



'''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 		lowerCamelCase__      (						A	,   A     ):




					'''simple docstring'''



					@register_to_config
					def __init__(  self				:	str     ,  UpperCamelCase_				:	int = 128     ,  UpperCamelCase_				:	int = 256     ,  UpperCamelCase_				:	float = 20_00.0     ,  UpperCamelCase_				:	int = 768     ,  UpperCamelCase_				:	int = 12     ,  UpperCamelCase_				:	int = 12     ,  UpperCamelCase_				:	int = 64     ,  UpperCamelCase_				:	int = 2048     ,  UpperCamelCase_				:	float = 0.1     ,  )  ->       Optional[int]:



						'''simple docstring'''





						super().__init__()

						_lowercase					:       Tuple       =		nn.Sequential(
						    nn.Linear(UpperCamelCase_     ,  d_model * 4     ,  bias=UpperCamelCase_					)     ,  nn.SiLU()     ,  nn.Linear(d_model * 4     ,  d_model * 4     ,  bias=UpperCamelCase_					)     ,  nn.SiLU()     ,  )

						_lowercase					:       int       =		nn.Embedding(UpperCamelCase_     ,  UpperCamelCase_					)
						_lowercase					:       List[Any]       =		False

						_lowercase					:       Union[str, Any]       =		nn.Linear(UpperCamelCase_     ,  UpperCamelCase_     ,  bias=UpperCamelCase_					)

						_lowercase					:       List[str]       =		nn.Dropout(p=UpperCamelCase_					)

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

						_lowercase					:       Any       =		TaLayerNorm(UpperCamelCase_					)

						_lowercase					:       List[Any]       =		nn.Dropout(p=UpperCamelCase_					)
						_lowercase					:       List[Any]       =		nn.Linear(UpperCamelCase_     ,  UpperCamelCase_     ,  bias=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	str					)  ->       Union[str, Any]:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	Optional[int]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase  ,    _lowercase  ,    _lowercase					:       str       =		decoder_input_tokens.shape
						assert decoder_noise_time.shape == (batch,)

						# decoder_noise_time is in [0, 1), so rescale to expected timing range.
						_lowercase					:       Any       =		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					:       Tuple       =		self.conditioning_emb(UpperCamelCase_					).unsqueeze(1					)

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

						_lowercase					:       str       =		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					:       Union[str, Any]       =		torch.broadcast_to(
						    torch.arange(UpperCamelCase_     ,  device=decoder_input_tokens.device					)     ,  (batch, seq_length)     ,  )

						_lowercase					:       Any       =		self.position_encoding(UpperCamelCase_					)

						_lowercase					:       List[Any]       =		self.continuous_inputs_projection(UpperCamelCase_					)
						inputs += position_encodings
						_lowercase					:       str       =		self.dropout(UpperCamelCase_					)

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

						# Translate encoding masks to encoder-decoder masks.
						_lowercase					:       List[str]       =		[(x, self.encoder_decoder_mask(UpperCamelCase_     ,  UpperCamelCase_					)) for x, y in encodings_and_masks]

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

						for lyr in self.decoders:
							_lowercase					:       Union[str, Any]       =		lyr(
							    UpperCamelCase_     ,  conditioning_emb=UpperCamelCase_     ,  encoder_hidden_states=UpperCamelCase_     ,  encoder_attention_mask=UpperCamelCase_     ,  )[0]

						_lowercase					:       Union[str, Any]       =		self.decoder_norm(UpperCamelCase_					)
						_lowercase					:       str       =		self.post_dropout(UpperCamelCase_					)

						_lowercase					:       Optional[int]       =		self.spec_out(UpperCamelCase_					)
						return spec_out






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



					def __init__(  self				:	Optional[Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Tuple     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	List[str]=1E-6					)  ->       Dict:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Union[str, Any]       =		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 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[int]=None     ,  UpperCamelCase_				:	str=None     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  UpperCamelCase_				:	Any=None     ,  UpperCamelCase_				:	str=None     ,  )  ->       Any:



						'''simple docstring'''





						_lowercase					:       int       =		self.layer[0](
						    UpperCamelCase_     ,  conditioning_emb=UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  )

