






"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OpenAIGPTConfig,
						    OpenAIGPTDoubleHeadsModel,
						    OpenAIGPTForSequenceClassification,
						    OpenAIGPTLMHeadModel,
						    OpenAIGPTModel,
						)





class 					_UpperCAmelCase       :




					def __init__(				self     :						Optional[int]    ,						A     :						Any    ,						A     :						List[Any]=13    ,						A     :						Optional[int]=7    ,						A     :						Optional[Any]=True    ,						A     :						List[Any]=True    ,						A     :						List[str]=True    ,						A     :						List[str]=99    ,						A     :						Optional[int]=32    ,						A     :						int=5    ,						A     :						Tuple=4    ,						A     :						Optional[Any]=37    ,						A     :						List[str]="gelu"    ,						A     :						str=0.1    ,						A     :						Tuple=0.1    ,						A     :						Union[str, Any]=5_12    ,						A     :						List[str]=16    ,						A     :						Optional[Any]=2    ,						A     :						int=0.02    ,						A     :						Optional[int]=3    ,						A     :						Optional[Any]=4    ,						A     :						Optional[int]=None    ,						)  ->  Optional[Any]:
							lowercase_ : List[Any]							       =   parent
							lowercase_ : str							       =   batch_size
							lowercase_ : str							       =   seq_length
							lowercase_ : Dict							       =   is_training
							lowercase_ : Optional[int]							       =   use_token_type_ids
							lowercase_ : Optional[Any]							       =   use_labels
							lowercase_ : int							       =   vocab_size
							lowercase_ : List[Any]							       =   hidden_size
							lowercase_ : Any							       =   num_hidden_layers
							lowercase_ : List[Any]							       =   num_attention_heads
							lowercase_ : Any							       =   intermediate_size
							lowercase_ : List[Any]							       =   hidden_act
							lowercase_ : Tuple							       =   hidden_dropout_prob
							lowercase_ : Union[str, Any]							       =   attention_probs_dropout_prob
							lowercase_ : Dict							       =   max_position_embeddings
							lowercase_ : Any							       =   type_vocab_size
							lowercase_ : Dict							       =   type_sequence_label_size
							lowercase_ : Dict							       =   initializer_range
							lowercase_ : str							       =   num_labels
							lowercase_ : Union[str, Any]							       =   num_choices
							lowercase_ : Optional[int]							       =   scope
							lowercase_ : Tuple							       =   self.vocab_size - 1




					def   A  (				self     :						int   )  ->  List[Any]:
							lowercase_ : Optional[int]							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size   )

							lowercase_ : List[str]							       =   None
							if self.use_token_type_ids:
									lowercase_ : Dict							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.type_vocab_size   )

							lowercase_ : Tuple							       =   None
							lowercase_ : Union[str, Any]							       =   None
							lowercase_ : int							       =   None
							if self.use_labels:
									lowercase_ : List[str]							       =   ids_tensor([self.batch_size]    ,						self.type_sequence_label_size   )
									lowercase_ : Optional[int]							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels   )
									lowercase_ : Optional[int]							       =   ids_tensor([self.batch_size]    ,						self.num_choices   )

							lowercase_ : List[Any]							       =   OpenAIGPTConfig(
							    vocab_size=self.vocab_size    ,						n_embd=self.hidden_size    ,						n_layer=self.num_hidden_layers    ,						n_head=self.num_attention_heads    ,						n_positions=self.max_position_embeddings    ,						pad_token_id=self.pad_token_id    ,						)

							lowercase_ : Optional[Any]							       =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]    ,						2   )

							return (
							    config,
							    input_ids,
							    head_mask,
							    token_type_ids,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							)




					def   A  (				self     :						Optional[Any]    ,						A     :						Dict    ,						A     :						Optional[int]    ,						A     :						Union[str, Any]    ,						A     :						List[Any]    ,						*A     :						Dict   )  ->  str:
							lowercase_ : str							       =   OpenAIGPTModel(config=A   )
							model.to(A   )
							model.eval()

							lowercase_ : Tuple							       =   model(A    ,						token_type_ids=A    ,						head_mask=A   )
							lowercase_ : Dict							       =   model(A    ,						token_type_ids=A   )
							lowercase_ : Any							       =   model(A   )

