
'''simple docstring'''



import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__A        =     logging.get_logger(__name__)

__A        =     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__A        =     {
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

__A        =     {
    'ctrl': 256,
}

__A        =     {
    'Pregnancy': 168_629,
    'Christianity': 7_675,
    'Explain': 106_423,
    'Fitness': 63_440,
    'Saving': 63_163,
    'Ask': 27_171,
    'Ass': 95_985,
    'Joke': 163_509,
    'Questions': 45_622,
    'Thoughts': 49_605,
    'Retail': 52_342,
    'Feminism': 164_338,
    'Writing': 11_992,
    'Atheism': 192_263,
    'Netflix': 48_616,
    'Computing': 39_639,
    'Opinion': 43_213,
    'Alone': 44_967,
    'Funny': 58_917,
    'Gaming': 40_358,
    'Human': 4_088,
    'India': 1_331,
    'Joker': 77_138,
    'Diet': 36_206,
    'Legal': 11_859,
    'Norman': 4_939,
    'Tip': 72_689,
    'Weight': 52_343,
    'Movies': 46_273,
    'Running': 23_425,
    'Science': 2_090,
    'Horror': 37_793,
    'Confession': 60_572,
    'Finance': 12_250,
    'Politics': 16_360,
    'Scary': 191_985,
    'Support': 12_654,
    'Technologies': 32_516,
    'Teenage': 66_160,
    'Event': 32_769,
    'Learned': 67_460,
    'Notion': 182_770,
    'Wikipedia': 37_583,
    'Books': 6_665,
    'Extract': 76_050,
    'Confessions': 102_701,
    'Conspiracy': 75_932,
    'Links': 63_674,
    'Narcissus': 150_425,
    'Relationship': 54_766,
    'Relationships': 134_796,
    'Reviews': 41_671,
    'News': 4_256,
    'Translation': 26_820,
    'multilingual': 128_406,
}





def 			_A		(				lowercase__						):
						lowercase__							=							set()
						lowercase__							=							word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)						)
												lowercase__							=							char

						lowercase__							=							set(__UpperCAmelCase						)
						return pairs







class       A				(							_SCREAMING_SNAKE_CASE					):
			lowerCamelCase				:				Tuple									=	VOCAB_FILES_NAMES
			lowerCamelCase				:				Any									=	PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase				:				Optional[Any]									=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase				:				Dict									=	CONTROL_CODES



			def __init__(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__="<unk>"							,  **lowerCamelCase__							)      ->		Optional[int]:




									'''simple docstring'''



									super().__init__(unk_token=_lowerCAmelCase							,  **_lowerCAmelCase							)

									with open(_lowerCAmelCase							,  encoding="""utf-8"""							) as vocab_handle:
															lowercase__							=							json.load(_lowerCAmelCase							)
									lowercase__							=							{v: k for k, v in self.encoder.items()}
									with open(_lowerCAmelCase							,  encoding="""utf-8"""							) as merges_handle:
															lowercase__							=							merges_handle.read().split("""\n"""							)[1:-1]
									lowercase__							=							[tuple(merge.split()							) for merge in merges]
									lowercase__							=							dict(zip(_lowerCAmelCase							,  range(len(_lowerCAmelCase							)							)							)							)
									lowercase__							=							{}



			@property
			def A__			(       self							)      ->		List[str]:




									'''simple docstring'''



									return len(self.encoder							)



			def A__			(       self							)      ->		List[str]:




									'''simple docstring'''



									return dict(self.encoder							,  **self.added_tokens_encoder							)



			def A__			(       self							,  lowerCamelCase__							)      ->		Union[str, Any]:




									'''simple docstring'''



									if token in self.cache:
															return self.cache[token]
									lowercase__							=							tuple(_lowerCAmelCase							)
									lowercase__							=							tuple(list(word[:-1]							) + [word[-1] + """</w>"""]							)
									lowercase__							=							get_pairs(_lowerCAmelCase							)

									if not pairs:
															return token

									while True:
															lowercase__							=							min(_lowerCAmelCase							,  key=lambda lowerCamelCase__							: self.bpe_ranks.get(_lowerCAmelCase							,  float("""inf"""							)							)							)
															if bigram not in self.bpe_ranks:
																					break
															lowercase__			,    lowercase__							=							bigram
															lowercase__							=							[]
															lowercase__							=							0
															while i < len(_lowerCAmelCase							):
																					try:
																											lowercase__							=							word.index(_lowerCAmelCase							,  _lowerCAmelCase							)
																					except ValueError:
																											new_word.extend(word[i:]							)
																											break
																					else:
																											new_word.extend(word[i:j]							)
																											lowercase__							=							j