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

							_lowercase					:       Optional[Any]       =		self.layer[1](
							    UpperCamelCase_     ,  key_value_states=UpperCamelCase_     ,  attention_mask=UpperCamelCase_     ,  )

						# Apply Film Conditional Feed Forward layer
						_lowercase					:       List[str]       =		self.layer[-1](UpperCamelCase_     ,  UpperCamelCase_					)

						return (hidden_states,)






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



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



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Union[str, Any]       =		TaLayerNorm(UpperCamelCase_					)
						_lowercase					:       Any       =		TaFiLMLayer(in_features=d_model * 4     ,  out_features=UpperCamelCase_					)
						_lowercase					:       str       =		Attention(query_dim=UpperCamelCase_     ,  heads=UpperCamelCase_     ,  dim_head=UpperCamelCase_     ,  out_bias=UpperCamelCase_     ,  scale_qk=UpperCamelCase_					)
						_lowercase					:       List[Any]       =		nn.Dropout(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	int=None     ,  UpperCamelCase_				:	Tuple=None     ,  )  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.layer_norm(UpperCamelCase_					)

						if conditioning_emb is not None:
							_lowercase					:       int       =		self.FiLMLayer(UpperCamelCase_     ,  UpperCamelCase_					)

						# Self-attention block
						_lowercase					:       int       =		self.attention(UpperCamelCase_					)

						_lowercase					:       List[Any]       =		hidden_states + self.dropout(UpperCamelCase_					)

						return hidden_states






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



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



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Tuple       =		Attention(query_dim=UpperCamelCase_     ,  heads=UpperCamelCase_     ,  dim_head=UpperCamelCase_     ,  out_bias=UpperCamelCase_     ,  scale_qk=UpperCamelCase_					)
						_lowercase					:       List[str]       =		TaLayerNorm(UpperCamelCase_     ,  eps=UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		nn.Dropout(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Any=None     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  )  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.layer_norm(UpperCamelCase_					)
						_lowercase					:       Tuple       =		self.attention(
						    UpperCamelCase_     ,  encoder_hidden_states=UpperCamelCase_     ,  attention_mask=attention_mask.squeeze(1					)     ,  )
						_lowercase					:       Union[str, Any]       =		hidden_states + self.dropout(UpperCamelCase_					)
						return layer_output






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



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



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Dict       =		TaDenseGatedActDense(d_model=UpperCamelCase_     ,  d_ff=UpperCamelCase_     ,  dropout_rate=UpperCamelCase_					)
						_lowercase					:       List[Any]       =		TaFiLMLayer(in_features=d_model * 4     ,  out_features=UpperCamelCase_					)
						_lowercase					:       Tuple       =		TaLayerNorm(UpperCamelCase_     ,  eps=UpperCamelCase_					)
						_lowercase					:       Dict       =		nn.Dropout(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	str=None					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.layer_norm(UpperCamelCase_					)
						if conditioning_emb is not None:
							_lowercase					:       int       =		self.film(UpperCamelCase_     ,  UpperCamelCase_					)

						_lowercase					:       Tuple       =		self.DenseReluDense(UpperCamelCase_					)
						_lowercase					:       List[Any]       =		hidden_states + self.dropout(UpperCamelCase_					)
						return hidden_states






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Tuple					)  ->       Optional[int]:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Optional[Any]       =		nn.Linear(UpperCamelCase_     ,  UpperCamelCase_     ,  bias=UpperCamelCase_					)
						_lowercase					:       Dict       =		nn.Linear(UpperCamelCase_     ,  UpperCamelCase_     ,  bias=UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		nn.Linear(UpperCamelCase_     ,  UpperCamelCase_     ,  bias=UpperCamelCase_					)
						_lowercase					:       Tuple       =		nn.Dropout(UpperCamelCase_					)
						_lowercase					:       int       =		NewGELUActivation()




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	List[str]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.act(self.wi_a(UpperCamelCase_					)					)
						_lowercase					:       List[Any]       =		self.wi_a(UpperCamelCase_					)
						_lowercase					:       Tuple       =		hidden_gelu * hidden_linear
						_lowercase					:       Union[str, Any]       =		self.dropout(UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		self.wo(UpperCamelCase_					)
						return hidden_states






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



					def __init__(  self				:	Any     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Dict=1E-6					)  ->       Any:



