



'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				snake_case (__UpperCAmelCase		,      __UpperCAmelCase		,      unittest.TestCase   ):
							lowerCAmelCase__  :Any						   =					IFImgaImgSuperResolutionPipeline
							lowerCAmelCase__  :str						   =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
							lowerCAmelCase__  :Tuple						   =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}   )
							lowerCAmelCase__  :Any						   =					PipelineTesterMixin.required_optional_params - {"""latents"""}



							def 							_a      (				self      )  ->							str:
											return self._get_superresolution_dummy_components()



							def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=0      )  ->							List[Any]:
											if str(UpperCAmelCase_      ).startswith("mps"      ):
															lowercase__		       =						torch.manual_seed(UpperCAmelCase_      )
											else:
															lowercase__		       =						torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )

											lowercase__		       =						floats_tensor((1, 3, 32, 32)     ,rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )
											lowercase__		       =						floats_tensor((1, 3, 16, 16)     ,rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )

											lowercase__		       =						{
											    "prompt": "A painting of a squirrel eating a burger",
											    "image": image,
											    "original_image": original_image,
											    "generator": generator,
											    "num_inference_steps": 2,
											    "output_type": "numpy",
											}

											return inputs



							@unittest.skipIf(
							    torch_device != "cuda" or not is_xformers_available()     ,reason="XFormers attention is only available with CUDA and `xformers` installed"     ,)
							def 							_a      (				self      )  ->							Tuple:
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )



							def 							_a      (				self      )  ->							Tuple:
											self._test_save_load_optional_components()



							@unittest.skipIf(torch_device != "cuda"     ,reason="float16 requires CUDA"      )
							def 							_a      (				self      )  ->							str:
											# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
											super().test_save_load_floataa(expected_max_diff=1E-1      )



							def 							_a      (				self      )  ->							Optional[int]:
											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )



							def 							_a      (				self      )  ->							Any:
											self._test_save_load_local()







							def 							_a      (				self      )  ->							Tuple:
											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2     ,)









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls     ,"model_type"      ) and config_dict["model_type"] != cls.model_type:
               logger.warning(
                   F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                   F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''      )

           return cls.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




