




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__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	UnCLIPPipeline
     a		:			Any							=	TEXT_TO_IMAGE_PARAMS - {
         """negative_prompt""",
         """height""",
         """width""",
         """negative_prompt_embeds""",
         """guidance_scale""",
         """prompt_embeds""",
         """cross_attention_kwargs""",
     }
     a		:			int							=	TEXT_TO_IMAGE_BATCH_PARAMS
     a		:			str							=	[
         """generator""",
         """return_dict""",
         """prior_num_inference_steps""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     a		:			int							=	False







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



          '''simple docstring'''



          return 32







     @property
     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          return 32







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



          '''simple docstring'''



          return self.time_input_dim







     @property
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          return self.time_input_dim * 4







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



          '''simple docstring'''



          return 100







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



          '''simple docstring'''



          a      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
          return tokenizer







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



          '''simple docstring'''



          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(A )







     @property
     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          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(**A )
          return model







     @property
     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          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(**A )
          return model







     @property
     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          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(**A )
          return model







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



          '''simple docstring'''



          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 )  ->    Any:



          '''simple docstring'''



          torch.manual_seed(0 )

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







     @property
     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          torch.manual_seed(1 )

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







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



          '''simple docstring'''



          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  ,		A  ,		A=0 )  ->    Any:



          '''simple docstring'''



          if str(A ).startswith("mps" ):
               a      =      torch.manual_seed(A )
          else:
               a      =      torch.Generator(device=A ).manual_seed(A )
          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[int]:



          '''simple docstring'''



          a      =      "cpu"

          a      =      self.get_dummy_components()

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

          pipe.set_progress_bar_config(disable=A )

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

          a      =      pipe(
              **self.get_dummy_inputs(A )  ,		return_dict=A  ,		)[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 )  ->    Optional[int]:



          '''simple docstring'''



          a      =      torch.device("cpu" )




          class 			a__       :
               a		:			str							=	1







          a      =      self.get_dummy_components()

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

          a      =      components["prior"]
          a      =      components["decoder"]
          a      =      components["super_res_first"]
          a      =      components["tokenizer"]
          a      =      components["text_encoder"]

          a      =      torch.Generator(device=A ).manual_seed(0 )
          a      =      prior.dtype
          a      =      1

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

          pipe.set_progress_bar_config(disable=A )

          a      =      "this is a prompt example"

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

          a      =      tokenizer(
              A  ,		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=A ).manual_seed(0 )
          a      =      pipe(
              generator=A  ,		prior_num_inference_steps=2  ,		decoder_num_inference_steps=2  ,		super_res_num_inference_steps=2  ,		prior_latents=A  ,		decoder_latents=A  ,		super_res_latents=A  ,		text_model_output=A  ,		text_attention_mask=A  ,		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 )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      torch_device == "cpu"

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







     @skip_mps
     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          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=A  ,		relax_max_difference=A  ,		additional_params_copy_to_batched_inputs=A  ,		)







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          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=A  ,		additional_params_copy_to_batched_inputs=A  ,		)
          else:
               self._test_inference_batch_consistent(
                   additional_params_copy_to_batched_inputs=A )







     @skip_mps
     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          return super().test_dict_tuple_outputs_equivalent()







     @skip_mps
     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          return super().test_save_load_local()







     @skip_mps
     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          return super().test_save_load_optional_components()







@nightly
class 			a__       (					unittest.TestCase      ):







     def 			lowerCAmelCase_				(	self )  ->    List[str]:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          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=A )

          a      =      torch.manual_seed(0 )
          a      =      pipeline(
              "horse"  ,		num_images_per_prompt=1  ,		generator=A  ,		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 )  ->    Dict:



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          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(A )
          pipeline.set_progress_bar_config(disable=A )

          a      =      torch.Generator(device="cpu" ).manual_seed(0 )
          a      =      pipeline(
              "horse"  ,		generator=A  ,		output_type="np"  ,		)

          a      =      output.images[0]

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

          assert_mean_pixel_difference(A  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          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(A )
          pipe.set_progress_bar_config(disable=A )
          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







import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	MPNetTokenizer
     a		:			Dict							=	MPNetTokenizerFast
     a		:			Optional[int]							=	True
     a		:			Any							=	True







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          super().setUp()

          a      =      [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          a      =      os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8" ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          a      =      "UNwant\u00E9d,running"
          a      =      "unwanted, running"
          return input_text, output_text







     def 			lowerCAmelCase_				(	self )  ->    List[Any]:



          '''simple docstring'''



          a      =      self.tokenizer_class(self.vocab_file )

          a      =      tokenizer.tokenize("UNwant\u00E9d,running" )
          self.assertListEqual(A  ,		["un", "##want", "##ed", ",", "runn", "##ing"] )
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A )  ,		[9, 6, 7, 12, 10, 11] )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