						'''simple docstring'''





						super().__init__()
						_lowercase					:       int       =		nn.Parameter(torch.ones(UpperCamelCase_					)					)
						_lowercase					:       Optional[int]       =		eps




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Optional[Any]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       str       =		hidden_states.to(torch.floataa					).pow(2					).mean(-1     ,  keepdim=UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		hidden_states * torch.rsqrt(variance + self.variance_epsilon					)

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

						return self.weight * hidden_states






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	torch.Tensor					)  ->       torch.Tensor:



						'''simple docstring'''





						return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi					) * (input + 0.04_47_15 * torch.pow(UpperCamelCase_     ,  3.0					))					))






class 		lowerCamelCase__      (						nn.Module     ):




					'''simple docstring'''



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



						'''simple docstring'''





						super().__init__()
						_lowercase					:       Optional[Any]       =		nn.Linear(UpperCamelCase_     ,  out_features * 2     ,  bias=UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Optional[int]     ,  UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Any       =		self.scale_bias(UpperCamelCase_					)
						_lowercase  ,    _lowercase					:       Dict       =		torch.chunk(UpperCamelCase_     ,  2     ,  -1					)
						_lowercase					:       Dict       =		x * (1 + scale) + shift
						return x





'''simple docstring'''

import argparse
from collections import defaultdict







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					) ->			int:
	_lowercase					:       Optional[int]       =		f'''{file}_{class_name}_{test_name}'''
	done_test[_id] += 1

	with open(_lowercase,				'r'					) as f:
		_lowercase					:       Optional[int]       =		f.readlines()

	_lowercase					:       Dict       =		f'''class {class_name}('''
	_lowercase					:       List[Any]       =		f'''{4 * " "}def {test_name}('''
	_lowercase					:       List[str]       =		f'''{8 * " "}{correct_line.split()[0]}'''
	_lowercase					:       List[str]       =		f'''{16 * " "}{correct_line.split()[0]}'''
	_lowercase					:       Dict       =		False
	_lowercase					:       str       =		False
	_lowercase					:       List[Any]       =		False
	_lowercase					:       Union[str, Any]       =		False
	_lowercase					:       Any       =		0
	_lowercase					:       Tuple       =		0

	_lowercase					:       Optional[int]       =		[]
	for line in lines:
		if line.startswith(_lowercase					):
			_lowercase					:       int       =		True
		elif in_class and line.startswith(_lowercase					):
			_lowercase					:       List[Any]       =		True
		elif in_class and in_func and (line.startswith(_lowercase					) or line.startswith(_lowercase					)):
			_lowercase					:       str       =		len(line.split(correct_line.split()[0]					)[0]					)
			count += 1

			if count == done_test[_id]:
				_lowercase					:       List[Any]       =		True

		if in_class and in_func and in_line:
			if ")" not in line:
				continue
			else:
				_lowercase					:       Any       =		True

		if in_class and in_func and in_line and insert_line:
			new_lines.append(f'''{spaces * " "}{correct_line}'''					)
			_lowercase					:       Any       =		False
		else:
			new_lines.append(_lowercase					)

	with open(_lowercase,				'w'					) as f:
		for line in new_lines:
			f.write(_lowercase					)







def 						__UpperCamelCase				(							_lowercase,				_lowercase=None					) ->			Optional[Any]:
	if fail is not None:
		with open(_lowercase,				'r'					) as f:
			_lowercase					:       Any       =		{l.strip() for l in f.readlines()}
	else:
		_lowercase					:       str       =		None

	with open(_lowercase,				'r'					) as f:
		_lowercase					:       str       =		f.readlines()

	_lowercase					:       Union[str, Any]       =		defaultdict(_lowercase					)
	for line in correct_lines:
		_lowercase  ,    _lowercase  ,    _lowercase  ,    _lowercase					:       Union[str, Any]       =		line.split(';'					)
		if test_failures is None or "::".join([file, class_name, test_name]					) in test_failures:
			overwrite_file(_lowercase,				_lowercase,				_lowercase,				_lowercase,				_lowercase					)


if __name__ == "__main__":
		_A				:    str     						=argparse.ArgumentParser()
		parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
		parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
		_A				:    Union[str, Any]     						=parser.parse_args()

		main(args.correct_filename, args.fail_filename)
