"""simple docstring"""





from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowercase_   (   _lowerCamelCase:	Dict      ) ->      List[Any]:



							'''simple docstring'''

							return DownloadCommand(args.model						,   args.cache_dir						,   args.force						,   args.trust_remote_code      )







class  _snake_case     (					a__		):






						@staticmethod
						def       lowerCamelCase__     (       UpperCAmelCase      :			ArgumentParser    ):
													__lowerCamelCase					:		str		   =     parser.add_parser("download"    )
													download_parser.add_argument(
													    "--cache-dir"							,					type=UpperCAmelCase							,					default=UpperCAmelCase							,					help="Path to location to store the models"    )
													download_parser.add_argument(
													    "--force"							,					action="store_true"							,					help="Force the model to be download even if already in cache-dir"    )
													download_parser.add_argument(
													    "--trust-remote-code"							,					action="store_true"							,					help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"							,					)
													download_parser.add_argument("model"							,					type=UpperCAmelCase							,					help="Name of the model to download"    )
													download_parser.set_defaults(func=UpperCAmelCase    )






						def __init__(       self      :			List[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			bool							,					UpperCAmelCase      :			bool    ):
													__lowerCamelCase					:		Optional[Any]		   =     model
													__lowerCamelCase					:		List[str]		   =     cache
													__lowerCamelCase					:		Optional[int]		   =     force
													__lowerCamelCase					:		Union[str, Any]		   =     trust_remote_code







						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													from ..models.auto import AutoModel, AutoTokenizer

													AutoModel.from_pretrained(
													    self._model							,					cache_dir=self._cache							,					force_download=self._force							,					trust_remote_code=self._trust_remote_code    )
													AutoTokenizer.from_pretrained(
													    self._model							,					cache_dir=self._cache							,					force_download=self._force							,					trust_remote_code=self._trust_remote_code    )
"""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  _snake_case     (					a__				, a__		):






						@register_to_config
						def __init__(       self      :			Optional[Any]							,					UpperCAmelCase      :			int = 128							,					UpperCAmelCase      :			int = 256							,					UpperCAmelCase      :			float = 2_0_0_0.0							,					UpperCAmelCase      :			int = 768							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 12							,					UpperCAmelCase      :			int = 64							,					UpperCAmelCase      :			int = 2048							,					UpperCAmelCase      :			float = 0.1							,					):
													super().__init__()

													__lowerCamelCase					:		List[Any]		   =     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					:		str		   =     nn.Embedding(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     False

													__lowerCamelCase					:		Optional[int]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(p=UpperCAmelCase    )

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

													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     nn.Dropout(p=UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int    ):
													__lowerCamelCase					:		List[Any]		   =     torch.mul(query_input.unsqueeze(-1    )							,					key_input.unsqueeze(-2    )    )
													return mask.unsqueeze(-3    )







						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			str    ):
													__lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Optional[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					:		str		   =     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					:		Optional[int]		   =     self.conditioning_emb(UpperCAmelCase    ).unsqueeze(1    )

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

													__lowerCamelCase					:		Tuple		   =     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					:		int		   =     torch.broadcast_to(
													    torch.arange(UpperCAmelCase							,					device=decoder_input_tokens.device    )							,					(batch, seq_length)							,					)

													__lowerCamelCase					:		Optional[Any]		   =     self.position_encoding(UpperCAmelCase    )

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

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

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

													# cross attend style: concat encodings
													__lowerCamelCase					:		Union[str, 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(
																				    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					encoder_attention_mask=UpperCAmelCase							,					)[0]

													__lowerCamelCase					:		Dict		   =     self.decoder_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     self.post_dropout(UpperCAmelCase    )

													__lowerCamelCase					:		str		   =     self.spec_out(UpperCAmelCase    )
													return spec_out

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			List[str]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]=1E-6    ):
													super().__init__()
													__lowerCamelCase					:		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       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			Tuple=None							,					UpperCAmelCase      :			Dict=None							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			Dict=None							,					):
													__lowerCamelCase					:		Union[str, Any]		   =     self.layer[0](
													    UpperCAmelCase							,					conditioning_emb=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

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

																				__lowerCamelCase					:		Dict		   =     self.layer[1](
																				    UpperCAmelCase							,					key_value_states=UpperCAmelCase							,					attention_mask=UpperCAmelCase							,					)

