






'''simple docstring'''







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
		from ... import FeatureExtractionMixin, TensorType

__lowerCAmelCase       		=      logging.get_logger(__name__)

__lowerCAmelCase       		=      {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"imagegpt"
			snake_case__  =				["past_key_values"]
			snake_case__  =				{
			    "hidden_size": "n_embd",
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}






			def __init__( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			str=512 + 1  ,						__SCREAMING_SNAKE_CASE      :			int=32 * 32  ,						__SCREAMING_SNAKE_CASE      :			Dict=512  ,						__SCREAMING_SNAKE_CASE      :			List[str]=24  ,						__SCREAMING_SNAKE_CASE      :			Tuple=8  ,						__SCREAMING_SNAKE_CASE      :			Tuple=None  ,						__SCREAMING_SNAKE_CASE      :			str="quick_gelu"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=0.1  ,						__SCREAMING_SNAKE_CASE      :			List[str]=0.1  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=0.1  ,						__SCREAMING_SNAKE_CASE      :			int=1e-5  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=0.02  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=True  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=True  ,						__SCREAMING_SNAKE_CASE      :			str=False  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=False  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=False  ,						**__SCREAMING_SNAKE_CASE      :			Tuple  ,						)			->			Any:
							a_		:  str											=      vocab_size
							a_		:  Optional[int]											=      n_positions
							a_		:  Any											=      n_embd
							a_		:  Union[str, Any]											=      n_layer
							a_		:  List[Any]											=      n_head
							a_		:  str											=      n_inner
							a_		:  int											=      activation_function
							a_		:  Optional[int]											=      resid_pdrop
							a_		:  Dict											=      embd_pdrop
							a_		:  List[Any]											=      attn_pdrop
							a_		:  Dict											=      layer_norm_epsilon
							a_		:  List[str]											=      initializer_range
							a_		:  Optional[Any]											=      scale_attn_weights
							a_		:  str											=      use_cache
							a_		:  Dict											=      scale_attn_by_inverse_layer_idx
							a_		:  Optional[Any]											=      reorder_and_upcast_attn
							a_		:  str											=      tie_word_embeddings

							super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
							    ]					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			"FeatureExtractionMixin"  ,						__SCREAMING_SNAKE_CASE      :			int = 1  ,						__SCREAMING_SNAKE_CASE      :			int = -1  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			Optional["TensorType"] = None  ,						__SCREAMING_SNAKE_CASE      :			int = 3  ,						__SCREAMING_SNAKE_CASE      :			int = 32  ,						__SCREAMING_SNAKE_CASE      :			int = 32  ,						)			->			Mapping[str, Any]:
							a_		:  Tuple											=      self._generate_dummy_images(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      dict(preprocessor(images=__SCREAMING_SNAKE_CASE  ,						return_tensors=__SCREAMING_SNAKE_CASE					)					)

							return inputs












'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				IFInpaintingSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=0					)			->			List[Any]:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Optional[int]											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, Any]											=      {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''original_image''': original_image,
							    '''mask_image''': mask_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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda'''  ,						reason='''float16 requires CUDA'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							# 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 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[int]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Optional[Any]:
							self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[Any]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2  ,						)




