from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
						import datasets






class      __lowercase					(    _A    ):

						@slow
						@require_torch
						def        __a		(	self				:      int   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''							,							'''prajjwal1/bert-tiny'''   )
									lowercase							=		BertTokenizer.from_pretrained('''bert-base-uncased'''   )

									lowercase							=		bertabert.config.encoder.vocab_size
									lowercase							=		tokenizer.sep_token_id
									lowercase							=		tokenizer.cls_token_id
									lowercase							=		1_28

									lowercase							=		datasets.load_dataset('''cnn_dailymail'''							,							'''3.0.0'''							,							split='''train[:1%]'''   )
									lowercase							=		datasets.load_dataset('''cnn_dailymail'''							,							'''3.0.0'''							,							split='''validation[:1%]'''   )

									lowercase							=		train_dataset.select(range(32   )   )
									lowercase							=		val_dataset.select(range(16   )   )

									lowercase							=		4

									def _map_to_encoder_decoder_inputs(__lowerCamelCase				:      Any   ):
												# Tokenizer will automatically set [BOS] <text> [EOS]
												lowercase							=		tokenizer(batch['''article''']							,							padding='''max_length'''							,							truncation=__lowerCamelCase							,							max_length=5_12   )
												lowercase							=		tokenizer(batch['''highlights''']							,							padding='''max_length'''							,							truncation=__lowerCamelCase							,							max_length=1_28   )
												lowercase							=		inputs.input_ids
												lowercase							=		inputs.attention_mask

												lowercase							=		outputs.input_ids
												lowercase							=		outputs.input_ids.copy()
												lowercase							=		[
												    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
												]
												lowercase							=		outputs.attention_mask

												assert all(len(__lowerCamelCase   ) == 5_12 for x in inputs.input_ids   )
												assert all(len(__lowerCamelCase   ) == 1_28 for x in outputs.input_ids   )

												return batch

									def _compute_metrics(__lowerCamelCase				:      int   ):
												lowercase							=		pred.label_ids
												lowercase							=		pred.predictions

												# all unnecessary tokens are removed
												lowercase							=		tokenizer.batch_decode(__lowerCamelCase							,							skip_special_tokens=__lowerCamelCase   )
												lowercase							=		tokenizer.batch_decode(__lowerCamelCase							,							skip_special_tokens=__lowerCamelCase   )

												lowercase							=		sum([int(pred_str[i] == label_str[i]   ) for i in range(len(__lowerCamelCase   )   )]   ) / len(__lowerCamelCase   )

												return {"accuracy": accuracy}

									# map train dataset
									lowercase							=		train_dataset.map(
									    _map_to_encoder_decoder_inputs							,							batched=__lowerCamelCase							,							batch_size=__lowerCamelCase							,							remove_columns=['''article''', '''highlights''']							,							)
									train_dataset.set_format(
									    type='''torch'''							,							columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']							,							)

									# same for validation dataset
									lowercase							=		val_dataset.map(
									    _map_to_encoder_decoder_inputs							,							batched=__lowerCamelCase							,							batch_size=__lowerCamelCase							,							remove_columns=['''article''', '''highlights''']							,							)
									val_dataset.set_format(
									    type='''torch'''							,							columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']							,							)

									lowercase							=		self.get_auto_remove_tmp_dir()

									lowercase							=		SeqaSeqTrainingArguments(
									    output_dir=__lowerCamelCase							,							per_device_train_batch_size=__lowerCamelCase							,							per_device_eval_batch_size=__lowerCamelCase							,							predict_with_generate=__lowerCamelCase							,							evaluation_strategy='''steps'''							,							do_train=__lowerCamelCase							,							do_eval=__lowerCamelCase							,							warmup_steps=0							,							eval_steps=2							,							logging_steps=2							,							)

									# instantiate trainer
									lowercase							=		SeqaSeqTrainer(
									    model=__lowerCamelCase							,							args=__lowerCamelCase							,							compute_metrics=_compute_metrics							,							train_dataset=__lowerCamelCase							,							eval_dataset=__lowerCamelCase							,							tokenizer=__lowerCamelCase							,							)

									# start training
									trainer.train()






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      __lowercase					(    _A       ,     _A    ):

						@register_to_config
						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      int = 1_28							,							__lowerCamelCase				:      int = 2_56							,							__lowerCamelCase				:      float = 2000.0							,							__lowerCamelCase				:      int = 7_68							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 12							,							__lowerCamelCase				:      int = 64							,							__lowerCamelCase				:      int = 20_48							,							__lowerCamelCase				:      float = 0.1							,							)							->		Optional[int]:


									'''simple docstring'''

									super().__init__()

									lowercase							=		nn.Sequential(
									    nn.Linear(__lowerCamelCase							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							nn.Linear(d_model * 4							,							d_model * 4							,							bias=__lowerCamelCase   )							,							nn.SiLU()							,							)

									lowercase							=		nn.Embedding(__lowerCamelCase							,							__lowerCamelCase   )
									lowercase							=		False

