

"""simple docstring"""





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 				SCREAMING_SNAKE_CASE					(		a_    ,							a_       ):






					"""simple docstring"""



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

						lowerCAmelCase__	:						str  				=	nn.Sequential(
						    nn.Linear(lowercase_   ,d_model * 4   ,bias=lowercase_		)   ,nn.SiLU()   ,nn.Linear(d_model * 4   ,d_model * 4   ,bias=lowercase_		)   ,nn.SiLU()   ,)

						lowerCAmelCase__	:						List[str]  				=	nn.Embedding(lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	False

						lowerCAmelCase__	:						int  				=	nn.Linear(lowercase_   ,lowercase_   ,bias=lowercase_		)

						lowerCAmelCase__	:						List[Any]  				=	nn.Dropout(p=lowercase_		)

						lowerCAmelCase__	:						int  				=	nn.ModuleList()
						for lyr_num in range(lowercase_		):
							# FiLM conditional T5 decoder
							lowerCAmelCase__	:						List[Any]  				=	DecoderLayer(d_model=lowercase_   ,d_kv=lowercase_   ,num_heads=lowercase_   ,d_ff=lowercase_   ,dropout_rate=lowercase_		)
							self.decoders.append(lowercase_		)

						lowerCAmelCase__	:						Dict  				=	TaLayerNorm(lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	nn.Dropout(p=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	nn.Linear(lowercase_   ,lowercase_   ,bias=lowercase_		)







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int		):
						lowerCAmelCase__	:						Dict  				=	torch.mul(query_input.unsqueeze(-1		)   ,key_input.unsqueeze(-2		)		)
						return mask.unsqueeze(-3		)







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Any   ,lowercase_			:				Any		):
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	decoder_input_tokens.shape
						assert decoder_noise_time.shape == (batch,)

						# decoder_noise_time is in [0, 1), so rescale to expected timing range.
						lowerCAmelCase__	:						int  				=	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__	:						str  				=	self.conditioning_emb(lowercase_		).unsqueeze(1		)

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

						lowerCAmelCase__	:						Dict  				=	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__	:						Tuple  				=	torch.broadcast_to(
						    torch.arange(lowercase_   ,device=decoder_input_tokens.device		)   ,(batch, seq_length)   ,)

						lowerCAmelCase__	:						Tuple  				=	self.position_encoding(lowercase_		)

						lowerCAmelCase__	:						Dict  				=	self.continuous_inputs_projection(lowercase_		)
						inputs += position_encodings
						lowerCAmelCase__	:						List[Any]  				=	self.dropout(lowercase_		)

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

						# Translate encoding masks to encoder-decoder masks.
						lowerCAmelCase__	:						Tuple  				=	[(x, self.encoder_decoder_mask(lowercase_   ,lowercase_		)) for x, y in encodings_and_masks]

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

						for lyr in self.decoders:
							lowerCAmelCase__	:						Any  				=	lyr(
							    lowercase_   ,conditioning_emb=lowercase_   ,encoder_hidden_states=lowercase_   ,encoder_attention_mask=lowercase_   ,)[0]

						lowerCAmelCase__	:						Optional[Any]  				=	self.decoder_norm(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	self.post_dropout(lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.spec_out(lowercase_		)
						return spec_out







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Tuple   ,lowercase_			:				List[str]   ,lowercase_			:				Any   ,lowercase_			:				str   ,lowercase_			:				Dict=1E-6		):
						super().__init__()
						lowerCAmelCase__	:						List[str]  				=	nn.ModuleList()

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

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

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







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Any   ,lowercase_			:				Dict=None   ,lowercase_			:				Union[str, Any]=None   ,lowercase_			:				List[Any]=None   ,lowercase_			:				Tuple=None   ,lowercase_			:				List[str]=None   ,):
						lowerCAmelCase__	:						List[Any]  				=	self.layer[0](
						    lowercase_   ,conditioning_emb=lowercase_   ,attention_mask=lowercase_   ,)

						if encoder_hidden_states is not None:
							lowerCAmelCase__	:						Optional[Any]  				=	torch.where(encoder_attention_mask > 0   ,0   ,-1E10		).to(
							    encoder_hidden_states.dtype		)

							lowerCAmelCase__	:						Tuple  				=	self.layer[1](
							    lowercase_   ,key_value_states=lowercase_   ,attention_mask=lowercase_   ,)

						# Apply Film Conditional Feed Forward layer
						lowerCAmelCase__	:						int  				=	self.layer[-1](lowercase_   ,lowercase_		)

						return (hidden_states,)







