
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''MCTCTFeatureExtractor'''
				_lowercase    	='''AutoTokenizer'''




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			self.feature_extractor
							lowerCAmelCase_      			=			False




				def __call__(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Union[str, Any]:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*_UpperCamelCase       ,       **_UpperCamelCase  )

							if "raw_speech" in kwargs:
										warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."  )
										lowerCAmelCase_      			=			kwargs.pop("raw_speech"  )
							else:
										lowerCAmelCase_      			=			kwargs.pop("audio"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("sampling_rate"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("text"       ,       _UpperCamelCase  )
							if len(_UpperCamelCase  ) > 0:
										lowerCAmelCase_      			=			args[0]
										lowerCAmelCase_      			=			args[1:]

							if audio is None and text is None:
										raise ValueError("You need to specify either an `audio` or `text` input to process."  )

							if audio is not None:
										lowerCAmelCase_      			=			self.feature_extractor(_UpperCamelCase       ,       *_UpperCamelCase       ,       sampling_rate=_UpperCamelCase       ,       **_UpperCamelCase  )
							if text is not None:
										lowerCAmelCase_      			=			self.tokenizer(_UpperCamelCase       ,       **_UpperCamelCase  )

							if text is None:
										return inputs
							elif audio is None:
										return encodings
							else:
										lowerCAmelCase_      			=			encodings["input_ids"]
										return inputs




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   List[str]:
							return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[int]:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor.pad(*_UpperCamelCase       ,       **_UpperCamelCase  )

							lowerCAmelCase_      			=			kwargs.pop("input_features"       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			kwargs.pop("labels"       ,       _UpperCamelCase  )
							if len(_UpperCamelCase  ) > 0:
										lowerCAmelCase_      			=			args[0]
										lowerCAmelCase_      			=			args[1:]

							if input_features is not None:
										lowerCAmelCase_      			=			self.feature_extractor.pad(_UpperCamelCase       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )
							if labels is not None:
										lowerCAmelCase_      			=			self.tokenizer.pad(_UpperCamelCase       ,       **_UpperCamelCase  )

							if labels is None:
										return input_features
							elif input_features is None:
										return labels
							else:
										lowerCAmelCase_      			=			labels["input_ids"]
										return input_features




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				@contextmanager
				def   __a		(      self  )       ->   int:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your audio inputs, or in a separate call."  )
							lowerCAmelCase_      			=			True
							lowerCAmelCase_      			=			self.tokenizer
							yield
							lowerCAmelCase_      			=			self.feature_extractor
							lowerCAmelCase_      			=			False





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 			_lowerCAmelCase      (			__a      ,   __a      ,   __a      ,   unittest.TestCase ):
				_lowercase    	=StableUnCLIPPipeline
				_lowercase    	=TEXT_TO_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase    	=TEXT_TO_IMAGE_IMAGE_PARAMS

				# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
				_lowercase    	=False




				def   __a		(      self  )       ->   Dict:
							lowerCAmelCase_      			=			32
							lowerCAmelCase_      			=			embedder_hidden_size

							# prior components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=_UpperCamelCase       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			PriorTransformer(
							    num_attention_heads=2       ,       attention_head_dim=12       ,       embedding_dim=_UpperCamelCase       ,       num_layers=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDPMScheduler(
							    variance_type="fixed_small_log"       ,       prediction_type="sample"       ,       num_train_timesteps=1_000       ,       clip_sample=_UpperCamelCase       ,       clip_sample_range=5.0       ,       beta_schedule="squaredcos_cap_v2"       ,       )

							# regular denoising components

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase  )
							lowerCAmelCase_      			=			DDPMScheduler(beta_schedule="squaredcos_cap_v2"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0       ,       eos_token_id=2       ,       hidden_size=_UpperCamelCase       ,       projection_dim=32       ,       intermediate_size=37       ,       layer_norm_eps=1e-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=1_000       ,       )  )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			UNetaDConditionModel(
							    sample_size=32       ,       in_channels=4       ,       out_channels=4       ,       down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")       ,       up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")       ,       block_out_channels=(32, 64)       ,       attention_head_dim=(2, 4)       ,       class_embed_type="projection"       ,       projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,       cross_attention_dim=_UpperCamelCase       ,       layers_per_block=1       ,       upcast_attention=_UpperCamelCase       ,       use_linear_projection=_UpperCamelCase       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			DDIMScheduler(
							    beta_schedule="scaled_linear"       ,       beta_start=0.00085       ,       beta_end=0.012       ,       prediction_type="v_prediction"       ,       set_alpha_to_one=_UpperCamelCase       ,       steps_offset=1       ,       )

							torch.manual_seed(0  )
							lowerCAmelCase_      			=			AutoencoderKL()

							lowerCAmelCase_      			=			{
							    # prior components
							    "prior_tokenizer": prior_tokenizer,
							    "prior_text_encoder": prior_text_encoder,
							    "prior": prior,
							    "prior_scheduler": prior_scheduler,
							    # image noising components
							    "image_normalizer": image_normalizer,
							    "image_noising_scheduler": image_noising_scheduler,
							    # regular denoising components
							    "tokenizer": tokenizer,
							    "text_encoder": text_encoder,
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							}

							return components




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=0  )       ->   Tuple:
							if str(_UpperCamelCase  ).startswith("mps"  ):
										lowerCAmelCase_      			=			torch.manual_seed(_UpperCamelCase  )
							else:
										lowerCAmelCase_      			=			torch.Generator(device=_UpperCamelCase  ).manual_seed(_UpperCamelCase  )
							lowerCAmelCase_      			=			{
							    "prompt": "A painting of a squirrel eating a burger",
							    "generator": generator,
							    "num_inference_steps": 2,
							    "prior_num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			torch_device == "cpu"

							self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			torch_device in ["cpu", "mps"]

							self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase  )





@slow
@require_torch_gpu
class 			_lowerCAmelCase      (			unittest.TestCase ):




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




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"  )

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			torch.Generator(device="cpu"  ).manual_seed(0  )
							lowerCAmelCase_      			=			pipe("anime turle"       ,       generator=_UpperCamelCase       ,       output_type="np"  )

							lowerCAmelCase_      			=			output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_UpperCamelCase       ,       _UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[Any]:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase_      			=			StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"       ,       torch_dtype=torch.floataa  )
							lowerCAmelCase_      			=			pipe.to(_UpperCamelCase  )
							pipe.set_progress_bar_config(disable=_UpperCamelCase  )
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase_      			=			pipe(
							    "anime turtle"       ,       prior_num_inference_steps=2       ,       num_inference_steps=2       ,       output_type="np"       ,       )

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