																					if word[i] == first and i < len(_lowerCAmelCase							) - 1 and word[i + 1] == second:
																											new_word.append(first + second							)
																											i += 2
																					else:
																											new_word.append(word[i]							)
																											i += 1
															lowercase__							=							tuple(_lowerCAmelCase							)
															lowercase__							=							new_word
															if len(_lowerCAmelCase							) == 1:
																					break
															else:
																					lowercase__							=							get_pairs(_lowerCAmelCase							)
									lowercase__							=							"""@@ """.join(_lowerCAmelCase							)
									lowercase__							=							word[:-4]
									lowercase__							=							word
									return word



			def A__			(       self							,  lowerCamelCase__							)      ->		Optional[Any]:




									'''simple docstring'''



									lowercase__							=							[]

									lowercase__							=							re.findall(R"""\S+\n?"""							,  _lowerCAmelCase							)

									for token in words:
															split_tokens.extend(list(self.bpe(_lowerCAmelCase							).split(""" """							)							)							)
									return split_tokens



			def A__			(       self							,  lowerCamelCase__							)      ->		str:




									'''simple docstring'''



									return self.encoder.get(_lowerCAmelCase							,  self.encoder.get(self.unk_token							)							)



			def A__			(       self							,  lowerCamelCase__							)      ->		List[str]:




									'''simple docstring'''



									return self.decoder.get(_lowerCAmelCase							,  self.unk_token							)



			def A__			(       self							,  lowerCamelCase__							)      ->		Any:




									'''simple docstring'''



									lowercase__							=							""" """.join(_lowerCAmelCase							).replace("""@@ """							,  """"""							).strip()
									return out_string



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__ = None							)      ->		Any:




									'''simple docstring'''



									if not os.path.isdir(_lowerCAmelCase							):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									lowercase__							=							os.path.join(
									    _lowerCAmelCase							,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
									lowercase__							=							os.path.join(
									    _lowerCAmelCase							,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

									with open(_lowerCAmelCase							,  """w"""							,  encoding="""utf-8"""							) as f:
															f.write(json.dumps(self.encoder							,  indent=2							,  sort_keys=_lowerCAmelCase							,  ensure_ascii=_lowerCAmelCase							) + """\n"""							)

									lowercase__							=							0
									with open(_lowerCAmelCase							,  """w"""							,  encoding="""utf-8"""							) as writer:
															writer.write("""#version: 0.2\n"""							)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,  key=lambda lowerCamelCase__							: kv[1]							):
																					if index != token_index:
																											logger.warning(
																											    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																											    """ Please check that the tokenizer is not corrupted!"""							)
																											lowercase__							=							token_index
																					writer.write(""" """.join(_lowerCAmelCase							) + """\n"""							)
																					index += 1

									return vocab_file, merge_file

			# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
			#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
			#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
			#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
			#     return ''.join(tokens_generated_so_far)












import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







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



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     3
       SCREAMING_SNAKE_CASE_      		=     (32, 32)

       SCREAMING_SNAKE_CASE_      		=     floats_tensor((batch_size, num_channels) + sizes							,							rng=random.Random(0		)		).to(_lowerCAmelCase		)
       return image



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     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							,							)
       return model



 @property
 def  lowerCAmelCase_    (				self    :						Tuple		):
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     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							,							)
       return model



 @property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     RobertaSeriesConfig(
           hidden_size=32							,							project_dim=32							,							intermediate_size=37							,							layer_norm_eps=1E-05							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=5_006							,							)
       return RobertaSeriesModelWithTransformation(_lowerCAmelCase		)



 @property
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       def extract(*_lowerCAmelCase    :						Optional[int]							,							**_lowerCAmelCase    :						str		):





             class 			lowerCamelCase_				:


              '''simple docstring'''







              def __init__(				self    :						str		):
                    SCREAMING_SNAKE_CASE_      		=     torch.ones([0]		)




              def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
                    self.pixel_values.to(_lowerCAmelCase		)
                    return self


             return Out()

       return extract



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_      		=     self.dummy_cond_unet
       SCREAMING_SNAKE_CASE_      		=     PNDMScheduler(skip_prk_steps=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.dummy_vae
       SCREAMING_SNAKE_CASE_      		=     self.dummy_text_encoder
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
       SCREAMING_SNAKE_CASE_      		=     77

