"""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 			UpperCamelCase__(	__A						,  __A       ):





						@register_to_config
						def __init__(				self						,__UpperCAmelCase = 1_28						,__UpperCAmelCase = 2_56						,__UpperCAmelCase = 2_0_0_0.0						,__UpperCAmelCase = 7_68						,__UpperCAmelCase = 12						,__UpperCAmelCase = 12						,__UpperCAmelCase = 64						,__UpperCAmelCase = 20_48						,__UpperCAmelCase = 0.1						,)    -> Optional[Any]:
													super().__init__()

													A__		   =							nn.Sequential(
													    nn.Linear(__UpperCAmelCase						,d_model * 4						,bias=__UpperCAmelCase   )						,nn.SiLU()						,nn.Linear(d_model * 4						,d_model * 4						,bias=__UpperCAmelCase   )						,nn.SiLU()						,)

													A__		   =							nn.Embedding(__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							False

													A__		   =							nn.Linear(__UpperCAmelCase						,__UpperCAmelCase						,bias=__UpperCAmelCase   )

													A__		   =							nn.Dropout(p=__UpperCAmelCase   )

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

													A__		   =							TaLayerNorm(__UpperCAmelCase   )

													A__		   =							nn.Dropout(p=__UpperCAmelCase   )
													A__		   =							nn.Linear(__UpperCAmelCase						,__UpperCAmelCase						,bias=__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Any:
													A__		   =							torch.mul(query_input.unsqueeze(-1   )						,key_input.unsqueeze(-2   )   )
													return mask.unsqueeze(-3   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[Any]:
													A__   ,     A__   ,     A__		   =							decoder_input_tokens.shape
													assert decoder_noise_time.shape == (batch,)

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

													A__		   =							self.conditioning_emb(__UpperCAmelCase   ).unsqueeze(1   )

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

													A__		   =							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.
													A__		   =							torch.broadcast_to(
													    torch.arange(__UpperCAmelCase						,device=decoder_input_tokens.device   )						,(batch, seq_length)						,)

													A__		   =							self.position_encoding(__UpperCAmelCase   )

													A__		   =							self.continuous_inputs_projection(__UpperCAmelCase   )
													inputs += position_encodings
													A__		   =							self.dropout(__UpperCAmelCase   )

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

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

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

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

													A__		   =							self.decoder_norm(__UpperCAmelCase   )
													A__		   =							self.post_dropout(__UpperCAmelCase   )

													A__		   =							self.spec_out(__UpperCAmelCase   )
													return spec_out






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=1e-6   )    -> Optional[Any]:
													super().__init__()
													A__		   =							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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,)    -> Any:
													A__		   =							self.layer[0](
													    __UpperCAmelCase						,conditioning_emb=__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,)

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

																				A__		   =							self.layer[1](
																				    __UpperCAmelCase						,key_value_states=__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,)

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

													return (hidden_states,)






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													super().__init__()
													A__		   =							TaLayerNorm(__UpperCAmelCase   )
													A__		   =							TaFiLMLayer(in_features=d_model * 4						,out_features=__UpperCAmelCase   )
													A__		   =							Attention(query_dim=__UpperCAmelCase						,heads=__UpperCAmelCase						,dim_head=__UpperCAmelCase						,out_bias=__UpperCAmelCase						,scale_qk=__UpperCAmelCase   )
													A__		   =							nn.Dropout(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,)    -> Optional[int]:
													# pre_self_attention_layer_norm
													A__		   =							self.layer_norm(__UpperCAmelCase   )

													if conditioning_emb is not None:
																				A__		   =							self.FiLMLayer(__UpperCAmelCase						,__UpperCAmelCase   )

													# Self-attention block
													A__		   =							self.attention(__UpperCAmelCase   )

													A__		   =							hidden_states + self.dropout(__UpperCAmelCase   )

													return hidden_states






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													super().__init__()
													A__		   =							Attention(query_dim=__UpperCAmelCase						,heads=__UpperCAmelCase						,dim_head=__UpperCAmelCase						,out_bias=__UpperCAmelCase						,scale_qk=__UpperCAmelCase   )
													A__		   =							TaLayerNorm(__UpperCAmelCase						,eps=__UpperCAmelCase   )
													A__		   =							nn.Dropout(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase=None						,)    -> int:
													A__		   =							self.layer_norm(__UpperCAmelCase   )
													A__		   =							self.attention(
													    __UpperCAmelCase						,encoder_hidden_states=__UpperCAmelCase						,attention_mask=attention_mask.squeeze(1   )						,)
													A__		   =							hidden_states + self.dropout(__UpperCAmelCase   )
													return layer_output






