



"""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 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	@register_to_config
	def __init__(     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		int = 128       ,			_SCREAMING_SNAKE_CASE:		int = 256       ,			_SCREAMING_SNAKE_CASE:		float = 2000.0       ,			_SCREAMING_SNAKE_CASE:		int = 768       ,			_SCREAMING_SNAKE_CASE:		int = 12       ,			_SCREAMING_SNAKE_CASE:		int = 12       ,			_SCREAMING_SNAKE_CASE:		int = 64       ,			_SCREAMING_SNAKE_CASE:		int = 2048       ,			_SCREAMING_SNAKE_CASE:		float = 0.1       ,			)				->       Optional[Any]:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase      :  Optional[Any]												=       nn.Sequential(
				    nn.Linear(_SCREAMING_SNAKE_CASE       ,			d_model * 4       ,			bias=_SCREAMING_SNAKE_CASE)       ,			nn.SiLU()       ,			nn.Linear(d_model * 4       ,			d_model * 4       ,			bias=_SCREAMING_SNAKE_CASE)       ,			nn.SiLU()       ,			)

				__lowerCAmelCase      :  List[str]												=       nn.Embedding(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       False

				__lowerCAmelCase      :  Optional[Any]												=       nn.Linear(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			bias=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Union[str, Any]												=       nn.Dropout(p=_SCREAMING_SNAKE_CASE)

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

				__lowerCAmelCase      :  Any												=       TaLayerNorm(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[Any]												=       nn.Dropout(p=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[Any]												=       nn.Linear(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			bias=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       torch.mul(query_input.unsqueeze(-1)       ,			key_input.unsqueeze(-2))
				return mask.unsqueeze(-3)







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

				"""simple docstring"""

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

				# decoder_noise_time is in [0, 1), so rescale to expected timing range.
				__lowerCAmelCase      :  Dict												=       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      :  int												=       self.conditioning_emb(_SCREAMING_SNAKE_CASE).unsqueeze(1)

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

				__lowerCAmelCase      :  List[Any]												=       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      :  Optional[Any]												=       torch.broadcast_to(
				    torch.arange(_SCREAMING_SNAKE_CASE       ,			device=decoder_input_tokens.device)       ,			(batch, seq_length)       ,			)

				__lowerCAmelCase      :  Optional[Any]												=       self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Tuple												=       self.continuous_inputs_projection(_SCREAMING_SNAKE_CASE)
				inputs += position_encodings
				__lowerCAmelCase      :  Union[str, Any]												=       self.dropout(_SCREAMING_SNAKE_CASE)

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

				# Translate encoding masks to encoder-decoder masks.
				__lowerCAmelCase      :  Any												=       [(x, self.encoder_decoder_mask(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)) for x, y in encodings_and_masks]

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

				for lyr in self.decoders:
							__lowerCAmelCase      :  List[Any]												=       lyr(
							    _SCREAMING_SNAKE_CASE       ,			conditioning_emb=_SCREAMING_SNAKE_CASE       ,			encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,			encoder_attention_mask=_SCREAMING_SNAKE_CASE       ,			)[0]

				__lowerCAmelCase      :  Optional[int]												=       self.decoder_norm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[Any]												=       self.post_dropout(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  str												=       self.spec_out(_SCREAMING_SNAKE_CASE)
				return spec_out






class 			A__   (  nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase      :  str												=       nn.ModuleList()

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

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

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Any=None       ,			_SCREAMING_SNAKE_CASE:		Any=None       ,			_SCREAMING_SNAKE_CASE:		int=None       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=None       ,			_SCREAMING_SNAKE_CASE:		Any=None       ,			)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       self.layer[0](
				    _SCREAMING_SNAKE_CASE       ,			conditioning_emb=_SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE       ,			)

				if encoder_hidden_states is not None:
							__lowerCAmelCase      :  Tuple												=       torch.where(encoder_attention_mask > 0       ,			0       ,			-1e10).to(
							    encoder_hidden_states.dtype)

							__lowerCAmelCase      :  Optional[int]												=       self.layer[1](
							    _SCREAMING_SNAKE_CASE       ,			key_value_states=_SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE       ,			)

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

				return (hidden_states,)






class 			A__   (  nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase      :  Optional[Any]												=       TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Tuple												=       TaFiLMLayer(in_features=d_model * 4       ,			out_features=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  int												=       Attention(query_dim=_SCREAMING_SNAKE_CASE       ,			heads=_SCREAMING_SNAKE_CASE       ,			dim_head=_SCREAMING_SNAKE_CASE       ,			out_bias=_SCREAMING_SNAKE_CASE       ,			scale_qk=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       nn.Dropout(_SCREAMING_SNAKE_CASE)