							self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)   )




					def   A  (				self     :						Any    ,						A     :						Union[str, Any]    ,						A     :						Optional[Any]    ,						A     :						List[Any]    ,						A     :						Tuple    ,						*A     :						Optional[int]   )  ->  Dict:
							lowercase_ : List[str]							       =   OpenAIGPTLMHeadModel(A   )
							model.to(A   )
							model.eval()

							lowercase_ : Tuple							       =   model(A    ,						token_type_ids=A    ,						labels=A   )
							self.parent.assertEqual(result.loss.shape    ,						()   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)   )




					def   A  (				self     :						Any    ,						A     :						Any    ,						A     :						int    ,						A     :						List[str]    ,						A     :						List[str]    ,						*A     :						List[str]   )  ->  Union[str, Any]:
							lowercase_ : Optional[Any]							       =   OpenAIGPTDoubleHeadsModel(A   )
							model.to(A   )
							model.eval()

							lowercase_ : Optional[int]							       =   model(A    ,						token_type_ids=A    ,						labels=A   )
							self.parent.assertEqual(result.loss.shape    ,						()   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)   )




					def   A  (				self     :						int    ,						A     :						Any    ,						A     :						Optional[Any]    ,						A     :						Any    ,						A     :						int    ,						*A     :						int   )  ->  int:
							lowercase_ : Optional[Any]							       =   self.num_labels
							lowercase_ : Optional[Any]							       =   OpenAIGPTForSequenceClassification(A   )
							model.to(A   )
							model.eval()

							lowercase_ : List[Any]							       =   ids_tensor([self.batch_size]    ,						self.type_sequence_label_size   )
							lowercase_ : Dict							       =   model(A    ,						token_type_ids=A    ,						labels=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )






					def   A  (				self     :						List[str]   )  ->  Union[str, Any]:
							lowercase_ : Optional[int]							       =   self.prepare_config_and_inputs()
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : Dict							       =   config_and_inputs
							lowercase_ : Union[str, Any]							       =   {
							    '''input_ids''': input_ids,
							    '''token_type_ids''': token_type_ids,
							    '''head_mask''': head_mask,
							}

							return config, inputs_dict





@require_torch
class 					_UpperCAmelCase       ( _A   ,						_A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   (
					    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   (
					    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
					)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   (
					    {
					        "feature-extraction": OpenAIGPTModel,
					        "text-classification": OpenAIGPTForSequenceClassification,
					        "text-generation": OpenAIGPTLMHeadModel,
					        "zero-shot": OpenAIGPTForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)




					def   A  (				self     :						Optional[int]    ,						A     :						Dict    ,						A     :						List[Any]    ,						A     :						str    ,						A     :						Tuple    ,						A     :						Optional[Any]   )  ->  Optional[int]:
							if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
									# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
									# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
									# tiny config could not be created.
									return True

							return False




					def   A  (				self     :						Optional[Any]    ,						A     :						str    ,						A     :						List[Any]    ,						A     :						List[str]=False   )  ->  str:
							lowercase_ : List[str]							       =   super()._prepare_for_class(A    ,						A    ,						return_labels=A   )

							if return_labels:
									if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
											lowercase_ : Optional[int]							       =   torch.zeros(
											    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)    ,						dtype=torch.long    ,						device=A    ,						)
											lowercase_ : Dict							       =   inputs_dict['''labels''']
											lowercase_ : Optional[int]							       =   inputs_dict['''labels''']
											lowercase_ : Optional[Any]							       =   torch.zeros(
											    (self.model_tester.batch_size, self.model_tester.num_choices)    ,						dtype=torch.long    ,						device=A    ,						)
											lowercase_ : Optional[Any]							       =   torch.zeros(
											    self.model_tester.batch_size    ,						dtype=torch.long    ,						device=A   )
							return inputs_dict




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : Tuple							       =   OpenAIGPTModelTester(self   )
							lowercase_ : Dict							       =   ConfigTester(self    ,						config_class=A    ,						n_embd=37   )




					def   A  (				self     :						int   )  ->  Union[str, Any]:
							self.config_tester.run_common_tests()




					def   A  (				self     :						int   )  ->  Union[str, Any]:
							lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_model(*A   )




					def   A  (				self     :						int   )  ->  int:
							lowercase_ : Optional[int]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*A   )




					def   A  (				self     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_double_lm_head_model(*A   )




					def   A  (				self     :						str   )  ->  Optional[int]:
							lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*A   )






