


"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A  :     int    					=    logging.getLogger(__name__)
@dataclass
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[float]          =field(
							    default=0.0 ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
							__UpperCAmelCase		:					bool          =field(default=lowerCAmelCase__ ,metadata={"""help""": """Whether to SortishSamler or not."""}	)
							__UpperCAmelCase		:					bool          =field(
							    default=lowerCAmelCase__ ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
							__UpperCAmelCase		:					bool          =field(default=lowerCAmelCase__ ,metadata={"""help""": """whether to use adafactor"""}	)
							__UpperCAmelCase		:					Optional[float]          =field(
							    default=lowerCAmelCase__ ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
							__UpperCAmelCase		:					Optional[float]          =field(
							    default=lowerCAmelCase__ ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
							__UpperCAmelCase		:					Optional[float]          =field(default=lowerCAmelCase__ ,metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
							__UpperCAmelCase		:					Optional[float]          =field(
							    default=lowerCAmelCase__ ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
							__UpperCAmelCase		:					Optional[str]          =field(
							    default="""linear""" ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''} ,)








"""simple docstring"""


import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)  # pylint: disable=invalid-name
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7_68			):
										super().__init__(__a			)
										__lowerCAmelCase       					=    proj_size

										__lowerCAmelCase       					=    CLIPVisionModel(__a			)
										__lowerCAmelCase       					=    PaintByExampleMapper(__a			)
										__lowerCAmelCase       					=    nn.LayerNorm(config.hidden_size			)
										__lowerCAmelCase       					=    nn.Linear(config.hidden_size					,     self.proj_size			)

										# uncondition for scaling
										__lowerCAmelCase       					=    nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)



							def     snake_case       (  self					,     __a					,     __a=False			):
										__lowerCAmelCase       					=    self.model(pixel_values=__a			)
										__lowerCAmelCase       					=    clip_output.pooler_output
										__lowerCAmelCase       					=    self.mapper(latent_states[:, None]			)
										__lowerCAmelCase       					=    self.final_layer_norm(__a			)
										__lowerCAmelCase       					=    self.proj_out(__a			)
										if return_uncond_vector:
													return latent_states, self.uncond_vector

										return latent_states
class 						_UpperCamelCase						(     nn.Module	):
							'''simple docstring'''







							def __init__(  self					,     __a			):
										super().__init__()
										__lowerCAmelCase       					=    (config.num_hidden_layers + 1) // 5
										__lowerCAmelCase       					=    config.hidden_size
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    nn.ModuleList(
										    [
										        BasicTransformerBlock(__a					,     __a					,     __a					,     activation_fn="gelu"					,     attention_bias=__a			)
										        for _ in range(__a			)
										    ]			)



							def     snake_case       (  self					,     __a			):
										for block in self.blocks:
													__lowerCAmelCase       					=    block(__a			)

										return hidden_states




