



"""simple docstring"""







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     (   SCREAMING_SNAKE_CASE__    ,  unittest.TestCase):
   __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 							__lowercase				( self       :							str		)       ->				Optional[Any]:
         return 32



   @property
   def 							__lowercase				( self       :							Optional[int]		)       ->				List[str]:
         return 32



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



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



   @property
   def 							__lowercase				( self       :							Optional[int]		)       ->				Dict:
         return 100



   @property
   def 							__lowercase				( self       :							Optional[int]		)       ->				Tuple:
         snake_case      : Any							=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
         return tokenizer



   @property
   def 							__lowercase				( self       :							str		)       ->				int:
         torch.manual_seed(0		)
         snake_case      : int							=   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(_lowercase		)



   @property
   def 							__lowercase				( self       :							Tuple		)       ->				str:
         torch.manual_seed(0		)

         snake_case      : Tuple							=   {
             "num_attention_heads": 2,
             "attention_head_dim": 12,
             "embedding_dim": self.text_embedder_hidden_size,
             "num_layers": 1,
         }

         snake_case      : Tuple							=   PriorTransformer(**_lowercase		)
         return model



   @property
   def 							__lowercase				( self       :							Optional[Any]		)       ->				Optional[int]:
         torch.manual_seed(0		)

         snake_case      : Tuple							=   {
             "clip_embeddings_dim": self.text_embedder_hidden_size,
             "time_embed_dim": self.time_embed_dim,
             "cross_attention_dim": self.cross_attention_dim,
         }

         snake_case      : Dict							=   UnCLIPTextProjModel(**_lowercase		)
         return model



   @property
   def 							__lowercase				( self       :							int		)       ->				List[Any]:
         torch.manual_seed(0		)

         snake_case      : Optional[int]							=   {
             "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",
         }

         snake_case      : Any							=   UNetaDConditionModel(**_lowercase		)
         return model



   @property
   def 							__lowercase				( self       :							Optional[Any]		)       ->				Optional[int]:
         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 							__lowercase				( self       :							int		)       ->				List[Any]:
         torch.manual_seed(0		)

         snake_case      : Optional[Any]							=   UNetaDModel(**self.dummy_super_res_kwargs		)
         return model



   @property
   def 							__lowercase				( self       :							Optional[int]		)       ->				Optional[int]:
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1		)

         snake_case      : Optional[int]							=   UNetaDModel(**self.dummy_super_res_kwargs		)
         return model



   def 							__lowercase				( self       :							Optional[Any]		)       ->				Optional[int]:
         snake_case      : str							=   self.dummy_prior
         snake_case      : Dict							=   self.dummy_decoder
         snake_case      : List[str]							=   self.dummy_text_proj
         snake_case      : List[Any]							=   self.dummy_text_encoder
         snake_case      : List[str]							=   self.dummy_tokenizer
         snake_case      : Union[str, Any]							=   self.dummy_super_res_first
         snake_case      : str							=   self.dummy_super_res_last

         snake_case      : Dict							=   UnCLIPScheduler(
             variance_type="fixed_small_log"      ,  prediction_type="sample"      ,  num_train_timesteps=1000      ,  clip_sample_range=5.0      ,  )

         snake_case      : int							=   UnCLIPScheduler(
             variance_type="learned_range"      ,  prediction_type="epsilon"      ,  num_train_timesteps=1000      ,  )

         snake_case      : Tuple							=   UnCLIPScheduler(
             variance_type="fixed_small_log"      ,  prediction_type="epsilon"      ,  num_train_timesteps=1000      ,  )

         snake_case      : str							=   {
             "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 							__lowercase				( self       :							List[Any]      ,  _lowercase       :							Dict      ,  _lowercase       :							Union[str, Any]=0		)       ->				int:
         if str(_lowercase		).startswith("mps"		):
               snake_case      : Union[str, Any]							=   torch.manual_seed(_lowercase		)
         else:
               snake_case      : Any							=   torch.Generator(device=_lowercase		).manual_seed(_lowercase		)
         snake_case      : Optional[Any]							=   {
             "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 							__lowercase				( self       :							Union[str, Any]		)       ->				List[Any]:
         snake_case      : Tuple							=   "cpu"

         snake_case      : Union[str, Any]							=   self.get_dummy_components()

