
import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_A     =  """bert-base-cased"""
_A     =  """google/pegasus-xsum"""
_A     =  [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_A     =  ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_A     =  """patrickvonplaten/t5-tiny-random"""
_A     =  """sshleifer/bart-tiny-random"""
_A     =  """sshleifer/tiny-mbart"""
_A     =  """sshleifer/tiny-marian-en-de"""






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Tuple       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"\n".join(UpperCamelCase__       )
			Path(UpperCamelCase__       ).open("w"       ).writelines(UpperCamelCase__       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Dict       ):




			"""simple docstring"""


			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(UpperCamelCase__					,							F"""{split}.source"""       )					,							UpperCamelCase__       )
						_dump_articles(os.path.join(UpperCamelCase__					,							F"""{split}.target"""       )					,							UpperCamelCase__       )
			return tmp_dir





class 			_lowerCAmelCase      (			lowercase_ ):




				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]       ,       )
				@slow
				def   __a		(      self       ,       _UpperCamelCase  )       ->   List[Any]:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
							lowerCAmelCase_      			=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
							lowerCAmelCase_      			=			max(len(tokenizer.encode(_UpperCamelCase  )  ) for a in ARTICLES  )
							lowerCAmelCase_      			=			max(len(tokenizer.encode(_UpperCamelCase  )  ) for a in SUMMARIES  )
							lowerCAmelCase_      			=			4
							lowerCAmelCase_      			=			8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							lowerCAmelCase_			,						lowerCAmelCase_      			=			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
							lowerCAmelCase_      			=			SeqaSeqDataset(
							    _UpperCamelCase       ,       data_dir=_UpperCamelCase       ,       type_path="train"       ,       max_source_length=_UpperCamelCase       ,       max_target_length=_UpperCamelCase       ,       src_lang=_UpperCamelCase       ,       tgt_lang=_UpperCamelCase       ,       )
							lowerCAmelCase_      			=			DataLoader(_UpperCamelCase       ,       batch_size=2       ,       collate_fn=train_dataset.collate_fn  )
							for batch in dataloader:
										assert isinstance(_UpperCamelCase       ,       _UpperCamelCase  )
										assert batch["attention_mask"].shape == batch["input_ids"].shape
										# show that articles were trimmed.
										assert batch["input_ids"].shape[1] == max_src_len
										# show that targets are the same len
										assert batch["labels"].shape[1] == max_tgt_len
										if tok_name != MBART_TINY:
													continue
										# check language codes in correct place
										lowerCAmelCase_      			=			shift_tokens_right(batch["labels"]       ,       tokenizer.pad_token_id  )
										assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
										assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
										assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
										assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

										break  # No need to test every batch




				@parameterized.expand([BART_TINY, BERT_BASE_CASED]  )
				def   __a		(      self       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
							lowerCAmelCase_      			=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
							lowerCAmelCase_      			=			max(len(tokenizer.encode(_UpperCamelCase  )  ) for a in ARTICLES  )
							lowerCAmelCase_      			=			max(len(tokenizer.encode(_UpperCamelCase  )  ) for a in SUMMARIES  )
							lowerCAmelCase_      			=			4
							lowerCAmelCase_      			=			LegacySeqaSeqDataset(
							    _UpperCamelCase       ,       data_dir=_UpperCamelCase       ,       type_path="train"       ,       max_source_length=20       ,       max_target_length=_UpperCamelCase       ,       )
							lowerCAmelCase_      			=			DataLoader(_UpperCamelCase       ,       batch_size=2       ,       collate_fn=train_dataset.collate_fn  )
							for batch in dataloader:
										assert batch["attention_mask"].shape == batch["input_ids"].shape
										# show that articles were trimmed.
										assert batch["input_ids"].shape[1] == max_len_source
										assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
										# show that targets were truncated
										assert batch["labels"].shape[1] == trunc_target  # Truncated
										assert max_len_target > trunc_target  # Truncated
										break  # No need to test every batch




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"  )

							lowerCAmelCase_      			=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
							lowerCAmelCase_      			=			tmp_dir.joinpath("train.source"  ).open().readlines()
							lowerCAmelCase_      			=			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
							pack_data_dir(_UpperCamelCase       ,       _UpperCamelCase       ,       128       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			{x.name for x in tmp_dir.iterdir()}
							lowerCAmelCase_      			=			{x.name for x in save_dir.iterdir()}
							lowerCAmelCase_      			=			save_dir.joinpath("train.source"  ).open().readlines()
							# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
							# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
							assert len(_UpperCamelCase  ) < len(_UpperCamelCase  )
							assert len(_UpperCamelCase  ) == 1
							assert len(packed_examples[0]  ) == sum(len(_UpperCamelCase  ) for x in orig_examples  )
							assert orig_paths == new_paths




				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,       reason="This test requires fairseq"  )
				def   __a		(      self  )       ->   str:
							if not FAIRSEQ_AVAILABLE:
										return
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			self._get_dataset(max_len=64  )
							lowerCAmelCase_      			=			64
							lowerCAmelCase_      			=			ds.make_dynamic_sampler(_UpperCamelCase       ,       required_batch_size_multiple=_UpperCamelCase  )
							lowerCAmelCase_      			=			[len(_UpperCamelCase  ) for x in batch_sampler]
							assert len(set(_UpperCamelCase  )  ) > 1  # it's not dynamic batch size if every batch is the same length
							assert sum(_UpperCamelCase  ) == len(_UpperCamelCase  )  # no dropped or added examples
							lowerCAmelCase_      			=			DataLoader(_UpperCamelCase       ,       batch_sampler=_UpperCamelCase       ,       collate_fn=ds.collate_fn       ,       num_workers=2  )
							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			[]
							for batch in data_loader:
										lowerCAmelCase_      			=			batch["input_ids"].shape
										lowerCAmelCase_      			=			src_shape[0]
										assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
										lowerCAmelCase_      			=			np.product(batch["input_ids"].shape  )
										num_src_per_batch.append(_UpperCamelCase  )
										if num_src_tokens > (max_tokens * 1.1):
													failures.append(_UpperCamelCase  )
							assert num_src_per_batch[0] == max(_UpperCamelCase  )
							if failures:
										raise AssertionError(f"""too many tokens in {len(_UpperCamelCase  )} batches"""  )




