



import gc
import unittest

from transformers import CTRLConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)







class       __lowerCAmelCase   :



			def __init__(						self       :  int			,		A       :  str			,		A       :  Any=14			,		A       :  Any=7			,		A       :  int=True			,		A       :  Tuple=True			,		A       :  Optional[int]=True			,		A       :  Optional[Any]=True			,		A       :  Optional[int]=True			,		A       :  str=99			,		A       :  List[Any]=32			,		A       :  Dict=5			,		A       :  List[str]=4			,		A       :  Optional[Any]=37			,		A       :  Optional[int]="gelu"			,		A       :  Union[str, Any]=0.1			,		A       :  str=0.1			,		A       :  Optional[int]=5_12			,		A       :  Any=16			,		A       :  List[Any]=2			,		A       :  Union[str, Any]=0.0_2			,		A       :  List[str]=3			,		A       :  Any=4			,		A       :  Optional[int]=None			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_token_type_ids
						_UpperCAmelCase			=       use_input_mask
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       use_mc_token_ids
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       type_sequence_label_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       num_choices
						_UpperCAmelCase			=       scope
						_UpperCAmelCase			=       self.vocab_size - 1



			def    _lowerCamelCase	(						self       :  int)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

						_UpperCAmelCase			=       None
						if self.use_input_mask:
									_UpperCAmelCase			=       random_attention_mask([self.batch_size, self.seq_length])

						_UpperCAmelCase			=       None
						if self.use_token_type_ids:
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.type_vocab_size)

						_UpperCAmelCase			=       None
						if self.use_mc_token_ids:
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.num_choices]			,		self.seq_length)

						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None
						if self.use_labels:
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.type_sequence_label_size)
									_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.num_labels)
									_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.num_choices)

						_UpperCAmelCase			=       self.get_config()

						_UpperCAmelCase			=       ids_tensor([self.num_hidden_layers, self.num_attention_heads]			,		2)

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



			def    _lowerCamelCase	(						self       :  str)       ->   str:



						"""simple docstring"""






						return CTRLConfig(
						    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			,		)



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Optional[int]			,		A       :  Dict			,		A       :  Tuple			,		A       :  Optional[Any]			,		A       :  Optional[int]			,		*A       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       CTRLModel(config=A)
						model.to(A)
						model.eval()

						model(A			,		token_type_ids=A			,		head_mask=A)
						model(A			,		token_type_ids=A)
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.last_hidden_state.shape			,		(self.batch_size, self.seq_length, self.hidden_size))
						self.parent.assertEqual(len(result.past_key_values)			,		config.n_layer)



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  str			,		A       :  Optional[Any]			,		A       :  int			,		A       :  Optional[int]			,		A       :  List[str]			,		*A       :  Tuple)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       CTRLLMHeadModel(A)
						model.to(A)
						model.eval()

						_UpperCAmelCase			=       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    _lowerCamelCase	(						self       :  List[str])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()

						(
						    (
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					
						)			=       config_and_inputs

						_UpperCAmelCase			=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'head_mask': head_mask}

						return config, inputs_dict



			def    _lowerCamelCase	(						self       :  Dict			,		A       :  Optional[int]			,		A       :  int			,		A       :  Optional[int]			,		A       :  Union[str, Any]			,		*A       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       CTRLForSequenceClassification(A)
						model.to(A)
						model.eval()
						_UpperCAmelCase			=       ids_tensor([self.batch_size]			,		self.type_sequence_label_size)
						_UpperCAmelCase			=       model(A			,		token_type_ids=A			,		labels=A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))







@require_torch
class       __lowerCAmelCase   (      A    ,						A    ,						A    ,						unittest.TestCase						):
			UpperCamelCase        =					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
			UpperCamelCase        =					(CTRLLMHeadModel,) if is_torch_available() else ()
			UpperCamelCase        =					(
			    {
			        '''feature-extraction''': CTRLModel,
			        '''text-classification''': CTRLForSequenceClassification,
			        '''text-generation''': CTRLLMHeadModel,
			        '''zero-shot''': CTRLForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase        =					True
			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  str			,		A       :  Dict			,		A       :  Tuple			,		A       :  List[str]			,		A       :  str			,		A       :  Optional[Any])       ->   Dict:



						"""simple docstring"""






						if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
									# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
									# `CTRLConfig` 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    _lowerCamelCase	(						self       :  Dict)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       CTRLModelTester(self)
						_UpperCAmelCase			=       ConfigTester(self			,		config_class=A			,		n_embd=37)



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   str:



						"""simple docstring"""






						super().tearDown()
						# clean-up as much as possible GPU memory occupied by PyTorch
						gc.collect()
						torch.cuda.empty_cache()



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   str:



						"""simple docstring"""






						self.config_tester.run_common_tests()



