
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class       _lowerCAmelCase   (      unittest.TestCase			):




				@property
				def 	__a		(   self	)							-> List[Any]:
							torch.manual_seed(0	)
							lowerCAmelCase_         =      UNetaDModel(
							    block_out_channels=(32, 64)							,							layers_per_block=2							,							sample_size=32							,							in_channels=3							,							out_channels=3							,							down_block_types=("DownBlock2D", "AttnDownBlock2D")							,							up_block_types=("AttnUpBlock2D", "UpBlock2D")							,							)
							return model




				@property
				def 	__a		(   self	)							-> Union[str, Any]:
							torch.manual_seed(0	)
							lowerCAmelCase_         =      VQModel(
							    block_out_channels=[32, 64]							,							in_channels=3							,							out_channels=3							,							down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]							,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]							,							latent_channels=3							,							)
							return model




				@property
				def 	__a		(   self	)							-> Union[str, Any]:
							torch.manual_seed(0	)
							lowerCAmelCase_         =      CLIPTextConfig(
							    bos_token_id=0							,							eos_token_id=2							,							hidden_size=32							,							intermediate_size=37							,							layer_norm_eps=1e-0_5							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=1_000							,							)
							return CLIPTextModel(_UpperCamelCase	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.dummy_uncond_unet
							lowerCAmelCase_         =      DDIMScheduler()
							lowerCAmelCase_         =      self.dummy_vq_model

							lowerCAmelCase_         =      LDMPipeline(unet=_UpperCamelCase							,							vqvae=_UpperCamelCase							,							scheduler=_UpperCamelCase	)
							ldm.to(_UpperCamelCase	)
							ldm.set_progress_bar_config(disable=_UpperCamelCase	)

							lowerCAmelCase_         =      torch.manual_seed(0	)
							lowerCAmelCase_         =      ldm(generator=_UpperCamelCase							,							num_inference_steps=2							,							output_type="numpy"	).images

							lowerCAmelCase_         =      torch.manual_seed(0	)
							lowerCAmelCase_         =      ldm(generator=_UpperCamelCase							,							num_inference_steps=2							,							output_type="numpy"							,							return_dict=_UpperCamelCase	)[0]

							lowerCAmelCase_         =      image[0, -3:, -3:, -1]
							lowerCAmelCase_         =      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							lowerCAmelCase_         =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
							lowerCAmelCase_         =      1e-2 if torch_device != "mps" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance





@slow
@require_torch
class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
							ldm.to(_UpperCamelCase	)
							ldm.set_progress_bar_config(disable=_UpperCamelCase	)

							lowerCAmelCase_         =      torch.manual_seed(0	)
							lowerCAmelCase_         =      ldm(generator=_UpperCamelCase							,							num_inference_steps=5							,							output_type="numpy"	).images

							lowerCAmelCase_         =      image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							lowerCAmelCase_         =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
							lowerCAmelCase_         =      1e-2 if torch_device != "mps" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance





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

_A  =	logging.get_logger(__name__)

_A  =	{
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''imagegpt'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(   self							,							_UpperCamelCase=512 + 1							,							_UpperCamelCase=32 * 32							,							_UpperCamelCase=512							,							_UpperCamelCase=24							,							_UpperCamelCase=8							,							_UpperCamelCase=None							,							_UpperCamelCase="quick_gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> Tuple:
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      n_positions
							lowerCAmelCase_         =      n_embd
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      n_inner
							lowerCAmelCase_         =      activation_function
							lowerCAmelCase_         =      resid_pdrop
							lowerCAmelCase_         =      embd_pdrop
							lowerCAmelCase_         =      attn_pdrop
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scale_attn_weights
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      scale_attn_by_inverse_layer_idx
							lowerCAmelCase_         =      reorder_and_upcast_attn
							lowerCAmelCase_         =      tie_word_embeddings

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





class       _lowerCAmelCase   (      __a			):




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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = 1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = 3							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      self._generate_dummy_images(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      dict(preprocessor(images=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)	)

							return inputs



