



"""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 							lowerCamelCase    (     _lowerCAmelCase       ,		unittest.TestCase							):






					'''simple docstring'''





					_A						:     List[str]		=	UnCLIPPipeline
					_A						:     Optional[int]		=	TEXT_TO_IMAGE_PARAMS - {
					    """negative_prompt""",
					    """height""",
					    """width""",
					    """negative_prompt_embeds""",
					    """guidance_scale""",
					    """prompt_embeds""",
					    """cross_attention_kwargs""",
					}
					_A						:     Tuple		=	TEXT_TO_IMAGE_BATCH_PARAMS
					_A						:     str		=	[
					    """generator""",
					    """return_dict""",
					    """prior_num_inference_steps""",
					    """decoder_num_inference_steps""",
					    """super_res_num_inference_steps""",
					]
					_A						:     List[str]		=	False






					@property
					def   lowerCAmelCase_							(			self:				Dict		)  ->      Union[str, Any]:
											return 32






					@property
					def   lowerCAmelCase_							(			self:				Union[str, Any]		)  ->      str:
											return 32






					@property
					def   lowerCAmelCase_							(			self:				int		)  ->      str:
											return self.time_input_dim






					@property
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      str:
											return self.time_input_dim * 4






					@property
					def   lowerCAmelCase_							(			self:				str		)  ->      Dict:
											return 100






					@property
					def   lowerCAmelCase_							(			self:				Any		)  ->      str:
											snake_case_					:int		=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)
											return tokenizer






					@property
					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      Optional[int]:
											torch.manual_seed(0		)
											snake_case_					:List[str]		=     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=1_000		,	)
											return CLIPTextModelWithProjection(snake_case		)






					@property
					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Union[str, Any]:
											torch.manual_seed(0		)

											snake_case_					:List[str]		=     {
											    """num_attention_heads""": 2,
											    """attention_head_dim""": 12,
											    """embedding_dim""": self.text_embedder_hidden_size,
											    """num_layers""": 1,
											}

											snake_case_					:List[str]		=     PriorTransformer(**snake_case		)
											return model






					@property
					def   lowerCAmelCase_							(			self:				Dict		)  ->      Dict:
											torch.manual_seed(0		)

											snake_case_					:str		=     {
											    """clip_embeddings_dim""": self.text_embedder_hidden_size,
											    """time_embed_dim""": self.time_embed_dim,
											    """cross_attention_dim""": self.cross_attention_dim,
											}

											snake_case_					:str		=     UnCLIPTextProjModel(**snake_case		)
											return model






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

											snake_case_					:List[str]		=     {
											    """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_					:Union[str, Any]		=     UNetaDConditionModel(**snake_case		)
											return model






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

											snake_case_					:Any		=     UNetaDModel(**self.dummy_super_res_kwargs		)
											return model






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

											snake_case_					:Dict		=     UNetaDModel(**self.dummy_super_res_kwargs		)
											return model






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

											snake_case_					:str		=     UnCLIPScheduler(
											    variance_type="""fixed_small_log"""		,	prediction_type="""sample"""		,	num_train_timesteps=1_000		,	clip_sample_range=5.0		,	)

											snake_case_					:Optional[Any]		=     UnCLIPScheduler(
											    variance_type="""learned_range"""		,	prediction_type="""epsilon"""		,	num_train_timesteps=1_000		,	)

											snake_case_					:List[Any]		=     UnCLIPScheduler(
											    variance_type="""fixed_small_log"""		,	prediction_type="""epsilon"""		,	num_train_timesteps=1_000		,	)

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

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

											snake_case_					:List[str]		=     self.pipeline_class(**snake_case		)
											snake_case_					:List[Any]		=     pipe.to(snake_case		)

											pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_					:Union[str, Any]		=     pipe(**self.get_dummy_inputs(snake_case		)		)
											snake_case_					:Optional[int]		=     output.images

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

											snake_case_					:str		=     image[0, -3:, -3:, -1]
											snake_case_					:int		=     image_from_tuple[0, -3:, -3:, -1]

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

											snake_case_					:List[Any]		=     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:				List[str]		)  ->      str:
											snake_case_					:Optional[Any]		=     torch.device("""cpu"""		)
											class 							lowerCamelCase    :






																'''simple docstring'''