					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Optional[Any]							       =   OpenAIGPTModel.from_pretrained(A   )
									self.assertIsNotNone(A   )





@require_torch
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						List[str]   )  ->  List[str]:
							lowercase_ : Any							       =   OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''   )
							model.to(A   )
							lowercase_ : str							       =   torch.tensor([[4_81, 47_35, 5_44]]    ,						dtype=torch.long    ,						device=A   )  # the president is
							lowercase_ : Tuple							       =   [
							    4_81,
							    47_35,
							    5_44,
							    2_46,
							    9_63,
							    8_70,
							    7_62,
							    2_39,
							    2_44,
							    4_04_77,
							    2_44,
							    2_49,
							    7_19,
							    8_81,
							    4_87,
							    5_44,
							    2_40,
							    2_44,
							    6_03,
							    4_81,
							]  # the president is a very good man. " \n " i\'m sure he is, " said the

							lowercase_ : Optional[int]							       =   model.generate(A    ,						do_sample=A   )
							self.assertListEqual(output_ids[0].tolist()    ,						A   )











"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Any							    =   KandinskyVaaControlnetImgaImgPipeline
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   ["image_embeds", "negative_image_embeds", "image", "hint"]
					SCREAMING_SNAKE_CASE_		:				str							    =   ["image_embeds", "negative_image_embeds", "image", "hint"]
					SCREAMING_SNAKE_CASE_		:				Dict							    =   [
					    "generator",
					    "height",
					    "width",
					    "strength",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False




					@property
					def   A  (				self     :						Any   )  ->  Any:
							return 32




					@property
					def   A  (				self     :						Optional[int]   )  ->  Any:
							return 32




					@property
					def   A  (				self     :						Dict   )  ->  int:
							return self.time_input_dim




					@property
					def   A  (				self     :						Tuple   )  ->  str:
							return self.time_input_dim * 4




					@property
					def   A  (				self     :						Any   )  ->  str:
							return 1_00




					@property
					def   A  (				self     :						str   )  ->  List[str]:
							torch.manual_seed(0   )

							lowercase_ : List[Any]							       =   {
							    '''in_channels''': 8,
							    # Out channels is double in channels because predicts mean and variance
							    '''out_channels''': 8,
							    '''addition_embed_type''': '''image_hint''',
							    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
							    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
							    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
							    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
							    '''layers_per_block''': 1,
							    '''encoder_hid_dim''': self.text_embedder_hidden_size,
							    '''encoder_hid_dim_type''': '''image_proj''',
							    '''cross_attention_dim''': self.cross_attention_dim,
							    '''attention_head_dim''': 4,
							    '''resnet_time_scale_shift''': '''scale_shift''',
							    '''class_embed_type''': None,
							}

							lowercase_ : Dict							       =   UNetaDConditionModel(**A   )
							return model




					@property
					def   A  (				self     :						Optional[Any]   )  ->  Union[str, Any]:
							return {
							    "block_out_channels": [32, 32, 64, 64],
							    "down_block_types": [
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "AttnDownEncoderBlock2D",
							    ],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
							    "vq_embed_dim": 4,
							}




					@property
					def   A  (				self     :						List[Any]   )  ->  Dict:
							torch.manual_seed(0   )
							lowercase_ : int							       =   VQModel(**self.dummy_movq_kwargs   )
							return model




					def   A  (				self     :						Union[str, Any]   )  ->  Optional[int]:
							lowercase_ : Tuple							       =   self.dummy_unet
							lowercase_ : int							       =   self.dummy_movq

							lowercase_ : List[Any]							       =   {
							    '''num_train_timesteps''': 10_00,
							    '''beta_schedule''': '''linear''',
							    '''beta_start''': 0.00085,
							    '''beta_end''': 0.012,
							    '''clip_sample''': False,
							    '''set_alpha_to_one''': False,
							    '''steps_offset''': 0,
							    '''prediction_type''': '''epsilon''',
							    '''thresholding''': False,
							}

