


import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format
class 						_UpperCamelCase						:
							'''simple docstring'''







							__UpperCAmelCase		:					List[str]          =None

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)
										__lowerCAmelCase       					=    json.loads(feat_extract.to_json_string()			)
										for key, value in self.feat_extract_dict.items():
													self.assertEqual(obj[key]					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase       					=    os.path.join(__a					,     "feat_extract.json"			)
													feat_extract_first.to_json_file(__a			)
													__lowerCAmelCase       					=    self.feature_extraction_class.from_json_file(__a			)

										self.assertEqual(feat_extract_second.to_dict()					,     feat_extract_first.to_dict()			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase       					=    feat_extract_first.save_pretrained(__a			)[0]
													check_json_file_has_correct_format(__a			)
													__lowerCAmelCase       					=    self.feature_extraction_class.from_pretrained(__a			)

										self.assertEqual(feat_extract_second.to_dict()					,     feat_extract_first.to_dict()			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.feature_extraction_class()
										self.assertIsNotNone(__a			)








"""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