																_A						:     Any		=	1

											snake_case_					:List[str]		=     self.get_dummy_components()

											snake_case_					:Tuple		=     self.pipeline_class(**snake_case		)
											snake_case_					:int		=     pipe.to(snake_case		)

											snake_case_					:Optional[int]		=     components["""prior"""]
											snake_case_					:str		=     components["""decoder"""]
											snake_case_					:Any		=     components["""super_res_first"""]
											snake_case_					:Dict		=     components["""tokenizer"""]
											snake_case_					:str		=     components["""text_encoder"""]

											snake_case_					:Union[str, Any]		=     torch.Generator(device=snake_case		).manual_seed(0		)
											snake_case_					:List[Any]		=     prior.dtype
											snake_case_					:Dict		=     1

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

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

											pipe.set_progress_bar_config(disable=snake_case		)

											snake_case_					:Union[str, Any]		=     """this is a prompt example"""

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

											snake_case_					:str		=     tokenizer(
											    snake_case		,	padding="""max_length"""		,	max_length=tokenizer.model_max_length		,	return_tensors="""pt"""		,	)
											snake_case_					:Optional[int]		=     text_encoder(text_inputs.input_ids		)
											snake_case_					:Optional[int]		=     text_inputs.attention_mask

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

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






					@skip_mps
					def   lowerCAmelCase_							(			self:				Tuple		)  ->      Optional[int]:
											snake_case_					:Any		=     torch_device == """cpu"""
											snake_case_					:Union[str, Any]		=     True
											snake_case_					:Optional[int]		=     [
											    """prior_num_inference_steps""",
											    """decoder_num_inference_steps""",
											    """super_res_num_inference_steps""",
											]

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






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Dict:
											snake_case_					:Union[str, Any]		=     [
											    """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_					:Union[str, Any]		=     [2, 3]
																	self._test_inference_batch_consistent(
																	    batch_sizes=snake_case		,	additional_params_copy_to_batched_inputs=snake_case		,	)
											else:
																	self._test_inference_batch_consistent(
																	    additional_params_copy_to_batched_inputs=snake_case		)






					@skip_mps
					def   lowerCAmelCase_							(			self:				int		)  ->      Tuple:
											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def   lowerCAmelCase_							(			self:				Dict		)  ->      Optional[Any]:
											return super().test_save_load_local()






					@skip_mps
					def   lowerCAmelCase_							(			self:				Optional[Any]		)  ->      Optional[Any]:
											return super().test_save_load_optional_components()

@nightly
class 							lowerCamelCase    (     unittest.TestCase							):






					'''simple docstring'''





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






					def   lowerCAmelCase_							(			self:				Optional[int]		)  ->      Union[str, Any]:
											snake_case_					:Union[str, Any]		=     load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/unclip/karlo_v1_alpha_horse_cpu.npy"""		)

											snake_case_					:Optional[Any]		=     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""		)
											pipeline.set_progress_bar_config(disable=snake_case		)

											snake_case_					:str		=     torch.manual_seed(0		)
											snake_case_					:Optional[Any]		=     pipeline(
											    """horse"""		,	num_images_per_prompt=1		,	generator=snake_case		,	output_type="""np"""		,	)

											snake_case_					:str		=     output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image		).max() < 1E-1

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






					'''simple docstring'''





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






					def   lowerCAmelCase_							(			self:				List[str]		)  ->      Tuple:
											snake_case_					:Union[str, Any]		=     load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/unclip/karlo_v1_alpha_horse_fp16.npy"""		)

											snake_case_					:Dict		=     UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""		,	torch_dtype=torch.floataa		)
											snake_case_					:Union[str, Any]		=     pipeline.to(snake_case		)
											pipeline.set_progress_bar_config(disable=snake_case		)

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

											snake_case_					:Any		=     output.images[0]

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

											assert_mean_pixel_difference(snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				List[Any]		)  ->      int:
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

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

											snake_case_					:Union[str, Any]		=     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_					:List[str]		=     torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 10**9







'''simple docstring'''


import sys

from transformers import AutoTokenizer


_lowerCamelCase					:						Any					     =							sys.argv[1]
_lowerCamelCase					:						Union[str, Any]					     =							sys.argv[2]
_lowerCamelCase					:						Optional[Any]					     =							int(sys.argv[3])

_lowerCamelCase					:						Union[str, Any]					     =							0

_lowerCamelCase					:						Tuple					     =							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
				for line in f_p:
								_lowerCamelCase					:						Optional[int]					     =							line.rstrip()

								if not line:
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							0
												continue

								_lowerCamelCase					:						int					     =							line.split()[0]

								_lowerCamelCase					:						Union[str, Any]					     =							len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('')
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