							lowercase_ : str							       =   DDIMScheduler(**A   )

							lowercase_ : Tuple							       =   {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}

							return components




					def   A  (				self     :						Optional[int]    ,						A     :						int    ,						A     :						List[str]=0   )  ->  int:
							lowercase_ : str							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(A   )   ).to(A   )
							lowercase_ : Tuple							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1   )   ).to(
							    A   )
							# create init_image
							lowercase_ : Union[str, Any]							       =   floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(A   )   ).to(A   )
							lowercase_ : Any							       =   image.cpu().permute(0    ,						2    ,						3    ,						1   )[0]
							lowercase_ : Optional[Any]							       =   Image.fromarray(np.uinta(A   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )
							# create hint
							lowercase_ : Optional[int]							       =   floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(A   )   ).to(A   )

							if str(A   ).startswith('''mps'''   ):
									lowercase_ : Optional[Any]							       =   torch.manual_seed(A   )
							else:
									lowercase_ : List[Any]							       =   torch.Generator(device=A   ).manual_seed(A   )
							lowercase_ : Dict							       =   {
							    '''image''': init_image,
							    '''image_embeds''': image_embeds,
							    '''negative_image_embeds''': negative_image_embeds,
							    '''hint''': hint,
							    '''generator''': generator,
							    '''height''': 64,
							    '''width''': 64,
							    '''num_inference_steps''': 10,
							    '''guidance_scale''': 7.0,
							    '''strength''': 0.2,
							    '''output_type''': '''np''',
							}
							return inputs






					def   A  (				self     :						Any   )  ->  List[Any]:
							lowercase_ : List[str]							       =   '''cpu'''

							lowercase_ : Any							       =   self.get_dummy_components()

							lowercase_ : Any							       =   self.pipeline_class(**A   )
							lowercase_ : int							       =   pipe.to(A   )

							pipe.set_progress_bar_config(disable=A   )

							lowercase_ : Dict							       =   pipe(**self.get_dummy_inputs(A   )   )
							lowercase_ : str							       =   output.images

							lowercase_ : int							       =   pipe(
							    **self.get_dummy_inputs(A   )    ,						return_dict=A    ,						)[0]

							lowercase_ : Dict							       =   image[0, -3:, -3:, -1]
							lowercase_ : Union[str, Any]							       =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowercase_ : List[str]							       =   np.array(
							    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]   )
							assert (
							    np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
							), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
							), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						Tuple   )  ->  str:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






					def   A  (				self     :						Any   )  ->  Optional[int]:
							lowercase_ : Dict							       =   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''   )

							lowercase_ : Dict							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
							lowercase_ : Optional[int]							       =   init_image.resize((5_12, 5_12)   )

							lowercase_ : Dict							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/hint_image_cat.png'''   )
							lowercase_ : Optional[int]							       =   torch.from_numpy(np.array(A   )   ).float() / 255.0
							lowercase_ : Tuple							       =   hint.permute(2    ,						0    ,						1   ).unsqueeze(0   )

							lowercase_ : Optional[Any]							       =   '''A robot, 4k photo'''

							lowercase_ : Tuple							       =   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa   )
							pipe_prior.to(A   )

							lowercase_ : Dict							       =   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''    ,						torch_dtype=torch.floataa   )
							lowercase_ : int							       =   pipeline.to(A   )

							pipeline.set_progress_bar_config(disable=A   )

							lowercase_ : Tuple							       =   torch.Generator(device='''cpu'''   ).manual_seed(0   )

							lowercase_       ,					lowercase_ : int							       =   pipe_prior(
							    A    ,						image=A    ,						strength=0.85    ,						generator=A    ,						negative_prompt=''''''    ,						).to_tuple()

							lowercase_ : str							       =   pipeline(
							    image=A    ,						image_embeds=A    ,						negative_image_embeds=A    ,						hint=A    ,						generator=A    ,						num_inference_steps=1_00    ,						height=5_12    ,						width=5_12    ,						strength=0.5    ,						output_type='''np'''    ,						)

							lowercase_ : Optional[Any]							       =   output.images[0]

							assert image.shape == (5_12, 5_12, 3)

							assert_mean_pixel_difference(A    ,						A   )



