





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class     lowerCamelCase(	__snake_case ,   unittest.TestCase  ):
					'''simple docstring'''


					__magic_name__        = UnCLIPPipeline
					__magic_name__        = TEXT_TO_IMAGE_PARAMS - {
					    'negative_prompt',
					    'height',
					    'width',
					    'negative_prompt_embeds',
					    'guidance_scale',
					    'prompt_embeds',
					    'cross_attention_kwargs',
					}
					__magic_name__        = TEXT_TO_IMAGE_BATCH_PARAMS
					__magic_name__        = [
					    'generator',
					    'return_dict',
					    'prior_num_inference_steps',
					    'decoder_num_inference_steps',
					    'super_res_num_inference_steps',
					]
					__magic_name__        = False




					@property
					def 					lowerCAmelCase__		(						self       ):
									return 32




					@property
					def 					lowerCAmelCase__		(						self       ):
									return 32




					@property
					def 					lowerCAmelCase__		(						self       ):
									return self.time_input_dim




					@property
					def 					lowerCAmelCase__		(						self       ):
									return self.time_input_dim * 4




					@property
					def 					lowerCAmelCase__		(						self       ):
									return 100




					@property
					def 					lowerCAmelCase__		(						self       ):
									_A    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
									return tokenizer




					@property
					def 					lowerCAmelCase__		(						self       ):
									torch.manual_seed(0       )
									_A    =       CLIPTextConfig(
									    bos_token_id=0							,    eos_token_id=2							,    hidden_size=self.text_embedder_hidden_size							,    projection_dim=self.text_embedder_hidden_size							,    intermediate_size=37							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1000							,    )
									return CLIPTextModelWithProjection(snake_case_       )




					@property
					def 					lowerCAmelCase__		(						self       ):
									torch.manual_seed(0       )

									_A    =       {
									    'num_attention_heads': 2,
									    'attention_head_dim': 12,
									    'embedding_dim': self.text_embedder_hidden_size,
									    'num_layers': 1,
									}

									_A    =       PriorTransformer(**snake_case_       )
									return model




					@property
					def 					lowerCAmelCase__		(						self       ):
									torch.manual_seed(0       )

									_A    =       {
									    'clip_embeddings_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'cross_attention_dim': self.cross_attention_dim,
									}

									_A    =       UnCLIPTextProjModel(**snake_case_       )
									return model




					@property
					def 					lowerCAmelCase__		(						self       ):
									torch.manual_seed(0       )

									_A    =       {
									    'sample_size': 32,
									    # RGB in channels
									    'in_channels': 3,
									    # Out channels is double in channels because predicts mean and variance
									    'out_channels': 6,
									    '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,
									    'cross_attention_dim': self.cross_attention_dim,
									    'attention_head_dim': 4,
									    'resnet_time_scale_shift': 'scale_shift',
									    'class_embed_type': 'identity',
									}

									_A    =       UNetaDConditionModel(**snake_case_       )
									return model




					@property
					def 					lowerCAmelCase__		(						self       ):
									return {
									    "sample_size": 64,
									    "layers_per_block": 1,
									    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
									    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
									    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    "in_channels": 6,
									    "out_channels": 3,
									}




					@property
					def 					lowerCAmelCase__		(						self       ):
									torch.manual_seed(0       )

									_A    =       UNetaDModel(**self.dummy_super_res_kwargs       )
									return model




					@property
					def 					lowerCAmelCase__		(						self       ):
									# seeded differently to get different unet than `self.dummy_super_res_first`
									torch.manual_seed(1       )

									_A    =       UNetaDModel(**self.dummy_super_res_kwargs       )
									return model




					def 					lowerCAmelCase__		(						self       ):
									_A    =       self.dummy_prior
									_A    =       self.dummy_decoder
									_A    =       self.dummy_text_proj
									_A    =       self.dummy_text_encoder
									_A    =       self.dummy_tokenizer
									_A    =       self.dummy_super_res_first
									_A    =       self.dummy_super_res_last

									_A    =       UnCLIPScheduler(
									    variance_type='fixed_small_log'							,    prediction_type='sample'							,    num_train_timesteps=1000							,    clip_sample_range=5.0							,    )

									_A    =       UnCLIPScheduler(
									    variance_type='learned_range'							,    prediction_type='epsilon'							,    num_train_timesteps=1000							,    )

									_A    =       UnCLIPScheduler(
									    variance_type='fixed_small_log'							,    prediction_type='epsilon'							,    num_train_timesteps=1000							,    )