         snake_case      : int							=   self.pipeline_class(**_lowercase		)
         snake_case      : Dict							=   pipe.to(_lowercase		)

         pipe.set_progress_bar_config(disable=_lowercase		)

         snake_case      : Optional[int]							=   pipe(**self.get_dummy_inputs(_lowercase		)		)
         snake_case      : Optional[int]							=   output.images

         snake_case      : Union[str, Any]							=   pipe(
             **self.get_dummy_inputs(_lowercase		)      ,  return_dict=_lowercase      ,  )[0]

         snake_case      : Dict							=   image[0, -3:, -3:, -1]
         snake_case      : Optional[int]							=   image_from_tuple[0, -3:, -3:, -1]

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

         snake_case      : List[str]							=   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 							__lowercase				( self       :							Union[str, Any]		)       ->				Optional[Any]:
         snake_case      : Any							=   torch.device("cpu"		)

         class 			_a     :
            __magic_name__    				=    1



         snake_case      : Dict							=   self.get_dummy_components()

         snake_case      : Union[str, Any]							=   self.pipeline_class(**_lowercase		)
         snake_case      : Optional[Any]							=   pipe.to(_lowercase		)

         snake_case      : Optional[int]							=   components["prior"]
         snake_case      : Union[str, Any]							=   components["decoder"]
         snake_case      : Any							=   components["super_res_first"]
         snake_case      : Tuple							=   components["tokenizer"]
         snake_case      : Any							=   components["text_encoder"]

         snake_case      : Optional[int]							=   torch.Generator(device=_lowercase		).manual_seed(0		)
         snake_case      : Tuple							=   prior.dtype
         snake_case      : Any							=   1

         snake_case      : Any							=   (batch_size, prior.config.embedding_dim)
         snake_case      : Union[str, Any]							=   pipe.prepare_latents(
             _lowercase      ,  dtype=_lowercase      ,  device=_lowercase      ,  generator=_lowercase      ,  latents=_lowercase      ,  scheduler=DummyScheduler()		)
         snake_case      : Optional[Any]							=   (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
         snake_case      : Any							=   pipe.prepare_latents(
             _lowercase      ,  dtype=_lowercase      ,  device=_lowercase      ,  generator=_lowercase      ,  latents=_lowercase      ,  scheduler=DummyScheduler()		)

         snake_case      : str							=   (
             batch_size,
             super_res_first.config.in_channels // 2,
             super_res_first.config.sample_size,
             super_res_first.config.sample_size,
         )
         snake_case      : List[Any]							=   pipe.prepare_latents(
             _lowercase      ,  dtype=_lowercase      ,  device=_lowercase      ,  generator=_lowercase      ,  latents=_lowercase      ,  scheduler=DummyScheduler()		)

         pipe.set_progress_bar_config(disable=_lowercase		)

         snake_case      : List[Any]							=   "this is a prompt example"

         snake_case      : Tuple							=   torch.Generator(device=_lowercase		).manual_seed(0		)
         snake_case      : int							=   pipe(
             [prompt]      ,  generator=_lowercase      ,  prior_num_inference_steps=2      ,  decoder_num_inference_steps=2      ,  super_res_num_inference_steps=2      ,  prior_latents=_lowercase      ,  decoder_latents=_lowercase      ,  super_res_latents=_lowercase      ,  output_type="np"      ,  )
         snake_case      : int							=   output.images

         snake_case      : Any							=   tokenizer(
             _lowercase      ,  padding="max_length"      ,  max_length=tokenizer.model_max_length      ,  return_tensors="pt"      ,  )
         snake_case      : List[Any]							=   text_encoder(text_inputs.input_ids		)
         snake_case      : Union[str, Any]							=   text_inputs.attention_mask

         snake_case      : List[Any]							=   torch.Generator(device=_lowercase		).manual_seed(0		)
         snake_case      : Optional[int]							=   pipe(
             generator=_lowercase      ,  prior_num_inference_steps=2      ,  decoder_num_inference_steps=2      ,  super_res_num_inference_steps=2      ,  prior_latents=_lowercase      ,  decoder_latents=_lowercase      ,  super_res_latents=_lowercase      ,  text_model_output=_lowercase      ,  text_attention_mask=_lowercase      ,  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 							__lowercase				( self       :							Union[str, Any]		)       ->				Optional[Any]:
         snake_case      : Optional[int]							=   torch_device == "cpu"

