


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

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


_snake_case             =     logging.get_logger(__name__)  # pylint: disable=invalid-name
class 	UpperCAmelCase_					(					_UpperCAmelCase    ):

   '''simple docstring'''




   def __init__(		self						,				__A						,				__A=768 ):






     """simple docstring"""





     super().__init__(lowerCAmelCase__ )
     lowerCamelCase :					Tuple    =				proj_size

     lowerCamelCase :					Dict    =				CLIPVisionModel(lowerCAmelCase__ )
     lowerCamelCase :					Any    =				PaintByExampleMapper(lowerCAmelCase__ )
     lowerCamelCase :					Optional[int]    =				nn.LayerNorm(config.hidden_size )
     lowerCamelCase :					List[str]    =				nn.Linear(config.hidden_size						,				self.proj_size )

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






   def _snake_case    (		self						,				__A						,				__A=False ):






     """simple docstring"""





     lowerCamelCase :					Tuple    =				self.model(pixel_values=lowerCAmelCase__ )
     lowerCamelCase :					Any    =				clip_output.pooler_output
     lowerCamelCase :					Union[str, Any]    =				self.mapper(latent_states[:, None] )
     lowerCamelCase :					Optional[Any]    =				self.final_layer_norm(lowerCAmelCase__ )
     lowerCamelCase :					str    =				self.proj_out(lowerCAmelCase__ )
     if return_uncond_vector:
       return latent_states, self.uncond_vector

     return latent_states



class 	UpperCAmelCase_					(					nn.Module    ):

   '''simple docstring'''




   def __init__(		self						,				__A ):






     """simple docstring"""





     super().__init__()
     lowerCamelCase :					List[Any]    =				(config.num_hidden_layers + 1) // 5
     lowerCamelCase :					Optional[Any]    =				config.hidden_size
     lowerCamelCase :					int    =				1
     lowerCamelCase :					Optional[Any]    =				nn.ModuleList(
         [
             BasicTransformerBlock(lowerCAmelCase__						,				lowerCAmelCase__						,				lowerCAmelCase__						,				activation_fn="gelu"						,				attention_bias=lowerCAmelCase__ )
             for _ in range(lowerCAmelCase__ )
         ] )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     for block in self.blocks:
       lowerCamelCase :					str    =				block(lowerCAmelCase__ )

     return hidden_states




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		__a  :     Dict       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)