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			self._get_dataset(max_len=512  )
							lowerCAmelCase_      			=			2
							lowerCAmelCase_      			=			ds.make_sortish_sampler(_UpperCamelCase       ,       shuffle=_UpperCamelCase  )

							lowerCAmelCase_      			=			DataLoader(_UpperCamelCase       ,       batch_size=_UpperCamelCase       ,       collate_fn=ds.collate_fn       ,       num_workers=2  )
							lowerCAmelCase_      			=			DataLoader(_UpperCamelCase       ,       batch_size=_UpperCamelCase       ,       collate_fn=ds.collate_fn       ,       num_workers=2       ,       sampler=_UpperCamelCase  )

							lowerCAmelCase_      			=			tokenizer.pad_token_id

							def count_pad_tokens(_UpperCamelCase       ,       _UpperCamelCase="input_ids"  ):
										return [batch[k].eq(_UpperCamelCase  ).sum().item() for batch in data_loader]

							assert sum(count_pad_tokens(_UpperCamelCase       ,       k="labels"  )  ) < sum(count_pad_tokens(_UpperCamelCase       ,       k="labels"  )  )
							assert sum(count_pad_tokens(_UpperCamelCase  )  ) < sum(count_pad_tokens(_UpperCamelCase  )  )
							assert len(_UpperCamelCase  ) == len(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase=1_000       ,       _UpperCamelCase=128  )       ->   Tuple:
							if os.getenv("USE_REAL_DATA"       ,       _UpperCamelCase  ):
										lowerCAmelCase_      			=			"examples/seq2seq/wmt_en_ro"
										lowerCAmelCase_      			=			max_len * 2 * 64
										if not Path(_UpperCamelCase  ).joinpath("train.len"  ).exists():
													save_len_file(_UpperCamelCase       ,       _UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			"examples/seq2seq/test_data/wmt_en_ro"
										lowerCAmelCase_      			=			max_len * 4
										save_len_file(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase  )
							lowerCAmelCase_      			=			SeqaSeqDataset(
							    _UpperCamelCase       ,       data_dir=_UpperCamelCase       ,       type_path="train"       ,       max_source_length=_UpperCamelCase       ,       max_target_length=_UpperCamelCase       ,       n_obs=_UpperCamelCase       ,       )
							return ds, max_tokens, tokenizer




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			self._get_dataset()
							lowerCAmelCase_      			=			set(DistributedSortishSampler(_UpperCamelCase       ,       256       ,       num_replicas=2       ,       rank=0       ,       add_extra_examples=_UpperCamelCase  )  )
							lowerCAmelCase_      			=			set(DistributedSortishSampler(_UpperCamelCase       ,       256       ,       num_replicas=2       ,       rank=1       ,       add_extra_examples=_UpperCamelCase  )  )
							assert idsa.intersection(_UpperCamelCase  ) == set()




				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]       ,       )
				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							lowerCAmelCase_      			=			AutoTokenizer.from_pretrained(_UpperCamelCase       ,       use_fast=_UpperCamelCase  )
							if tok_name == MBART_TINY:
										lowerCAmelCase_      			=			SeqaSeqDataset(
										    _UpperCamelCase       ,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )       ,       type_path="train"       ,       max_source_length=4       ,       max_target_length=8       ,       src_lang="EN"       ,       tgt_lang="FR"       ,       )
										lowerCAmelCase_      			=			train_dataset.dataset_kwargs
										assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
										lowerCAmelCase_      			=			SeqaSeqDataset(
										    _UpperCamelCase       ,       data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )       ,       type_path="train"       ,       max_source_length=4       ,       max_target_length=8       ,       )
										lowerCAmelCase_      			=			train_dataset.dataset_kwargs
										assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
										assert len(_UpperCamelCase  ) == 1 if tok_name == BART_TINY else len(_UpperCamelCase  ) == 0





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 			_lowerCAmelCase      (			__a      ,   __a ):




				@register_to_config
				def __init__(      self       ,       _UpperCamelCase = 128       ,       _UpperCamelCase = 256       ,       _UpperCamelCase = 2000.0       ,       _UpperCamelCase = 768       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 64       ,       _UpperCamelCase = 2_048       ,       _UpperCamelCase = 0.1       ,       )       ->   str:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			torch.mul(query_input.unsqueeze(-1  )       ,       key_input.unsqueeze(-2  )  )
							return mask.unsqueeze(-3  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   Dict:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Any:
							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       ,       -1e1_0  ).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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Dict:
							# 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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   int:
							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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Tuple:
							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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Union[str, 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   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							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 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   int:
							super().__init__()
							lowerCAmelCase_      			=			nn.Parameter(torch.ones(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			eps




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Union[str, 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 			_lowerCAmelCase      (			nn.Module ):




				def   __a		(      self       ,       _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 			_lowerCAmelCase      (			nn.Module ):




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




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			self.scale_bias(_UpperCamelCase  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			torch.chunk(_UpperCamelCase       ,       2       ,       -1  )
							lowerCAmelCase_      			=			x * (1 + scale) + shift
							return x



