'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCamelCase_      	=	None


UpperCamelCase_      	=	logging.get_logger(__name__)


UpperCamelCase_      	=	'''▁'''

UpperCamelCase_      	=	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_      	=	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

UpperCamelCase_      	=	{
    '''google/pegasus-xsum''': 5_12,
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= VOCAB_FILES_NAMES
						A_			:							str     		= PRETRAINED_VOCAB_FILES_MAP
						A_			:							List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							int     		= PegasusTokenizer
						A_			:							Dict     		= ['input_ids', 'attention_mask']




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Union[str, Any]="<pad>"							,							UpperCamelCase_ :       List[Any]="</s>"							,							UpperCamelCase_ :       Tuple="<unk>"							,							UpperCamelCase_ :       List[Any]="<mask_2>"							,							UpperCamelCase_ :       int="<mask_1>"							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Optional[Any]=1_03							,							**UpperCamelCase_ :       Optional[Any]							,							)     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = offset

													if additional_special_tokens is not None:
																				if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																											raise TypeError(
																											    f'''additional_special_tokens should be of type {type(UpperCamelCase_      )}, but is'''
																											    f''' {type(UpperCamelCase_      )}'''      )

																				SCREAMING_SNAKE_CASE__     :List[str]						    = (
																				    ([mask_token_sent] + additional_special_tokens)
																				    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																				    else additional_special_tokens
																				)
																				# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																				additional_special_tokens_extended += [
																				    f'''<unk_{i}>''' for i in range(len(UpperCamelCase_      )							,							self.offset - 1      )
																				]

																				if len(set(UpperCamelCase_      )      ) != len(UpperCamelCase_      ):
																											raise ValueError(
																											    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = additional_special_tokens_extended
													else:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = [mask_token_sent] if mask_token_sent is not None else []
																				additional_special_tokens += [f'''<unk_{i}>''' for i in range(2							,							self.offset      )]

													super().__init__(
													    UpperCamelCase_							,							tokenizer_file=UpperCamelCase_							,							pad_token=UpperCamelCase_							,							eos_token=UpperCamelCase_							,							unk_token=UpperCamelCase_							,							mask_token=UpperCamelCase_							,							mask_token_sent=UpperCamelCase_							,							offset=UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :int						    = vocab_file
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False if not self.vocab_file else True




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :int						    = set(self.all_special_ids      )  # call it once instead of inside list comp
													all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

													if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
																				raise ValueError(
																				    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
																				    f''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

													return [1 if x in all_special_ids else 0 for x in seq]




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       List							,							UpperCamelCase_ :       Optional[List] = None							,							UpperCamelCase_ :       bool = False      )     ->				List[int]:
													if already_has_special_tokens:
																				return self._special_token_mask(UpperCamelCase_      )
													elif token_ids_a is None:
																				return self._special_token_mask(UpperCamelCase_      ) + [1]
													else:
																				return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Dict=None      )     ->				List[int]:
													if token_ids_a is None:
																				return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[str] = None      )     ->				Tuple[str]:
													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																				    'tokenizer.'      )

													if not os.path.isdir(UpperCamelCase_      ):
																				logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
																				return
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = os.path.join(
													    UpperCamelCase_							,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

													if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase_      ):
																				copyfile(self.vocab_file							,							UpperCamelCase_      )

													return (out_vocab_file,)