									_A    =       {
									    'prior': prior,
									    'decoder': decoder,
									    'text_proj': text_proj,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'super_res_first': super_res_first,
									    'super_res_last': super_res_last,
									    'prior_scheduler': prior_scheduler,
									    'decoder_scheduler': decoder_scheduler,
									    'super_res_scheduler': super_res_scheduler,
									}

									return components




					def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_=0       ):
									if str(snake_case_       ).startswith('mps'       ):
													_A    =       torch.manual_seed(snake_case_       )
									else:
													_A    =       torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )
									_A    =       {
									    'prompt': 'horse',
									    'generator': generator,
									    'prior_num_inference_steps': 2,
									    'decoder_num_inference_steps': 2,
									    'super_res_num_inference_steps': 2,
									    'output_type': 'numpy',
									}
									return inputs




					def 					lowerCAmelCase__		(						self       ):
									_A    =       'cpu'

									_A    =       self.get_dummy_components()

									_A    =       self.pipeline_class(**snake_case_       )
									_A    =       pipe.to(snake_case_       )

									pipe.set_progress_bar_config(disable=snake_case_       )

									_A    =       pipe(**self.get_dummy_inputs(snake_case_       )       )
									_A    =       output.images

									_A    =       pipe(
									    **self.get_dummy_inputs(snake_case_       )							,    return_dict=snake_case_							,    )[0]

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

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

									_A    =       np.array(
									    [
									        0.9997,
									        0.9988,
									        0.0028,
									        0.9997,
									        0.9984,
									        0.9965,
									        0.0029,
									        0.9986,
									        0.0025,
									    ]       )

									assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2




					def 					lowerCAmelCase__		(						self       ):
									_A    =       torch.device('cpu'       )







									class     lowerCamelCase:
														'''simple docstring'''


														__magic_name__        = 1




									_A    =       self.get_dummy_components()

									_A    =       self.pipeline_class(**snake_case_       )
									_A    =       pipe.to(snake_case_       )

									_A    =       components['prior']
									_A    =       components['decoder']
									_A    =       components['super_res_first']
									_A    =       components['tokenizer']
									_A    =       components['text_encoder']

									_A    =       torch.Generator(device=snake_case_       ).manual_seed(0       )
									_A    =       prior.dtype
									_A    =       1

									_A    =       (batch_size, prior.config.embedding_dim)
									_A    =       pipe.prepare_latents(
									    snake_case_							,    dtype=snake_case_							,    device=snake_case_							,    generator=snake_case_							,    latents=snake_case_							,    scheduler=DummyScheduler()       )
									_A    =       (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
									_A    =       pipe.prepare_latents(
									    snake_case_							,    dtype=snake_case_							,    device=snake_case_							,    generator=snake_case_							,    latents=snake_case_							,    scheduler=DummyScheduler()       )

									_A    =       (
									    batch_size,
									    super_res_first.config.in_channels // 2,
									    super_res_first.config.sample_size,
									    super_res_first.config.sample_size,
									)
									_A    =       pipe.prepare_latents(
									    snake_case_							,    dtype=snake_case_							,    device=snake_case_							,    generator=snake_case_							,    latents=snake_case_							,    scheduler=DummyScheduler()       )

									pipe.set_progress_bar_config(disable=snake_case_       )

									_A    =       'this is a prompt example'

									_A    =       torch.Generator(device=snake_case_       ).manual_seed(0       )
									_A    =       pipe(
									    [prompt]							,    generator=snake_case_							,    prior_num_inference_steps=2							,    decoder_num_inference_steps=2							,    super_res_num_inference_steps=2							,    prior_latents=snake_case_							,    decoder_latents=snake_case_							,    super_res_latents=snake_case_							,    output_type='np'							,    )
									_A    =       output.images

									_A    =       tokenizer(
									    snake_case_							,    padding='max_length'							,    max_length=tokenizer.model_max_length							,    return_tensors='pt'							,    )
									_A    =       text_encoder(text_inputs.input_ids       )
									_A    =       text_inputs.attention_mask

									_A    =       torch.Generator(device=snake_case_       ).manual_seed(0       )
									_A    =       pipe(
									    generator=snake_case_							,    prior_num_inference_steps=2							,    decoder_num_inference_steps=2							,    super_res_num_inference_steps=2							,    prior_latents=snake_case_							,    decoder_latents=snake_case_							,    super_res_latents=snake_case_							,    text_model_output=snake_case_							,    text_attention_mask=snake_case_							,    output_type='np'							,    )[0]

									# make sure passing text embeddings manually is identical
									assert np.abs(image - image_from_text       ).max() < 1E-4