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



   @skip_mps
   def 							__lowercase				( self       :							int		)       ->				List[Any]:
         snake_case      : int							=   torch_device == "cpu"
         snake_case      : Any							=   True
         snake_case      : Optional[Any]							=   [
             "prior_num_inference_steps",
             "decoder_num_inference_steps",
             "super_res_num_inference_steps",
         ]

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



   def 							__lowercase				( self       :							Union[str, Any]		)       ->				List[str]:
         snake_case      : str							=   [
             "prior_num_inference_steps",
             "decoder_num_inference_steps",
             "super_res_num_inference_steps",
         ]

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



   @skip_mps
   def 							__lowercase				( self       :							List[Any]		)       ->				Any:
         return super().test_dict_tuple_outputs_equivalent()



   @skip_mps
   def 							__lowercase				( self       :							Dict		)       ->				Tuple:
         return super().test_save_load_local()



   @skip_mps
   def 							__lowercase				( self       :							List[Any]		)       ->				str:
         return super().test_save_load_optional_components()



@nightly
class 			_a     (   unittest.TestCase):



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



   def 							__lowercase				( self       :							Optional[int]		)       ->				Dict:
         snake_case      : Dict							=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/unclip/karlo_v1_alpha_horse_cpu.npy"		)

         snake_case      : List[str]							=   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"		)
         pipeline.set_progress_bar_config(disable=_lowercase		)

         snake_case      : Dict							=   torch.manual_seed(0		)
         snake_case      : Dict							=   pipeline(
             "horse"      ,  num_images_per_prompt=1      ,  generator=_lowercase      ,  output_type="np"      ,  )

         snake_case      : Optional[int]							=   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 							__lowercase				( self       :							str		)       ->				Dict:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



   def 							__lowercase				( self       :							int		)       ->				List[Any]:
         snake_case      : int							=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/unclip/karlo_v1_alpha_horse_fp16.npy"		)

         snake_case      : str							=   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,  torch_dtype=torch.floataa		)
         snake_case      : int							=   pipeline.to(_lowercase		)
         pipeline.set_progress_bar_config(disable=_lowercase		)

         snake_case      : Optional[int]							=   torch.Generator(device="cpu"		).manual_seed(0		)
         snake_case      : Optional[Any]							=   pipeline(
             "horse"      ,  generator=_lowercase      ,  output_type="np"      ,  )

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

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

         assert_mean_pixel_difference(_lowercase      ,  _lowercase		)



   def 							__lowercase				( self       :							Union[str, Any]		)       ->				List[Any]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         snake_case      : Optional[int]							=   UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"      ,  torch_dtype=torch.floataa		)
         snake_case      : List[Any]							=   pipe.to(_lowercase		)
         pipe.set_progress_bar_config(disable=_lowercase		)
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         snake_case      : Dict							=   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"      ,  )

         snake_case      : Optional[Any]							=   torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 10**9








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	['CamembertTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CamembertForCausalLM',
			    'CamembertForMaskedLM',
			    'CamembertForMultipleChoice',
			    'CamembertForQuestionAnswering',
			    'CamembertForSequenceClassification',
			    'CamembertForTokenClassification',
			    'CamembertModel',
			    'CamembertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A    =	[
			    'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFCamembertForCausalLM',
			    'TFCamembertForMaskedLM',
			    'TFCamembertForMultipleChoice',
			    'TFCamembertForQuestionAnswering',
			    'TFCamembertForSequenceClassification',
			    'TFCamembertForTokenClassification',
			    'TFCamembertModel',
			    'TFCamembertPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert import CamembertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_camembert_fast import CamembertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_camembert import (
						    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CamembertForCausalLM,
						    CamembertForMaskedLM,
						    CamembertForMultipleChoice,
						    CamembertForQuestionAnswering,
						    CamembertForSequenceClassification,
						    CamembertForTokenClassification,
						    CamembertModel,
						    CamembertPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_camembert import (
						    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCamembertForCausalLM,
						    TFCamembertForMaskedLM,
						    TFCamembertForMultipleChoice,
						    TFCamembertForQuestionAnswering,
						    TFCamembertForSequenceClassification,
						    TFCamembertForTokenClassification,
						    TFCamembertModel,
						    TFCamembertPreTrainedModel,
						)

else:
			import sys

			A    =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



