





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 A_      (    _lowerCamelCase				,   unittest.TestCase     ):
   lowerCAmelCase__       =				UnCLIPPipeline
   lowerCAmelCase__       =				TEXT_TO_IMAGE_PARAMS - {
       """negative_prompt""",
       """height""",
       """width""",
       """negative_prompt_embeds""",
       """guidance_scale""",
       """prompt_embeds""",
       """cross_attention_kwargs""",
   }
   lowerCAmelCase__       =				TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase__       =				[
       """generator""",
       """return_dict""",
       """prior_num_inference_steps""",
       """decoder_num_inference_steps""",
       """super_res_num_inference_steps""",
   ]
   lowerCAmelCase__       =				False




   @property
   def _lowerCAmelCase   (self			:List[str]				)->      str:
         return 32




   @property
   def _lowerCAmelCase   (self			:Dict				)->      str:
         return 32




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




   @property
   def _lowerCAmelCase   (self			:Optional[Any]				)->      int:
         return self.time_input_dim * 4




   @property
   def _lowerCAmelCase   (self			:int				)->      Dict:
         return 100




   @property
   def _lowerCAmelCase   (self			:str				)->      Union[str, Any]:
         __A      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
         return tokenizer




   @property
   def _lowerCAmelCase   (self			:List[str]				)->      Dict:
         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(_UpperCamelCase				)




   @property
   def _lowerCAmelCase   (self			:Optional[int]				)->      List[str]:
         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(**_UpperCamelCase				)
         return model




   @property
   def _lowerCAmelCase   (self			:Optional[int]				)->      List[Any]:
         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(**_UpperCamelCase				)
         return model




   @property
   def _lowerCAmelCase   (self			:Optional[int]				)->      List[str]:
         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(**_UpperCamelCase				)
         return model




   @property
   def _lowerCAmelCase   (self			:Optional[int]				)->      Tuple:
         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			:Union[str, Any]				)->      Optional[int]:
         torch.manual_seed(0				)

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




   @property
   def _lowerCAmelCase   (self			:Optional[Any]				)->      Dict:
         # 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			:str				)->      Tuple:
         __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			:List[Any]					,     _UpperCamelCase			:int					,     _UpperCamelCase			:Optional[int]=0				)->      str:
         if str(_UpperCamelCase				).startswith('''mps'''				):
               __A      =       torch.manual_seed(_UpperCamelCase				)
         else:
               __A      =       torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
         __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			:Optional[Any]				)->      int:
         __A      =       '''cpu'''

         __A      =       self.get_dummy_components()

         __A      =       self.pipeline_class(**_UpperCamelCase				)
         __A      =       pipe.to(_UpperCamelCase				)

         pipe.set_progress_bar_config(disable=_UpperCamelCase				)

         __A      =       pipe(**self.get_dummy_inputs(_UpperCamelCase				)				)
         __A      =       output.images

         __A      =       pipe(
             **self.get_dummy_inputs(_UpperCamelCase				)					,     return_dict=_UpperCamelCase					,     )[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(
             [
                 0.9_9_9_7,
                 0.9_9_8_8,
                 0.0_0_2_8,
                 0.9_9_9_7,
                 0.9_9_8_4,
                 0.9_9_6_5,
                 0.0_0_2_9,
                 0.9_9_8_6,
                 0.0_0_2_5,
             ]				)

         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			:str				)->      str:
         __A      =       torch.device('''cpu'''				)





         class A_      :
            lowerCAmelCase__       =				1





         __A      =       self.get_dummy_components()

         __A      =       self.pipeline_class(**_UpperCamelCase				)
         __A      =       pipe.to(_UpperCamelCase				)

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

         __A      =       torch.Generator(device=_UpperCamelCase				).manual_seed(0				)
         __A      =       prior.dtype
         __A      =       1

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

         pipe.set_progress_bar_config(disable=_UpperCamelCase				)

         __A      =       '''this is a prompt example'''

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

         __A      =       tokenizer(
             _UpperCamelCase					,     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=_UpperCamelCase				).manual_seed(0				)
         __A      =       pipe(
             generator=_UpperCamelCase					,     prior_num_inference_steps=2					,     decoder_num_inference_steps=2					,     super_res_num_inference_steps=2					,     prior_latents=_UpperCamelCase					,     decoder_latents=_UpperCamelCase					,     super_res_latents=_UpperCamelCase					,     text_model_output=_UpperCamelCase					,     text_attention_mask=_UpperCamelCase					,     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			:List[Any]				)->      Optional[Any]:
         __A      =       torch_device == '''cpu'''

         self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase					,     expected_max_diff=0.0_1				)




   @skip_mps
   def _lowerCAmelCase   (self			:List[str]				)->      int:
         __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=_UpperCamelCase					,     relax_max_difference=_UpperCamelCase					,     additional_params_copy_to_batched_inputs=_UpperCamelCase					,     )




   def _lowerCAmelCase   (self			:Optional[int]				)->      Dict:
         __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=_UpperCamelCase					,     additional_params_copy_to_batched_inputs=_UpperCamelCase					,     )
         else:
               self._test_inference_batch_consistent(
                   additional_params_copy_to_batched_inputs=_UpperCamelCase				)




   @skip_mps
   def _lowerCAmelCase   (self			:Union[str, Any]				)->      Optional[int]:
         return super().test_dict_tuple_outputs_equivalent()




   @skip_mps
   def _lowerCAmelCase   (self			:List[Any]				)->      Tuple:
         return super().test_save_load_local()






   @skip_mps
   def _lowerCAmelCase   (self			:Optional[int]				)->      str:
         return super().test_save_load_optional_components()





@nightly
class A_      (    unittest.TestCase     ):




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






   def _lowerCAmelCase   (self			:Tuple				)->      Optional[Any]:
         __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=_UpperCamelCase				)

         __A      =       torch.manual_seed(0				)
         __A      =       pipeline(
             '''horse'''					,     num_images_per_prompt=1					,     generator=_UpperCamelCase					,     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 A_      (    unittest.TestCase     ):




   def _lowerCAmelCase   (self			:Optional[int]				)->      int:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




   def _lowerCAmelCase   (self			:Dict				)->      Union[str, Any]:
         __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(_UpperCamelCase				)
         pipeline.set_progress_bar_config(disable=_UpperCamelCase				)

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

         __A      =       output.images[0]

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

         assert_mean_pixel_difference(_UpperCamelCase					,     _UpperCamelCase				)






   def _lowerCAmelCase   (self			:List[Any]				)->      Tuple:
         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(_UpperCamelCase				)
         pipe.set_progress_bar_config(disable=_UpperCamelCase				)
         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 unittest.mock import patch

import datasets
from datasets import Dataset



def _a       (			)			->		Optional[Any]:







      '''simple docstring'''






      __A      =       Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}				)

      with patch('''tqdm.auto.tqdm'''				) as mock_tqdm:
            datasets.disable_progress_bar()
            dset.map(lambda lowerCamelCase				: {"col_2": x["col_1"] + 1}				)
            mock_tqdm.assert_not_called()

            mock_tqdm.reset_mock()

            datasets.enable_progress_bar()
            dset.map(lambda lowerCamelCase				: {"col_2": x["col_1"] + 1}				)
            mock_tqdm.assert_called()