'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Any     		= LDMTextToImagePipeline
						A_			:							Dict     		= TEXT_TO_IMAGE_PARAMS - {
						    'negative_prompt',
						    'negative_prompt_embeds',
						    'cross_attention_kwargs',
						    'prompt_embeds',
						}
						A_			:							Optional[Any]     		= PipelineTesterMixin.required_optional_params - {
						    'num_images_per_prompt',
						    'callback',
						    'callback_steps',
						}
						A_			:							Any     		= TEXT_TO_IMAGE_BATCH_PARAMS
						A_			:							Optional[int]     		= False




						def      __lowerCamelCase       (    self :       str      )     ->				List[str]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = UNetaDConditionModel(
													    block_out_channels=(32, 64)							,							layers_per_block=2							,							sample_size=32							,							in_channels=4							,							out_channels=4							,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,							cross_attention_dim=32							,							)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = DDIMScheduler(
													    beta_start=0.0_0085							,							beta_end=0.012							,							beta_schedule='scaled_linear'							,							clip_sample=UpperCamelCase_							,							set_alpha_to_one=UpperCamelCase_							,							)
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = AutoencoderKL(
													    block_out_channels=(32, 64)							,							in_channels=3							,							out_channels=3							,							down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')							,							up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')							,							latent_channels=4							,							)
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = CLIPTextConfig(
													    bos_token_id=0							,							eos_token_id=2							,							hidden_size=32							,							intermediate_size=37							,							layer_norm_eps=1e-05							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=10_00							,							)
													SCREAMING_SNAKE_CASE__     :List[str]						    = CLIPTextModel(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = {
													    'unet': unet,
													    'scheduler': scheduler,
													    'vqvae': vae,
													    'bert': text_encoder,
													    'tokenizer': tokenizer,
													}
													return components




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str=0      )     ->				List[Any]:
													if str(UpperCamelCase_      ).startswith('mps'      ):
																				SCREAMING_SNAKE_CASE__     :int						    = torch.manual_seed(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :Dict						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'generator': generator,
													    'num_inference_steps': 2,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 'cpu'  # ensure determinism for the device-dependent torch.Generator

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.get_dummy_components()
													SCREAMING_SNAKE_CASE__     :Tuple						    = LDMTextToImagePipeline(**UpperCamelCase_      )
													pipe.to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = self.get_dummy_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :int						    = image[0, -3:, -3:, -1]

													assert image.shape == (1, 16, 16, 3)
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3




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




						def      __lowerCamelCase       (    self :       str      )     ->				Union[str, Any]:
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Union[str, Any]							,							UpperCamelCase_ :       Optional[Any]=torch.floataa							,							UpperCamelCase_ :       int=0      )     ->				str:
													SCREAMING_SNAKE_CASE__     :str						    = torch.manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.random.RandomState(UpperCamelCase_      ).standard_normal((1, 4, 32, 32)      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.from_numpy(UpperCamelCase_      ).to(device=UpperCamelCase_							,							dtype=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'latents': latents,
													    'generator': generator,
													    'num_inference_steps': 3,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe(**UpperCamelCase_      ).images
													SCREAMING_SNAKE_CASE__     :Tuple						    = image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 2_56, 2_56, 3)
													SCREAMING_SNAKE_CASE__     :str						    = np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.abs(expected_slice - image_slice      ).max()
													assert max_diff < 1e-3




@nightly
@require_torch_gpu
class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Optional[Any]:
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Any=torch.floataa							,							UpperCamelCase_ :       List[str]=0      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.manual_seed(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = np.random.RandomState(UpperCamelCase_      ).standard_normal((1, 4, 32, 32)      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.from_numpy(UpperCamelCase_      ).to(device=UpperCamelCase_							,							dtype=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = {
													    'prompt': 'A painting of a squirrel eating a burger',
													    'latents': latents,
													    'generator': generator,
													    'num_inference_steps': 50,
													    'guidance_scale': 6.0,
													    'output_type': 'numpy',
													}
													return inputs




						def      __lowerCamelCase       (    self :       int      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :str						    = LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'      ).to(UpperCamelCase_      )
													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_inputs(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe(**UpperCamelCase_      ).images[0]

													SCREAMING_SNAKE_CASE__     :List[Any]						    = load_numpy(
													    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'      )
													SCREAMING_SNAKE_CASE__     :str						    = np.abs(expected_image - image      ).max()
													assert max_diff < 1e-3