       SCREAMING_SNAKE_CASE_      		=     self.dummy_image.to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline(
           unet=_lowerCAmelCase							,							scheduler=_lowerCAmelCase							,							vae=_lowerCAmelCase							,							text_encoder=_lowerCAmelCase							,							tokenizer=_lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							feature_extractor=self.dummy_extractor							,							)
       SCREAMING_SNAKE_CASE_      		=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,							do_normalize=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe.to(_lowerCAmelCase		)
       alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     'A painting of a squirrel eating a burger'
       SCREAMING_SNAKE_CASE_      		=     torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe(
           [prompt]							,							generator=_lowerCAmelCase							,							guidance_scale=6.0							,							num_inference_steps=2							,							output_type='np'							,							image=_lowerCAmelCase							,							)

       SCREAMING_SNAKE_CASE_      		=     output.images

       SCREAMING_SNAKE_CASE_      		=     torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe(
           [prompt]							,							generator=_lowerCAmelCase							,							guidance_scale=6.0							,							num_inference_steps=2							,							output_type='np'							,							image=_lowerCAmelCase							,							return_dict=_lowerCAmelCase							,							)[0]

       SCREAMING_SNAKE_CASE_      		=     image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE_      		=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_      		=     np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-3
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5E-3



 @unittest.skipIf(torch_device != 'cuda'							,							'This test requires a GPU'		)
 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     self.dummy_cond_unet
       SCREAMING_SNAKE_CASE_      		=     PNDMScheduler(skip_prk_steps=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.dummy_vae
       SCREAMING_SNAKE_CASE_      		=     self.dummy_text_encoder
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
       SCREAMING_SNAKE_CASE_      		=     77

       SCREAMING_SNAKE_CASE_      		=     self.dummy_image.to(_lowerCAmelCase		)

       # put models in fp16
       SCREAMING_SNAKE_CASE_      		=     unet.half()
       SCREAMING_SNAKE_CASE_      		=     vae.half()
       SCREAMING_SNAKE_CASE_      		=     bert.half()

       # make sure here that pndm scheduler skips prk
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline(
           unet=_lowerCAmelCase							,							scheduler=_lowerCAmelCase							,							vae=_lowerCAmelCase							,							text_encoder=_lowerCAmelCase							,							tokenizer=_lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							feature_extractor=self.dummy_extractor							,							)
       SCREAMING_SNAKE_CASE_      		=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,							do_normalize=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe.to(_lowerCAmelCase		)
       alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     'A painting of a squirrel eating a burger'
       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe(
           [prompt]							,							generator=_lowerCAmelCase							,							num_inference_steps=2							,							output_type='np'							,							image=_lowerCAmelCase							,							).images

       assert image.shape == (1, 32, 32, 3)




 @unittest.skipIf(torch_device != 'cuda'							,							'This test requires a GPU'		)
 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'		)
       # resize to resolution that is divisible by 8 but not 16 or 32
       SCREAMING_SNAKE_CASE_      		=     init_image.resize((760, 504)		)

       SCREAMING_SNAKE_CASE_      		=     'BAAI/AltDiffusion'
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline.from_pretrained(
           _lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							)
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_      		=     'A fantasy landscape, trending on artstation'

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     pipe(
           prompt=_lowerCAmelCase							,							image=_lowerCAmelCase							,							strength=0.75							,							guidance_scale=7.5							,							generator=_lowerCAmelCase							,							output_type='np'							,							)
       SCREAMING_SNAKE_CASE_      		=     output.images[0]

       SCREAMING_SNAKE_CASE_      		=     image[255:258, 383:386, -1]

       assert image.shape == (504, 760, 3)
       SCREAMING_SNAKE_CASE_      		=     np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2





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


 '''simple docstring'''







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




 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'		)
       SCREAMING_SNAKE_CASE_      		=     init_image.resize((768, 512)		)
       SCREAMING_SNAKE_CASE_      		=     load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'		)

       SCREAMING_SNAKE_CASE_      		=     'BAAI/AltDiffusion'
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline.from_pretrained(
           _lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							)
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_      		=     'A fantasy landscape, trending on artstation'

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     pipe(
           prompt=_lowerCAmelCase							,							image=_lowerCAmelCase							,							strength=0.75							,							guidance_scale=7.5							,							generator=_lowerCAmelCase							,							output_type='np'							,							)
       SCREAMING_SNAKE_CASE_      		=     output.images[0]

       assert image.shape == (512, 768, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image		).max() < 1E-2