

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     __UpperCamelCase     (       lowercase  ,  lowercase  ,  lowercase  ,  unittest.TestCase    ):
	SCREAMING_SNAKE_CASE__     					=  StableUnCLIPPipeline
	SCREAMING_SNAKE_CASE__     					=  TEXT_TO_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__     					=  TEXT_TO_IMAGE_BATCH_PARAMS
	SCREAMING_SNAKE_CASE__     					=  TEXT_TO_IMAGE_IMAGE_PARAMS
	SCREAMING_SNAKE_CASE__     					=  TEXT_TO_IMAGE_IMAGE_PARAMS

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




	def    __A       (		self						:    Tuple				):


		'''simple docstring'''







		UpperCAmelCase_												=      32
		UpperCAmelCase_												=      embedder_hidden_size

		# prior components

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

		torch.manual_seed(0				)
		UpperCAmelCase_												=      CLIPTextModelWithProjection(
		    CLIPTextConfig(
		        bos_token_id=0		,						eos_token_id=2		,						hidden_size=A__		,						projection_dim=A__		,						intermediate_size=37		,						layer_norm_eps=1e-05		,						num_attention_heads=4		,						num_hidden_layers=5		,						pad_token_id=1		,						vocab_size=1_000		,						)				)

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

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

		# regular denoising components

		torch.manual_seed(0				)
		UpperCAmelCase_												=      StableUnCLIPImageNormalizer(embedding_dim=A__				)
		UpperCAmelCase_												=      DDPMScheduler(beta_schedule="squaredcos_cap_v2"				)

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

		torch.manual_seed(0				)
		UpperCAmelCase_												=      CLIPTextModel(
		    CLIPTextConfig(
		        bos_token_id=0		,						eos_token_id=2		,						hidden_size=A__		,						projection_dim=32		,						intermediate_size=37		,						layer_norm_eps=1e-05		,						num_attention_heads=4		,						num_hidden_layers=5		,						pad_token_id=1		,						vocab_size=1_000		,						)				)

		torch.manual_seed(0				)
		UpperCAmelCase_												=      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=A__		,						layers_per_block=1		,						upcast_attention=A__		,						use_linear_projection=A__		,						)

		torch.manual_seed(0				)
		UpperCAmelCase_												=      DDIMScheduler(
		    beta_schedule="scaled_linear"		,						beta_start=0.00_085		,						beta_end=0.012		,						prediction_type="v_prediction"		,						set_alpha_to_one=A__		,						steps_offset=1		,						)

		torch.manual_seed(0				)
		UpperCAmelCase_												=      AutoencoderKL()

		UpperCAmelCase_												=      {
		    # 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						:    int		,						lowerCAmelCase						:    int		,						lowerCAmelCase						:    Optional[int]=0				):


		'''simple docstring'''







		if str(A__				).startswith("mps"				):
			UpperCAmelCase_												=      torch.manual_seed(A__				)
		else:
			UpperCAmelCase_												=      torch.Generator(device=A__				).manual_seed(A__				)
		UpperCAmelCase_												=      {
		    """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[str]				):


		'''simple docstring'''







		UpperCAmelCase_												=      torch_device == """cpu"""

		self._test_attention_slicing_forward_pass(test_max_difference=A__				)




	def    __A       (		self						:    str				):


		'''simple docstring'''







		UpperCAmelCase_												=      torch_device in ["""cpu""", """mps"""]

		self._test_inference_batch_single_identical(test_max_difference=A__				)


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




	def    __A       (		self						:    Any				):


		'''simple docstring'''







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




	def    __A       (		self						:    Tuple				):


		'''simple docstring'''







		UpperCAmelCase_												=      load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"				)

		UpperCAmelCase_												=      StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"		,						torch_dtype=torch.floataa				)
		pipe.to(A__				)
		pipe.set_progress_bar_config(disable=A__				)
		# 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()

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

		UpperCAmelCase_												=      output.images[0]

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

		assert_mean_pixel_difference(A__		,						A__				)




	def    __A       (		self						:    Optional[Any]				):


		'''simple docstring'''







		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

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

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

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


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


A_			:					List[Any]    			=     logging.get_logger(__name__)