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				str   ,lowercase_			:				Tuple   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				Tuple		):
						super().__init__()
						lowerCAmelCase__	:						str  				=	TaLayerNorm(lowercase_		)
						lowerCAmelCase__	:						Any  				=	TaFiLMLayer(in_features=d_model * 4   ,out_features=lowercase_		)
						lowerCAmelCase__	:						Any  				=	Attention(query_dim=lowercase_   ,heads=lowercase_   ,dim_head=lowercase_   ,out_bias=lowercase_   ,scale_qk=lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	nn.Dropout(lowercase_		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				List[Any]   ,lowercase_			:				Any=None   ,lowercase_			:				List[str]=None   ,):
						# pre_self_attention_layer_norm
						lowerCAmelCase__	:						str  				=	self.layer_norm(lowercase_		)

						if conditioning_emb is not None:
							lowerCAmelCase__	:						Any  				=	self.FiLMLayer(lowercase_   ,lowercase_		)

						# Self-attention block
						lowerCAmelCase__	:						int  				=	self.attention(lowercase_		)

						lowerCAmelCase__	:						str  				=	hidden_states + self.dropout(lowercase_		)

						return hidden_states







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				str   ,lowercase_			:				List[Any]   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				Tuple   ,lowercase_			:				List[Any]		):
						super().__init__()
						lowerCAmelCase__	:						Tuple  				=	Attention(query_dim=lowercase_   ,heads=lowercase_   ,dim_head=lowercase_   ,out_bias=lowercase_   ,scale_qk=lowercase_		)
						lowerCAmelCase__	:						str  				=	TaLayerNorm(lowercase_   ,eps=lowercase_		)
						lowerCAmelCase__	:						Dict  				=	nn.Dropout(lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				Tuple   ,lowercase_			:				str=None   ,lowercase_			:				Any=None   ,):
						lowerCAmelCase__	:						int  				=	self.layer_norm(lowercase_		)
						lowerCAmelCase__	:						int  				=	self.attention(
						    lowercase_   ,encoder_hidden_states=lowercase_   ,attention_mask=attention_mask.squeeze(1		)   ,)
						lowerCAmelCase__	:						Optional[Any]  				=	hidden_states + self.dropout(lowercase_		)
						return layer_output







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Optional[int]=None		):
						lowerCAmelCase__	:						Any  				=	self.layer_norm(lowercase_		)
						if conditioning_emb is not None:
							lowerCAmelCase__	:						str  				=	self.film(lowercase_   ,lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.DenseReluDense(lowercase_		)
						lowerCAmelCase__	:						Any  				=	hidden_states + self.dropout(lowercase_		)
						return hidden_states







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Any		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.act(self.wi_a(lowercase_		)		)
						lowerCAmelCase__	:						Any  				=	self.wi_a(lowercase_		)
						lowerCAmelCase__	:						List[str]  				=	hidden_gelu * hidden_linear
						lowerCAmelCase__	:						List[str]  				=	self.dropout(lowercase_		)

						lowerCAmelCase__	:						Union[str, Any]  				=	self.wo(lowercase_		)
						return hidden_states







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Optional[int]   ,lowercase_			:				Union[str, Any]=1E-6		):
						super().__init__()
						lowerCAmelCase__	:						str  				=	nn.Parameter(torch.ones(lowercase_		)		)
						lowerCAmelCase__	:						int  				=	eps







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				str		):
						# 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__	:						Union[str, Any]  				=	hidden_states.to(torch.floataa		).pow(2		).mean(-1   ,keepdim=lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	hidden_states * torch.rsqrt(variance + self.variance_epsilon		)

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

						return self.weight * hidden_states







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				torch.Tensor		):
						return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.04_4715 * torch.pow(lowercase_   ,3.0		))		))







class 				SCREAMING_SNAKE_CASE					(		nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				Union[str, Any]		):
						super().__init__()
						lowerCAmelCase__	:						Dict  				=	nn.Linear(lowercase_   ,out_features * 2   ,bias=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				Dict   ,lowercase_			:				List[Any]		):
						lowerCAmelCase__	:						Any  				=	self.scale_bias(lowercase_		)
						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	torch.chunk(lowercase_   ,2   ,-1		)
						lowerCAmelCase__	:						int  				=	x * (1 + scale) + shift
						return x







"""simple docstring"""





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase  :      List[str]							      =		'''true'''

