





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

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


_SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)  # pylint: disable=invalid-name
class SCREAMING_SNAKE_CASE_  (    _UpperCamelCase    ):






						def __init__(					self					:					List[Any]						,      _A					:					Tuple						,      _A					:					List[str]=768		)							->			Any:







							"""simple docstring"""


							super().__init__(__lowerCamelCase		)
							snake_case_						:	Optional[int]      =		proj_size

							snake_case_						:	int      =		CLIPVisionModel(__lowerCamelCase		)
							snake_case_						:	Optional[int]      =		PaintByExampleMapper(__lowerCamelCase		)
							snake_case_						:	int      =		nn.LayerNorm(config.hidden_size		)
							snake_case_						:	int      =		nn.Linear(config.hidden_size						,      self.proj_size		)

							# uncondition for scaling
							snake_case_						:	Optional[Any]      =		nn.Parameter(torch.randn((1, 1, self.proj_size)		)		)






						def UpperCAmelCase_ (					self					:					Union[str, Any]						,      _A					:					Tuple						,      _A					:					List[str]=False		)							->			Tuple:







							"""simple docstring"""


							snake_case_						:	Tuple      =		self.model(pixel_values=__lowerCamelCase		)
							snake_case_						:	int      =		clip_output.pooler_output
							snake_case_						:	Dict      =		self.mapper(latent_states[:, None]		)
							snake_case_						:	Dict      =		self.final_layer_norm(__lowerCamelCase		)
							snake_case_						:	Optional[Any]      =		self.proj_out(__lowerCamelCase		)
							if return_uncond_vector:
								return latent_states, self.uncond_vector

							return latent_states
class SCREAMING_SNAKE_CASE_  (    nn.Module    ):






						def __init__(					self					:					Dict						,      _A					:					List[Any]		)							->			Any:







							"""simple docstring"""


							super().__init__()
							snake_case_						:	List[Any]      =		(config.num_hidden_layers + 1) // 5
							snake_case_						:	Any      =		config.hidden_size
							snake_case_						:	Optional[Any]      =		1
							snake_case_						:	List[str]      =		nn.ModuleList(
							    [
							        BasicTransformerBlock(__lowerCamelCase						,      __lowerCamelCase						,      __lowerCamelCase						,      activation_fn='gelu'						,      attention_bias=__lowerCamelCase		)
							        for _ in range(__lowerCamelCase		)
							    ]		)






						def UpperCAmelCase_ (					self					:					Tuple						,      _A					:					int		)							->			Any:







							"""simple docstring"""


							for block in self.blocks:
								snake_case_						:	Tuple      =		block(__lowerCamelCase		)

							return hidden_states







from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={"""help""": """The output directory where the model will be written."""}    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The encoder model checkpoint for weights initialization."""
				            """Don't set if you want to train an encoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The decoder model checkpoint for weights initialization."""
				            """Don't set if you want to train a decoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			HfArgumentParser((ModelArguments,)   )
			((a)    ,		)    =			parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

			# Use explicit specified encoder config
			if model_args.encoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.encoder_config_name   )
			# Use pretrained encoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

			# Use explicit specified decoder config
			if model_args.decoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.decoder_config_name   )
			# Use pretrained decoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

			# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
			a    =			True
			a    =			True

			a    =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
			    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,  decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,  encoder_config=A,  decoder_config=A,  )

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			a    =			decoder_config.decoder_start_token_id
			a    =			decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a    =			decoder_config.bos_token_id
			if pad_token_id is None:
						a    =			decoder_config.eos_token_id

			# This is necessary to make Flax's generate() work
			a    =			decoder_config.eos_token_id
			a    =			decoder_start_token_id
			a    =			pad_token_id

			a    =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

			a    =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path   )
			a    =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id   )

			model.save_pretrained(model_args.output_dir   )
			image_processor.save_pretrained(model_args.output_dir   )
			tokenizer.save_pretrained(model_args.output_dir   )


if __name__ == "__main__":
		main()






