


'''simple docstring'''

import json
import os
from typing import Optional, Tuple

import regex as re

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


lowercase__				     =		logging.get_logger(__name__)

lowercase__				     =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowercase__				     =		{
    "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"},
}

lowercase__				     =		{
    "ctrl": 256,
}

lowercase__				     =		{
    "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 __UpperCamelCase   (			__lowerCamelCase		: int				)      ->     Union[str, Any]:







					'''simple docstring'''






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

					_a						=		set(__lowerCamelCase				)
					return pairs






class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
					UpperCAmelCase 					=				VOCAB_FILES_NAMES
					UpperCAmelCase 					=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase 					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase 					=				CONTROL_CODES






					def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase="<unk>"     ,							**__UpperCamelCase		)       ->    str:
										super().__init__(unk_token=__UpperCamelCase     ,							**__UpperCamelCase		)

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






					@property
					def  a_					(			self		)       ->    List[str]:
										return len(self.encoder		)






					def  a_					(			self		)       ->    List[str]:
										return dict(self.encoder     ,							**self.added_tokens_encoder		)






					def  a_					(			self     ,							__UpperCamelCase		)       ->    List[str]:
										if token in self.cache:
															return self.cache[token]
										_a						=		tuple(__UpperCamelCase		)
										_a						=		tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
										_a						=		get_pairs(__UpperCamelCase		)

										if not pairs:
															return token

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

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






					def  a_					(			self     ,							__UpperCamelCase		)       ->    Union[str, Any]:
										_a						=		[]

										_a						=		re.findall(R"\S+\n?"     ,							__UpperCamelCase		)

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






					def  a_					(			self     ,							__UpperCamelCase		)       ->    Dict:
										return self.encoder.get(__UpperCamelCase     ,							self.encoder.get(self.unk_token		)		)






					def  a_					(			self     ,							__UpperCamelCase		)       ->    Tuple:
										return self.decoder.get(__UpperCamelCase     ,							self.unk_token		)






					def  a_					(			self     ,							__UpperCamelCase		)       ->    Optional[int]:
										_a						=		" ".join(__UpperCamelCase		).replace("@@ "     ,							""		).strip()
										return out_string






					def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None		)       ->    Tuple[str]:
										if not os.path.isdir(__UpperCamelCase		):
															logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
															return
										_a						=		os.path.join(
										    __UpperCamelCase     ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
										_a						=		os.path.join(
										    __UpperCamelCase     ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

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

										_a						=		0
										with open(__UpperCamelCase     ,							"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 __UpperCamelCase		: 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!"		)
																									_a						=		token_index
																				writer.write(" ".join(__UpperCamelCase		) + "\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)





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







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






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






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=50     ,							output_type="numpy"		).images

          _a						=		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a						=		np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

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