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=16 ):
	set_seed(42 )
	lowerCAmelCase__	:						Union[str, Any]  				=	RegressionModel()
	lowerCAmelCase__	:						Optional[int]  				=	deepcopy(A_ )
	lowerCAmelCase__	:						Any  				=	RegressionDataset(length=A_ )
	lowerCAmelCase__	:						List[str]  				=	DataLoader(A_						,     batch_size=A_ )
	model.to(accelerator.device )
	lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	accelerator.prepare(A_						,     A_ )
	return model, ddp_model, dataloader

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						List[str]  				=	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
	lowerCAmelCase__	:						List[str]  				=	load_dataset('''glue'''						,     '''mrpc'''						,     split='''validation''' )

	def tokenize_function(A_ ):
		lowerCAmelCase__	:						Optional[Any]  				=	tokenizer(examples['''sentence1''']						,     examples['''sentence2''']						,     truncation=A_						,     max_length=A_ )
		return outputs

	with accelerator.main_process_first():
		lowerCAmelCase__	:						Dict  				=	dataset.map(
		    A_						,     batched=A_						,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,     )

	lowerCAmelCase__	:						int  				=	tokenized_datasets.rename_column('''label'''						,     '''labels''' )

	def collate_fn(A_ ):
		if use_longest:
			return tokenizer.pad(A_						,     padding='''longest'''						,     return_tensors='''pt''' )
		return tokenizer.pad(A_						,     padding='''max_length'''						,     max_length=1_28						,     return_tensors='''pt''' )

	return DataLoader(A_						,     shuffle=A_						,     collate_fn=A_						,     batch_size=16 )

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator(dispatch_batches=A_						,     split_batches=A_ )
	lowerCAmelCase__	:						str  				=	get_dataloader(A_						,     not dispatch_batches )
	lowerCAmelCase__	:						Optional[int]  				=	AutoModelForSequenceClassification.from_pretrained(
	    '''hf-internal-testing/mrpc-bert-base-cased'''						,     return_dict=A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	accelerator.prepare(A_						,     A_ )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	[]
	for batch in dataloader:
		lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	batch.values()
		with torch.no_grad():
			lowerCAmelCase__	:						List[str]  				=	model(A_ )
			lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	accelerator.gather_for_metrics((logit, target) )
			logits_and_targets.append((logit, target) )
	lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	[], []
	for logit, targ in logits_and_targets:
		logits.append(A_ )
		targs.append(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	torch.cat(A_ ), torch.cat(A_ )
	return logits, targs

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=False						,     A_=False						,     A_=16 ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	get_basic_setup(A_						,     A_						,     A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	generate_predictions(A_						,     A_						,     A_ )
	assert (
	    len(A_ ) == num_samples
	), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_ )}'

def   __SCREAMING_SNAKE_CASE  (      A_ = False						,     A_ = False ):
	lowerCAmelCase__	:						Optional[Any]  				=	evaluate.load('''glue'''						,     '''mrpc''' )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	get_mrpc_setup(A_						,     A_ )
	# First do baseline
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	setup['''no''']
	model.to(A_ )
	model.eval()
	for batch in dataloader:
		batch.to(A_ )
		with torch.inference_mode():
			lowerCAmelCase__	:						Optional[int]  				=	model(**A_ )
		lowerCAmelCase__	:						Dict  				=	outputs.logits.argmax(dim=-1 )
		metric.add_batch(predictions=A_						,     references=batch['''labels'''] )
	lowerCAmelCase__	:						Dict  				=	metric.compute()

	# Then do distributed
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	setup['''ddp''']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowerCAmelCase__	:						Union[str, Any]  				=	model(**A_ )
		lowerCAmelCase__	:						int  				=	outputs.logits.argmax(dim=-1 )
		lowerCAmelCase__	:						int  				=	batch['''labels''']
		lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	accelerator.gather_for_metrics((preds, references) )
		metric.add_batch(predictions=A_						,     references=A_ )
	lowerCAmelCase__	:						List[Any]  				=	metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]						,     distributed[key] ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('''**Testing gather_for_metrics**''' )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`' )
				test_mrpc(A_						,     A_ )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test torch metrics**''' )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			lowerCAmelCase__	:						Optional[Any]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
			if accelerator.is_local_main_process:
				print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
			test_torch_metrics(A_						,     99 )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**''' )
	lowerCAmelCase__	:						List[str]  				=	Accelerator()
	test_torch_metrics(A_						,     5_12 )
	accelerator.state._reset_state()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()