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> List[str]:
													super().__init__()
													A__		   =							TaDenseGatedActDense(d_model=__UpperCAmelCase						,d_ff=__UpperCAmelCase						,dropout_rate=__UpperCAmelCase   )
													A__		   =							TaFiLMLayer(in_features=d_model * 4						,out_features=__UpperCAmelCase   )
													A__		   =							TaLayerNorm(__UpperCAmelCase						,eps=__UpperCAmelCase   )
													A__		   =							nn.Dropout(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None   )    -> Any:
													A__		   =							self.layer_norm(__UpperCAmelCase   )
													if conditioning_emb is not None:
																				A__		   =							self.film(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							self.DenseReluDense(__UpperCAmelCase   )
													A__		   =							hidden_states + self.dropout(__UpperCAmelCase   )
													return hidden_states






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													super().__init__()
													A__		   =							nn.Linear(__UpperCAmelCase						,__UpperCAmelCase						,bias=__UpperCAmelCase   )
													A__		   =							nn.Linear(__UpperCAmelCase						,__UpperCAmelCase						,bias=__UpperCAmelCase   )
													A__		   =							nn.Linear(__UpperCAmelCase						,__UpperCAmelCase						,bias=__UpperCAmelCase   )
													A__		   =							nn.Dropout(__UpperCAmelCase   )
													A__		   =							NewGELUActivation()





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													A__		   =							self.act(self.wi_a(__UpperCAmelCase   )   )
													A__		   =							self.wi_a(__UpperCAmelCase   )
													A__		   =							hidden_gelu * hidden_linear
													A__		   =							self.dropout(__UpperCAmelCase   )

													A__		   =							self.wo(__UpperCAmelCase   )
													return hidden_states






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=1e-6   )    -> Any:
													super().__init__()
													A__		   =							nn.Parameter(torch.ones(__UpperCAmelCase   )   )
													A__		   =							eps





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[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

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

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

													return self.weight * hidden_states






class 			UpperCamelCase__(	nn.Module       ):





						def  snake_case__		(				self						,__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 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[int]:
													super().__init__()
													A__		   =							nn.Linear(__UpperCAmelCase						,out_features * 2						,bias=__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							self.scale_bias(__UpperCAmelCase   )
													A__   ,     A__		   =							torch.chunk(__UpperCAmelCase						,2						,-1   )
													A__		   =							x * (1 + scale) + shift
													return x






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							b.T
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=1 )
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=0 )
							A__		   =							np.matmul(UpperCamelCase__				,			UpperCamelCase__ )
							A__		   =							aa[:, None] - 2 * ab + ba[None, :]
							return d






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							x.reshape(-1				,			3 )
							A__		   =							squared_euclidean_distance(UpperCamelCase__				,			UpperCamelCase__ )
							return np.argmin(UpperCamelCase__				,			axis=1 )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Tuple  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'height': 2_56, 'width': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							np.array(__UpperCAmelCase   ) if clusters is not None else None
													A__		   =							do_resize
													A__		   =							size
													A__		   =							resample
													A__		   =							do_normalize
													A__		   =							do_color_quantize





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''   )
													return resize(
													    __UpperCAmelCase						,size=(size['height'], size['width'])						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> np.ndarray:
													A__		   =							rescale(image=__UpperCAmelCase						,scale=1 / 1_2_7.5						,data_format=__UpperCAmelCase   )
													A__		   =							image - 1
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
													A__		   =							clusters if clusters is not None else self.clusters
													A__		   =							np.array(__UpperCAmelCase   )

													A__		   =							make_list_of_images(__UpperCAmelCase   )

													if not valid_images(__UpperCAmelCase   ):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

													if do_resize and size is None or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_color_quantize and clusters is None:
																				raise ValueError('Clusters must be specified if do_color_quantize is True.'   )

													# All transformations expect numpy arrays.
													A__		   =							[to_numpy_array(__UpperCAmelCase   ) for image in images]

													if do_resize:
																				A__		   =							[self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   ) for image in images]

													if do_normalize:
																				A__		   =							[self.normalize(image=__UpperCAmelCase   ) for image in images]

													if do_color_quantize:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,ChannelDimension.LAST   ) for image in images]
																				# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																				A__		   =							np.array(__UpperCAmelCase   )
																				A__		   =							color_quantize(__UpperCAmelCase						,__UpperCAmelCase   ).reshape(images.shape[:-1]   )

																				# flatten to (batch_size, height*width)
																				A__		   =							images.shape[0]
																				A__		   =							images.reshape(__UpperCAmelCase						,-1   )

																				# We need to convert back to a list of images to keep consistent behaviour across processors.
																				A__		   =							list(__UpperCAmelCase   )
													else:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   ) for image in images]

													A__		   =							{'input_ids': images}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





