
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__		(    __snake_case,			__snake_case,			__snake_case,			unittest.TestCase   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				StableUnCLIPPipeline
	_lowerCAmelCase							       =				TEXT_TO_IMAGE_PARAMS
	_lowerCAmelCase							       =				TEXT_TO_IMAGE_BATCH_PARAMS
	_lowerCAmelCase							       =				TEXT_TO_IMAGE_IMAGE_PARAMS
	_lowerCAmelCase							       =				TEXT_TO_IMAGE_IMAGE_PARAMS

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





	def   __snake_case				(			self		):
					A__  :	List[Any]         =							32
					A__  :	Union[str, Any]         =							embedder_hidden_size

					# prior components

					torch.manual_seed(0		)
					A__  :	Tuple         =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

					torch.manual_seed(0		)
					A__  :	Any         =							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=1000				,       )		)

					torch.manual_seed(0		)
					A__  :	Optional[int]         =							PriorTransformer(
					    num_attention_heads=2				,       attention_head_dim=12				,       embedding_dim=A_				,       num_layers=1				,       )

					torch.manual_seed(0		)
					A__  :	Optional[Any]         =							DDPMScheduler(
					    variance_type='''fixed_small_log'''				,       prediction_type='''sample'''				,       num_train_timesteps=1000				,       clip_sample=A_				,       clip_sample_range=5.0				,       beta_schedule='''squaredcos_cap_v2'''				,       )

					# regular denoising components

					torch.manual_seed(0		)
					A__  :	Tuple         =							StableUnCLIPImageNormalizer(embedding_dim=A_		)
					A__  :	Optional[int]         =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		)

					torch.manual_seed(0		)
					A__  :	str         =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

					torch.manual_seed(0		)
					A__  :	Dict         =							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=1000				,       )		)

					torch.manual_seed(0		)
					A__  :	Optional[Any]         =							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		)
					A__  :	Union[str, Any]         =							DDIMScheduler(
					    beta_schedule='''scaled_linear'''				,       beta_start=0.0_0_0_8_5				,       beta_end=0.0_1_2				,       prediction_type='''v_prediction'''				,       set_alpha_to_one=A_				,       steps_offset=1				,       )

					torch.manual_seed(0		)
					A__  :	Any         =							AutoencoderKL()

					A__  :	List[Any]         =							{
					    # 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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=0		):




					if str(A_		).startswith('''mps'''		):
									A__  :	List[str]         =							torch.manual_seed(A_		)
					else:
									A__  :	str         =							torch.Generator(device=A_		).manual_seed(A_		)
					A__  :	int         =							{
					    "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   __snake_case				(			self		):
					A__  :	Any         =							torch_device == "cpu"

					self._test_attention_slicing_forward_pass(test_max_difference=A_		)





	def   __snake_case				(			self		):
					A__  :	List[str]         =							torch_device in ["cpu", "mps"]

					self._test_inference_batch_single_identical(test_max_difference=A_		)







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







	'''simple docstring'''





	def   __snake_case				(			self		):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





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

					A__  :	Union[str, Any]         =							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()

					A__  :	Optional[int]         =							torch.Generator(device='''cpu'''		).manual_seed(0		)
					A__  :	Tuple         =							pipe('''anime turle'''				,       generator=A_				,       output_type='''np'''		)

					A__  :	str         =							output.images[0]

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

					assert_mean_pixel_difference(A_				,       A_		)





	def   __snake_case				(			self		):
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					A__  :	Union[str, Any]         =							StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''				,       torch_dtype=torch.floataa		)
					A__  :	List[Any]         =							pipe.to(A_		)
					pipe.set_progress_bar_config(disable=A_		)
					pipe.enable_attention_slicing()
					pipe.enable_sequential_cpu_offload()

					A__  :	List[Any]         =							pipe(
					    '''anime turtle'''				,       prior_num_inference_steps=2				,       num_inference_steps=2				,       output_type='''np'''				,       )

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

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

_SCREAMING_SNAKE_CASE    :      Dict          =			{
    'gpt-neox-20b': 2_0_4_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__=None				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__="<|endoftext|>"				,       UpperCamelCase__=False				,       **UpperCamelCase__				,       ):
					super().__init__(
					    UpperCamelCase__				,       UpperCamelCase__				,       tokenizer_file=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       bos_token=UpperCamelCase__				,       eos_token=UpperCamelCase__				,       add_prefix_space=UpperCamelCase__				,       **UpperCamelCase__				,       )

					A__  :	Optional[Any]         =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
					if pre_tok_state.get('''add_prefix_space'''				,       UpperCamelCase__		) != add_prefix_space:
									A__  :	Union[str, Any]         =							getattr(UpperCamelCase__				,       pre_tok_state.pop('''type'''		)		)
									A__  :	List[Any]         =							add_prefix_space
									A__  :	Any         =							pre_tok_class(**UpperCamelCase__		)

					A__  :	List[Any]         =							add_prefix_space





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					A__  :	Any         =							self._tokenizer.model.save(UpperCamelCase__				,       name=UpperCamelCase__		)
					return tuple(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							[]
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(UpperCamelCase__				,       add_special_tokens=UpperCamelCase__		) + [self.eos_token_id]		)

					if len(UpperCamelCase__		) > self.model_max_length:
									A__  :	Tuple         =							input_ids[-self.model_max_length :]
					return input_ids