				      =		nn.Dropout(p=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		torch.mul(query_input.unsqueeze(-1		)  ,  key_input.unsqueeze(-2		)		)
											return mask.unsqueeze(-3		)







							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__      ,			a__      ,			a__				      =		decoder_input_tokens.shape
											assert decoder_noise_time.shape == (batch,)

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

											a__				      =		self.conditioning_emb(a_		).unsqueeze(1		)

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

											a__				      =		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.
											a__				      =		torch.broadcast_to(
											    torch.arange(a_  ,  device=decoder_input_tokens.device		)  ,  (batch, seq_length)  ,  )

											a__				      =		self.position_encoding(a_		)

											a__				      =		self.continuous_inputs_projection(a_		)
											inputs += position_encodings
											a__				      =		self.dropout(a_		)

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

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

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

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

											a__				      =		self.decoder_norm(a_		)
											a__				      =		self.post_dropout(a_		)

											a__				      =		self.spec_out(a_		)
											return spec_out




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.ModuleList()

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

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

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







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer[0](
											    a_  ,  conditioning_emb=a_  ,  attention_mask=a_  ,  )

											if encoder_hidden_states is not None:
															a__				      =		torch.where(encoder_attention_mask > 0  ,  0  ,  -1E1_0		).to(
															    encoder_hidden_states.dtype		)

															a__				      =		self.layer[1](
															    a_  ,  key_value_states=a_  ,  attention_mask=a_  ,  )

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

											return (hidden_states,)




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaLayerNorm(a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											# pre_self_attention_layer_norm
											a__				      =		self.layer_norm(a_		)

											if conditioning_emb is not None:
															a__				      =		self.FiLMLayer(a_  ,  a_		)

											# Self-attention block
											a__				      =		self.attention(a_		)

											a__				      =		hidden_states + self.dropout(a_		)

											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		Attention(query_dim=a_  ,  heads=a_  ,  dim_head=a_  ,  out_bias=a_  ,  scale_qk=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None  ,  a_=None  ,  ):
											a__				      =		self.layer_norm(a_		)
											a__				      =		self.attention(
											    a_  ,  encoder_hidden_states=a_  ,  attention_mask=attention_mask.squeeze(1		)  ,  )
											a__				      =		hidden_states + self.dropout(a_		)
											return layer_output




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		TaDenseGatedActDense(d_model=a_  ,  d_ff=a_  ,  dropout_rate=a_		)
											a__				      =		TaFiLMLayer(in_features=d_model * 4  ,  out_features=a_		)
											a__				      =		TaLayerNorm(a_  ,  eps=a_		)
											a__				      =		nn.Dropout(a_		)







							def _a      (							self  ,  a_  ,  a_=None		):
											a__				      =		self.layer_norm(a_		)
											if conditioning_emb is not None:
															a__				      =		self.film(a_  ,  a_		)

											a__				      =		self.DenseReluDense(a_		)
											a__				      =		hidden_states + self.dropout(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Linear(a_  ,  a_  ,  bias=a_		)
											a__				      =		nn.Dropout(a_		)
											a__				      =		NewGELUActivation()







							def _a      (							self  ,  a_		):
											a__				      =		self.act(self.wi_a(a_		)		)
											a__				      =		self.wi_a(a_		)
											a__				      =		hidden_gelu * hidden_linear
											a__				      =		self.dropout(a_		)

											a__				      =		self.wo(a_		)
											return hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=1E-6		):
											super().__init__()
											a__				      =		nn.Parameter(torch.ones(a_		)		)
											a__				      =		eps







							def _a      (							self  ,  a_		):
											# 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

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

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

											return self.weight * hidden_states




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def _a      (							self  ,  a_		):
											return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.044_715 * torch.pow(a_  ,  3.0		))		))




class  __snake_case      (   nn.Module):







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_		):
											super().__init__()
											a__				      =		nn.Linear(a_  ,  out_features * 2  ,  bias=a_		)







							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		self.scale_bias(a_		)
											a__      ,			a__				      =		torch.chunk(a_  ,  2  ,  -1		)
											a__				      =		x * (1 + scale) + shift
											return x