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_ctrl_model(*A)



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head_model(*A)



			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Optional[int]:



						"""simple docstring"""






						pass



			@slow
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_UpperCAmelCase			=       CTRLModel.from_pretrained(A)
									self.assertIsNotNone(A)



			@unittest.skip('The model doesn\'t support left padding')  # and it's not used enough to be worth fixing :)
			def    _lowerCamelCase	(						self       :  List[str])       ->   str:



						"""simple docstring"""






						pass







@require_torch
class       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  List[Any])       ->   str:



						"""simple docstring"""






						super().tearDown()
						# clean-up as much as possible GPU memory occupied by PyTorch
						gc.collect()
						torch.cuda.empty_cache()



			@slow
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       CTRLLMHeadModel.from_pretrained('ctrl')
						model.to(A)
						_UpperCAmelCase			=       torch.tensor(
						    [[1_18_59, 0, 16_11, 8]]			,		dtype=torch.long			,		device=A)  # Legal the president is
						_UpperCAmelCase			=       [
						    1_18_59,
						    0,
						    16_11,
						    8,
						    5,
						    1_50,
						    2_64_49,
						    2,
						    19,
						    3_48,
						    4_69,
						    3,
						    25_95,
						    48,
						    2_07_40,
						    24_65_33,
						    24_65_33,
						    19,
						    30,
						    5,
						]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

						_UpperCAmelCase			=       model.generate(A			,		do_sample=A)
						self.assertListEqual(output_ids[0].tolist()			,		A)





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					KandinskyVaaPipeline
			UpperCamelCase        =					[
			    '''image_embeds''',
			    '''negative_image_embeds''',
			]
			UpperCamelCase        =					['''image_embeds''', '''negative_image_embeds''']
			UpperCamelCase        =					[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase        =					False



			@property
			def    _lowerCamelCase	(						self       :  Tuple)       ->   List[str]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Union[str, Any]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  Any)       ->   List[Any]:



						"""simple docstring"""






						return self.time_input_dim



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						return 1_00



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   Dict:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'in_channels': 4,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 8,
						    'addition_embed_type': 'image',
						    '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,
						}

						_UpperCAmelCase			=       UNetaDConditionModel(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   List[Any]:



						"""simple docstring"""






						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["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",
						    ],
						    "vq_embed_dim": 4,
						}



			@property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Optional[Any]:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       VQModel(**self.dummy_movq_kwargs)
						return model



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_unet
						_UpperCAmelCase			=       self.dummy_movq

						_UpperCAmelCase			=       DDIMScheduler(
						    num_train_timesteps=10_00			,		beta_schedule='linear'			,		beta_start=0.0_0_0_8_5			,		beta_end=0.0_1_2			,		clip_sample=A			,		set_alpha_to_one=A			,		steps_offset=1			,		prediction_type='epsilon'			,		thresholding=A			,		)

						_UpperCAmelCase			=       {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}
						return components



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  str			,		A       :  Tuple=0)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, self.text_embedder_hidden_size)			,		rng=random.Random(A)).to(A)
						_UpperCAmelCase			=       floats_tensor((1, self.text_embedder_hidden_size)			,		rng=random.Random(seed + 1)).to(
						    A)
						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)
						_UpperCAmelCase			=       {
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'guidance_scale': 4.0,
						    'num_inference_steps': 2,
						    'output_type': 'np',
						}
						return inputs



			def    _lowerCamelCase	(						self       :  str)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       pipe(**self.get_dummy_inputs(A))
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       pipe(
						    **self.get_dummy_inputs(A)			,		return_dict=A			,		)[0]

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

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

						_UpperCAmelCase			=       np.array(
						    [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6])

						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       __lowerCAmelCase   (      unittest.TestCase						):



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy')

						_UpperCAmelCase			=       KandinskyVaaPriorPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-prior'			,		torch_dtype=torch.floataa)
						pipe_prior.to(A)

						_UpperCAmelCase			=       KandinskyVaaPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-decoder'			,		torch_dtype=torch.floataa)
						_UpperCAmelCase			=       pipeline.to(A)
						pipeline.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       'red cat, 4k photo'

						_UpperCAmelCase			=       torch.Generator(device='cuda').manual_seed(0)
						_UpperCAmelCase	,					_UpperCAmelCase			=       pipe_prior(
						    A			,		generator=A			,		num_inference_steps=5			,		negative_prompt=''			,		).to_tuple()

						_UpperCAmelCase			=       torch.Generator(device='cuda').manual_seed(0)
						_UpperCAmelCase			=       pipeline(
						    image_embeds=A			,		negative_image_embeds=A			,		generator=A			,		num_inference_steps=1_00			,		output_type='np'			,		)

						_UpperCAmelCase			=       output.images[0]

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

						assert_mean_pixel_difference(A			,		A)