A_			:					Any    			=     {
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			'''gpt_neo'''
					UpperCAmelCase__:  str    				=			['''past_key_values''']
					UpperCAmelCase__:  Dict    				=			{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






					def __init__(					self       ,			A__=5_0257       ,			A__=2048       ,			A__=2048       ,			A__=24       ,			A__=[[["global", "local"], 12]]       ,			A__=16       ,			A__=None       ,			A__=256       ,			A__="gelu_new"       ,			A__=0.0       ,			A__=0.0       ,			A__=0.0       ,			A__=0.1       ,			A__=1e-5       ,			A__=0.0_2       ,			A__=True       ,			A__=5_0256       ,			A__=5_0256       ,			**A__       ,			):
						A__				:       Optional[Any]	  =						vocab_size
						A__				:       Optional[int]	  =						max_position_embeddings
						A__				:       Optional[int]	  =						hidden_size
						A__				:       Dict	  =						num_layers
						A__				:       List[str]	  =						num_heads
						A__				:       Optional[int]	  =						intermediate_size
						A__				:       Union[str, Any]	  =						window_size
						A__				:       Optional[int]	  =						activation_function
						A__				:       Optional[Any]	  =						resid_dropout
						A__				:       Optional[int]	  =						embed_dropout
						A__				:       Any	  =						attention_dropout
						A__				:       Optional[Any]	  =						classifier_dropout
						A__				:       int	  =						layer_norm_epsilon
						A__				:       Tuple	  =						initializer_range
						A__				:       str	  =						use_cache

						A__				:       List[str]	  =						bos_token_id
						A__				:       List[Any]	  =						eos_token_id

						A__				:       Any	  =						attention_types
						A__				:       List[str]	  =						self.expand_attention_types_params(A__     )

						if len(self.attention_layers     ) != self.num_layers:
							raise ValueError(
							    """Configuration for convolutional module is incorrect. """
							    """It is required that `len(config.attention_layers)` == `config.num_layers` """
							    F"""but is `len(config.attention_layers) = {len(self.attention_layers     )}`, """
							    F"""`config.num_layers = {self.num_layers}`. """
							    """`config.attention_layers` is prepared using `config.attention_types`. """
							    """Please verify the value of `config.attention_types` argument."""     )

						super().__init__(bos_token_id=A__       ,			eos_token_id=A__       ,			**A__     )






					@staticmethod
					def     __A     (					A__     ):
						A__				:       int	  =						[]
						for item in attention_types:
							for _ in range(item[1]     ):
								attentions.extend(item[0]     )
						return attentions



def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Optional[Any]   )	-> Dict:
	import torch

	A__				:       List[Any]	  =						input.size()
	A__				:       List[str]	  =						len(lowercase_   )
	A__				:       Union[str, Any]	  =						shape[dimension]

	A__				:       Optional[int]	  =						torch.arange(0       , lowercase_       , lowercase_   )
	A__				:       str	  =						torch.div(sizedim - size       , lowercase_       , rounding_mode="""floor"""   ) + 1
	A__				:       Any	  =						torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

	A__				:       Union[str, Any]	  =						[slice(lowercase_   )] * rank
	A__				:       Any	  =						indices
	A__				:       List[str]	  =						input[s]

	A__				:       Dict	  =						list(range(0       , rank + 1   )   )
	perm.append(perm.pop(dimension + 1   )   )

	return sliced.permute(lowercase_   )

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					int   )	-> int:
	import torch

	A__				:       Union[str, Any]	  =						torch.arange(1       , lowercase_   )
	A__				:       str	  =						torch.remainder(lowercase_       , lowercase_   )
	A__				:       Optional[Any]	  =						remainders == 0
	A__				:       Any	  =						candidates[divisor_indices]
	A__				:       Optional[Any]	  =						torch.max(lowercase_   )
	return largest_divisor, torch.div(lowercase_       , lowercase_       , rounding_mode="""floor"""   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@property
					def     __A     (					self     ):
						A__				:       Any	  =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
						if self.use_past:
							self.fill_with_past_key_values_(A__       ,			direction="""inputs"""     )
							A__				:       Optional[Any]	  =						{0: """batch""", 1: """past_sequence + sequence"""}
						else:
							A__				:       List[str]	  =						{0: """batch""", 1: """sequence"""}

						return common_inputs






					@property
					def     __A     (					self     ):
						return self._config.num_heads






					def     __A     (					self       ,			A__       ,			A__ = -1       ,			A__ = -1       ,			A__ = False       ,			A__ = None       ,			):
						A__				:       int	  =						super(A__       ,			self     ).generate_dummy_inputs(
						    A__       ,			batch_size=A__       ,			seq_length=A__       ,			is_pair=A__       ,			framework=A__     )

						# We need to order the input in the way they appears in the forward()
						A__				:       Optional[int]	  =						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}     )

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
							else:
								import torch

								A__  ,  A__				:       Dict	  =						common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								A__				:       int	  =						seqlen + 2
								A__				:       int	  =						(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								A__				:       Dict	  =						[
								    (torch.zeros(A__     ), torch.zeros(A__     )) for _ in range(self.num_layers     )
								]

						A__				:       List[Any]	  =						common_inputs["""attention_mask"""]
						if self.use_past:
							A__				:       int	  =						ordered_inputs["""attention_mask"""].dtype
							A__				:       str	  =						torch.cat(
							    [ordered_inputs["""attention_mask"""], torch.ones(A__       ,			A__       ,			dtype=A__     )]       ,			dim=1     )

						return ordered_inputs






					@property
					def     __A     (					self     ):
						return 13



