


import json
import os
from typing import Optional, Tuple

import regex as re

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


snake_case							:	Tuple					      =				logging.get_logger(__name__)

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

snake_case							:	Optional[Any]					      =				{
    '''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'''},
}

snake_case							:	Optional[int]					      =				{
    '''ctrl''': 2_56,
}

snake_case							:	Union[str, Any]					      =				{
    '''Pregnancy''': 16_86_29,
    '''Christianity''': 76_75,
    '''Explain''': 10_64_23,
    '''Fitness''': 6_34_40,
    '''Saving''': 6_31_63,
    '''Ask''': 2_71_71,
    '''Ass''': 9_59_85,
    '''Joke''': 16_35_09,
    '''Questions''': 4_56_22,
    '''Thoughts''': 4_96_05,
    '''Retail''': 5_23_42,
    '''Feminism''': 16_43_38,
    '''Writing''': 1_19_92,
    '''Atheism''': 19_22_63,
    '''Netflix''': 4_86_16,
    '''Computing''': 3_96_39,
    '''Opinion''': 4_32_13,
    '''Alone''': 4_49_67,
    '''Funny''': 5_89_17,
    '''Gaming''': 4_03_58,
    '''Human''': 40_88,
    '''India''': 13_31,
    '''Joker''': 7_71_38,
    '''Diet''': 3_62_06,
    '''Legal''': 1_18_59,
    '''Norman''': 49_39,
    '''Tip''': 7_26_89,
    '''Weight''': 5_23_43,
    '''Movies''': 4_62_73,
    '''Running''': 2_34_25,
    '''Science''': 20_90,
    '''Horror''': 3_77_93,
    '''Confession''': 6_05_72,
    '''Finance''': 1_22_50,
    '''Politics''': 1_63_60,
    '''Scary''': 19_19_85,
    '''Support''': 1_26_54,
    '''Technologies''': 3_25_16,
    '''Teenage''': 6_61_60,
    '''Event''': 3_27_69,
    '''Learned''': 6_74_60,
    '''Notion''': 18_27_70,
    '''Wikipedia''': 3_75_83,
    '''Books''': 66_65,
    '''Extract''': 7_60_50,
    '''Confessions''': 10_27_01,
    '''Conspiracy''': 7_59_32,
    '''Links''': 6_36_74,
    '''Narcissus''': 15_04_25,
    '''Relationship''': 5_47_66,
    '''Relationships''': 13_47_96,
    '''Reviews''': 4_16_71,
    '''News''': 42_56,
    '''Translation''': 2_68_20,
    '''multilingual''': 12_84_06,
}






def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							a__					=					set()
							a__					=					word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)       )
														a__					=					char

							a__					=					set(__lowerCAmelCase       )
							return pairs







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[Any]   				=      VOCAB_FILES_NAMES
					UpperCAmelCase__ :   str   				=      PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__ :   str   				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__ :   int   				=      CONTROL_CODES
					def __init__(				self       :List[Any]      ,__snake_case       :str      ,__snake_case       :Dict      ,__snake_case       :Optional[Any]="<unk>"      ,**__snake_case       :str				) ->   List[Any]:
												super().__init__(unk_token=__snake_case      ,**__snake_case				)

												with open(__snake_case      ,encoding='utf-8'				) as vocab_handle:
																			a__					=					json.load(__snake_case				)
												a__					=					{v: k for k, v in self.encoder.items()}
												with open(__snake_case      ,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(__snake_case      ,range(len(__snake_case				)				)				)				)
												a__					=					{}
					@property
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												return len(self.encoder				)
					def 			lowerCamelCase__(				self       :str				) ->   Dict:
												return dict(self.encoder      ,**self.added_tokens_encoder				)
					def 			lowerCamelCase__(				self       :int      ,__snake_case       :Any				) ->   Any:
												if token in self.cache:
																			return self.cache[token]
												a__					=					tuple(__snake_case				)
												a__					=					tuple(list(word[:-1]				) + [word[-1] + '</w>']				)
												a__					=					get_pairs(__snake_case				)