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

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.layer_norm(_SCREAMING_SNAKE_CASE)

				if conditioning_emb is not None:
							__lowerCAmelCase      :  Optional[Any]												=       self.FiLMLayer(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				# Self-attention block
				__lowerCAmelCase      :  str												=       self.attention(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  str												=       hidden_states + self.dropout(_SCREAMING_SNAKE_CASE)

				return hidden_states






class 			A__   (  nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase      :  Optional[Any]												=       Attention(query_dim=_SCREAMING_SNAKE_CASE       ,			heads=_SCREAMING_SNAKE_CASE       ,			dim_head=_SCREAMING_SNAKE_CASE       ,			out_bias=_SCREAMING_SNAKE_CASE       ,			scale_qk=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[Any]												=       TaLayerNorm(_SCREAMING_SNAKE_CASE       ,			eps=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       nn.Dropout(_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			_SCREAMING_SNAKE_CASE:		Any=None       ,			)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       self.layer_norm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       self.attention(
				    _SCREAMING_SNAKE_CASE       ,			encoder_hidden_states=_SCREAMING_SNAKE_CASE       ,			attention_mask=attention_mask.squeeze(1)       ,			)
				__lowerCAmelCase      :  List[Any]												=       hidden_states + self.dropout(_SCREAMING_SNAKE_CASE)
				return layer_output






class 			A__   (  nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase      :  List[Any]												=       TaDenseGatedActDense(d_model=_SCREAMING_SNAKE_CASE       ,			d_ff=_SCREAMING_SNAKE_CASE       ,			dropout_rate=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       TaFiLMLayer(in_features=d_model * 4       ,			out_features=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[str]												=       TaLayerNorm(_SCREAMING_SNAKE_CASE       ,			eps=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       nn.Dropout(_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=None)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.layer_norm(_SCREAMING_SNAKE_CASE)
				if conditioning_emb is not None:
							__lowerCAmelCase      :  int												=       self.film(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

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






class 			A__   (  nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

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







	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Optional[int])				->       Any:

				"""simple docstring"""

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

				__lowerCAmelCase      :  int												=       self.wo(_SCREAMING_SNAKE_CASE)
				return hidden_states






class 			A__   (  nn.Module							):




	'''simple docstring'''





	def __init__(     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=1e-6)				->       str:

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase      :  List[Any]												=       nn.Parameter(torch.ones(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase      :  Union[str, Any]												=       eps







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Tuple:

				"""simple docstring"""

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

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

				return self.weight * hidden_states






class 			A__   (  nn.Module							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		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(_SCREAMING_SNAKE_CASE       ,			3.0))))






class 			A__   (  nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()
				__lowerCAmelCase      :  List[Any]												=       nn.Linear(_SCREAMING_SNAKE_CASE       ,			out_features * 2       ,			bias=_SCREAMING_SNAKE_CASE)







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

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       self.scale_bias(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase						,	__lowerCAmelCase      :  List[Any]												=       torch.chunk(_SCREAMING_SNAKE_CASE       ,			2       ,			-1)
				__lowerCAmelCase      :  Tuple												=       x * (1 + scale) + shift
				return x




"""simple docstring"""






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		str)				->       Dict:

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  List[str]												=       re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)")
							__lowerCAmelCase      :  List[Any]												=       input_file.read()
							__lowerCAmelCase      :  Any												=       regexp.search(_SCREAMING_SNAKE_CASE)

				return match

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

				"""simple docstring"""

				with open(_SCREAMING_SNAKE_CASE       ,			encoding="utf-8") as input_file:
							__lowerCAmelCase      :  Any												=       re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"       ,			re.DOTALL)
							__lowerCAmelCase      :  Optional[int]												=       input_file.read()
							# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
							__lowerCAmelCase      :  int												=       regexp.finditer(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Union[str, Any]												=       [match for match in matches if match is not None and match.group(1) is not None]
				return matches[0] if matches else None

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       Path("./datasets")
				__lowerCAmelCase      :  Optional[int]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       Path("./datasets")
				__lowerCAmelCase      :  Union[str, Any]												=       list(dataset_paths.absolute().glob("**/*.py"))

				for dataset in dataset_files:
							if self._no_print_statements(str(_SCREAMING_SNAKE_CASE)):
										raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""")