					@skip_mps
					def 					lowerCAmelCase__		(						self       ):
									_A    =       torch_device == 'cpu'

									self._test_attention_slicing_forward_pass(test_max_difference=snake_case_							,    expected_max_diff=0.01       )




					@skip_mps
					def 					lowerCAmelCase__		(						self       ):
									_A    =       torch_device == 'cpu'
									_A    =       True
									_A    =       [
									    'prior_num_inference_steps',
									    'decoder_num_inference_steps',
									    'super_res_num_inference_steps',
									]

									self._test_inference_batch_single_identical(
									    test_max_difference=snake_case_							,    relax_max_difference=snake_case_							,    additional_params_copy_to_batched_inputs=snake_case_							,    )




					def 					lowerCAmelCase__		(						self       ):
									_A    =       [
									    'prior_num_inference_steps',
									    'decoder_num_inference_steps',
									    'super_res_num_inference_steps',
									]

									if torch_device == "mps":
													# TODO: MPS errors with larger batch sizes
													_A    =       [2, 3]
													self._test_inference_batch_consistent(
													    batch_sizes=snake_case_							,    additional_params_copy_to_batched_inputs=snake_case_							,    )
									else:
													self._test_inference_batch_consistent(
													    additional_params_copy_to_batched_inputs=snake_case_       )




					@skip_mps
					def 					lowerCAmelCase__		(						self       ):
									return super().test_dict_tuple_outputs_equivalent()




					@skip_mps
					def 					lowerCAmelCase__		(						self       ):
									return super().test_save_load_local()




					@skip_mps
					def 					lowerCAmelCase__		(						self       ):
									return super().test_save_load_optional_components()







@nightly
class     lowerCamelCase(	unittest.TestCase  ):
					'''simple docstring'''


					def 					lowerCAmelCase__		(						self       ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




					def 					lowerCAmelCase__		(						self       ):
									_A    =       load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/unclip/karlo_v1_alpha_horse_cpu.npy'       )

									_A    =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'       )
									pipeline.set_progress_bar_config(disable=snake_case_       )

									_A    =       torch.manual_seed(0       )
									_A    =       pipeline(
									    'horse'							,    num_images_per_prompt=1							,    generator=snake_case_							,    output_type='np'							,    )

									_A    =       output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image       ).max() < 1E-1







@slow
@require_torch_gpu
class     lowerCamelCase(	unittest.TestCase  ):
					'''simple docstring'''


					def 					lowerCAmelCase__		(						self       ):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




					def 					lowerCAmelCase__		(						self       ):
									_A    =       load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/unclip/karlo_v1_alpha_horse_fp16.npy'       )

									_A    =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,    torch_dtype=torch.floataa       )
									_A    =       pipeline.to(snake_case_       )
									pipeline.set_progress_bar_config(disable=snake_case_       )

									_A    =       torch.Generator(device='cpu'       ).manual_seed(0       )
									_A    =       pipeline(
									    'horse'							,    generator=snake_case_							,    output_type='np'							,    )

									_A    =       output.images[0]

									assert image.shape == (256, 256, 3)

									assert_mean_pixel_difference(snake_case_							,    snake_case_       )




					def 					lowerCAmelCase__		(						self       ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_A    =       UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,    torch_dtype=torch.floataa       )
									_A    =       pipe.to(snake_case_       )
									pipe.set_progress_bar_config(disable=snake_case_       )
									pipe.enable_attention_slicing()
									pipe.enable_sequential_cpu_offload()

									_A    =       pipe(
									    'horse'							,    num_images_per_prompt=1							,    prior_num_inference_steps=2							,    decoder_num_inference_steps=2							,    super_res_num_inference_steps=2							,    output_type='np'							,    )

									_A    =       torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 10**9












from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class     lowerCamelCase(	__snake_case  ):
     '''simple docstring'''


     __magic_name__        = SqueezeBertTokenizer
     __magic_name__        = SqueezeBertTokenizerFast
     __magic_name__        = True




     def 					lowerCAmelCase__		(						self							,    **snake_case_       ):
         return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname							,    **snake_case_       )




     @slow
     def 					lowerCAmelCase__		(						self       ):
         _A    =       SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'       )

         _A    =       tokenizer.encode('sequence builders'							,    add_special_tokens=snake_case_       )
         _A    =       tokenizer.encode('multi-sequence build'							,    add_special_tokens=snake_case_       )

         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_       )
         _A    =       tokenizer.build_inputs_with_special_tokens(snake_case_							,    snake_case_       )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]