													# Apply Film Conditional Feed Forward layer
													__lowerCamelCase					:		List[Any]		   =     self.layer[-1](UpperCAmelCase							,					UpperCAmelCase    )

													return (hidden_states,)

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Dict    ):
													super().__init__()
													__lowerCamelCase					:		Union[str, Any]		   =     TaLayerNorm(UpperCAmelCase    )
													__lowerCamelCase					:		Any		   =     TaFiLMLayer(in_features=d_model * 4							,					out_features=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     Attention(query_dim=UpperCAmelCase							,					heads=UpperCAmelCase							,					dim_head=UpperCAmelCase							,					out_bias=UpperCAmelCase							,					scale_qk=UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			List[Any]=None							,					UpperCAmelCase      :			str=None							,					):
													# pre_self_attention_layer_norm
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )

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

													# Self-attention block
													__lowerCamelCase					:		Optional[Any]		   =     self.attention(UpperCAmelCase    )

													__lowerCamelCase					:		Dict		   =     hidden_states + self.dropout(UpperCAmelCase    )

													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     Attention(query_dim=UpperCAmelCase							,					heads=UpperCAmelCase							,					dim_head=UpperCAmelCase							,					out_bias=UpperCAmelCase							,					scale_qk=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]=None							,					UpperCAmelCase      :			Optional[int]=None							,					):
													__lowerCamelCase					:		str		   =     self.layer_norm(UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     self.attention(
													    UpperCAmelCase							,					encoder_hidden_states=UpperCAmelCase							,					attention_mask=attention_mask.squeeze(1    )							,					)
													__lowerCamelCase					:		List[str]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return layer_output

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		str		   =     TaDenseGatedActDense(d_model=UpperCAmelCase							,					d_ff=UpperCAmelCase							,					dropout_rate=UpperCAmelCase    )
													__lowerCamelCase					:		Union[str, Any]		   =     TaFiLMLayer(in_features=d_model * 4							,					out_features=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     TaLayerNorm(UpperCAmelCase							,					eps=UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     nn.Dropout(UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			Optional[int]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any=None    ):
													__lowerCamelCase					:		int		   =     self.layer_norm(UpperCAmelCase    )
													if conditioning_emb is not None:
																				__lowerCamelCase					:		Union[str, Any]		   =     self.film(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase					:		List[str]		   =     self.DenseReluDense(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[int]		   =     hidden_states + self.dropout(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			List[Any]							,					UpperCAmelCase      :			Union[str, Any]    ):
													super().__init__()
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     nn.Linear(UpperCAmelCase							,					UpperCAmelCase							,					bias=UpperCAmelCase    )
													__lowerCamelCase					:		str		   =     nn.Dropout(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     NewGELUActivation()







						def       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     self.act(self.wi_a(UpperCAmelCase    )    )
													__lowerCamelCase					:		Any		   =     self.wi_a(UpperCAmelCase    )
													__lowerCamelCase					:		Optional[Any]		   =     hidden_gelu * hidden_linear
													__lowerCamelCase					:		Any		   =     self.dropout(UpperCAmelCase    )

													__lowerCamelCase					:		List[Any]		   =     self.wo(UpperCAmelCase    )
													return hidden_states

class  _snake_case     (					nn.Module		):






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







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			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					:		int		   =     hidden_states.to(torch.floataa    ).pow(2    ).mean(-1							,					keepdim=UpperCAmelCase    )
													__lowerCamelCase					:		Dict		   =     hidden_states * torch.rsqrt(variance + self.variance_epsilon    )

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

													return self.weight * hidden_states

class  _snake_case     (					nn.Module		):






						def       lowerCamelCase__     (       self      :			str							,					UpperCAmelCase      :			torch.Tensor    ):
													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi    ) * (input + 0.0_4_4_7_1_5 * torch.pow(UpperCAmelCase							,					3.0    ))    ))

class  _snake_case     (					nn.Module		):






						def __init__(       self      :			Tuple							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[Any]    ):
													super().__init__()
													__lowerCamelCase					:		Any		   =     nn.Linear(UpperCAmelCase							,					out_features * 2							,					bias=UpperCAmelCase    )







						def       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Optional[Any]							,					UpperCAmelCase      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     self.scale_bias(UpperCAmelCase    )
													__lowerCamelCase   ,  __lowerCamelCase					:		Dict		   =     torch.chunk(UpperCAmelCase							,					2							,					-1    )
													__lowerCamelCase					:		List[Any]		   =     x * (1 + scale) + shift
													return x