									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )

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

									lowercase							=		TaLayerNorm(__lowerCamelCase   )

									lowercase							=		nn.Dropout(p=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		torch.mul(query_input.unsqueeze(-1   )							,							key_input.unsqueeze(-2   )   )
									return mask.unsqueeze(-3   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Any   )							->		List[Any]:


									'''simple docstring'''

									lowercase		,lowercase		,lowercase							=		decoder_input_tokens.shape
									assert decoder_noise_time.shape == (batch,)

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

									lowercase							=		self.conditioning_emb(__lowerCamelCase   ).unsqueeze(1   )

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

									lowercase							=		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.
									lowercase							=		torch.broadcast_to(
									    torch.arange(__lowerCamelCase							,							device=decoder_input_tokens.device   )							,							(batch, seq_length)							,							)

									lowercase							=		self.position_encoding(__lowerCamelCase   )

									lowercase							=		self.continuous_inputs_projection(__lowerCamelCase   )
									inputs += position_encodings
									lowercase							=		self.dropout(__lowerCamelCase   )

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

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

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

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

									lowercase							=		self.decoder_norm(__lowerCamelCase   )
									lowercase							=		self.post_dropout(__lowerCamelCase   )

									lowercase							=		self.spec_out(__lowerCamelCase   )
									return spec_out






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[str]=1E-6   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		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        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[Any]=None							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int=None							,							)							->		Any:


									'''simple docstring'''

									lowercase							=		self.layer[0](
									    __lowerCamelCase							,							conditioning_emb=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

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

												lowercase							=		self.layer[1](
												    __lowerCamelCase							,							key_value_states=__lowerCamelCase							,							attention_mask=__lowerCamelCase							,							)

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

									return (hidden_states,)






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any   )							->		Dict:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaLayerNorm(__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )

									if conditioning_emb is not None:
												lowercase							=		self.FiLMLayer(__lowerCamelCase							,							__lowerCamelCase   )

									# Self-attention block
									lowercase							=		self.attention(__lowerCamelCase   )

									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )

									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									super().__init__()
									lowercase							=		Attention(query_dim=__lowerCamelCase							,							heads=__lowerCamelCase							,							dim_head=__lowerCamelCase							,							out_bias=__lowerCamelCase							,							scale_qk=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Optional[Any]=None							,							__lowerCamelCase				:      List[str]=None							,							)							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									lowercase							=		self.attention(
									    __lowerCamelCase							,							encoder_hidden_states=__lowerCamelCase							,							attention_mask=attention_mask.squeeze(1   )							,							)
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return layer_output






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Tuple   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		TaDenseGatedActDense(d_model=__lowerCamelCase							,							d_ff=__lowerCamelCase							,							dropout_rate=__lowerCamelCase   )
									lowercase							=		TaFiLMLayer(in_features=d_model * 4							,							out_features=__lowerCamelCase   )
									lowercase							=		TaLayerNorm(__lowerCamelCase							,							eps=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      str=None   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.layer_norm(__lowerCamelCase   )
									if conditioning_emb is not None:
												lowercase							=		self.film(__lowerCamelCase							,							__lowerCamelCase   )

									lowercase							=		self.DenseReluDense(__lowerCamelCase   )
									lowercase							=		hidden_states + self.dropout(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Linear(__lowerCamelCase							,							__lowerCamelCase							,							bias=__lowerCamelCase   )
									lowercase							=		nn.Dropout(__lowerCamelCase   )
									lowercase							=		NewGELUActivation()



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.act(self.wi_a(__lowerCamelCase   )   )
									lowercase							=		self.wi_a(__lowerCamelCase   )
									lowercase							=		hidden_gelu * hidden_linear
									lowercase							=		self.dropout(__lowerCamelCase   )

									lowercase							=		self.wo(__lowerCamelCase   )
									return hidden_states






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Dict=1E-6   )							->		Optional[Any]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Parameter(torch.ones(__lowerCamelCase   )   )
									lowercase							=		eps



						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

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

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

									return self.weight * hidden_states






class      __lowercase					(    nn.Module    ):

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      torch.Tensor   )							->		torch.Tensor:


									'''simple docstring'''

									return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.04_4715 * torch.pow(__lowerCamelCase							,							3.0   ))   ))






class      __lowercase					(    nn.Module    ):

						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

									super().__init__()
									lowercase							=		nn.Linear(__lowerCamelCase							,							out_features * 2							,							bias=__lowerCamelCase   )



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

									lowercase							=		self.scale_bias(__lowerCamelCase   )
									lowercase		,lowercase							=		torch.chunk(__lowerCamelCase							,							2							,							-1   )
									lowercase							=		x * (1 + scale) + shift
									return x