												if not pairs:
																			return token

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

																										if word[i] == first and i < len(__snake_case				) - 1 and word[i + 1] == second:
																																	new_word.append(first + second				)
																																	i += 2
																										else:
																																	new_word.append(word[i]				)
																																	i += 1
																			a__					=					tuple(__snake_case				)
																			a__					=					new_word
																			if len(__snake_case				) == 1:
																										break
																			else:
																										a__					=					get_pairs(__snake_case				)
												a__					=					'@@ '.join(__snake_case				)
												a__					=					word[:-4]
												a__					=					word
												return word
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :Any				) ->   Any:
												a__					=					[]

												a__					=					re.findall(R'\S+\n?'      ,__snake_case				)

												for token in words:
																			split_tokens.extend(list(self.bpe(__snake_case				).split(' '				)				)				)
												return split_tokens
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :str				) ->   Tuple:
												return self.encoder.get(__snake_case      ,self.encoder.get(self.unk_token				)				)
					def 			lowerCamelCase__(				self       :int      ,__snake_case       :Dict				) ->   List[str]:
												return self.decoder.get(__snake_case      ,self.unk_token				)
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :Dict				) ->   str:
												a__					=					' '.join(__snake_case				).replace('@@ '      ,''				).strip()
												return out_string



					def 			lowerCamelCase__(				self       :str      ,__snake_case       :str      ,__snake_case       :Optional[str] = None				) ->   Tuple[str]:
												if not os.path.isdir(__snake_case				):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
																			return
												a__					=					os.path.join(
												    __snake_case      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
												a__					=					os.path.join(
												    __snake_case      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']				)

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

												a__					=					0
												with open(__snake_case      ,'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 __snake_case				: 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(__snake_case				) + '\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 unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 	snake_case_       (unittest.TestCase				):
					def 			lowerCamelCase__(				self       :List[Any]				) ->   str:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
					def 			lowerCamelCase__(				self       :int				) ->   Optional[Any]:
												a__				, a__					=					FlaxControlNetModel.from_pretrained(
												    'lllyasviel/sd-controlnet-canny'      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__				, a__					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    'runwayml/stable-diffusion-v1-5'      ,controlnet=__snake_case      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__					=					controlnet_params

												a__					=					'bird'
												a__					=					jax.device_count()
												a__					=					pipe.prepare_text_inputs([prompts] * num_samples				)

												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'				)
												a__					=					pipe.prepare_image_inputs([canny_image] * num_samples				)

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					pipe(
												    prompt_ids=__snake_case      ,image=__snake_case      ,params=__snake_case      ,prng_seed=__snake_case      ,num_inference_steps=50      ,jit=__snake_case      ,).images
												assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2



					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[Any]:
												a__				, a__					=					FlaxControlNetModel.from_pretrained(
												    'lllyasviel/sd-controlnet-openpose'      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__				, a__					=					FlaxStableDiffusionControlNetPipeline.from_pretrained(
												    'runwayml/stable-diffusion-v1-5'      ,controlnet=__snake_case      ,from_pt=__snake_case      ,dtype=jnp.bfloataa				)
												a__					=					controlnet_params

												a__					=					'Chef in the kitchen'
												a__					=					jax.device_count()
												a__					=					pipe.prepare_text_inputs([prompts] * num_samples				)

												a__					=					load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'				)
												a__					=					pipe.prepare_image_inputs([pose_image] * num_samples				)

												a__					=					jax.random.PRNGKey(0				)
												a__					=					jax.random.split(__snake_case      ,jax.device_count()				)

												a__					=					replicate(__snake_case				)
												a__					=					shard(__snake_case				)
												a__					=					shard(__snake_case				)

												a__					=					pipe(
												    prompt_ids=__snake_case      ,image=__snake_case      ,params=__snake_case      ,prng_seed=__snake_case      ,num_inference_steps=50      ,jit=__snake_case      ,).images
												assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

												a__					=					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
												a__					=					images[0, 2_53:2_56, 2_53:2_56, -1]

												a__					=					jnp.asarray(jax.device_get(image_slice.flatten()				)				)
												a__					=					jnp.array(
												    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]				)
												print(F'output_slice: {output_slice}'				)
												assert jnp.abs(output_slice - expected_slice				).max() < 1E-2




