





import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


snake_case_           =      logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
snake_case_           =      {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

snake_case_           =      {
    'allenai/led-base-16384': 16384,
}






@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def lowerCamelCase__   (							)				->    List[Any]:
			__snake_case								=       (
			    list(range(ord('''!'''	)	,				ord('''~'''	) + 1	)	) + list(range(ord('''¡'''	)	,				ord('''¬'''	) + 1	)	) + list(range(ord('''®'''	)	,				ord('''ÿ'''	) + 1	)	)
			)
			__snake_case								=       bs[:]
			__snake_case								=       0
			for b in range(2**8	):
						if b not in bs:
									bs.append(snake_case_	)
									cs.append(2**8 + n	)
									n += 1
			__snake_case								=       [chr(snake_case_	) for n in cs]
			return dict(zip(snake_case_	,				snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    List[Any]:
			__snake_case								=       set()
			__snake_case								=       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)	)
						__snake_case								=       char
			return pairs







class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
						A_						:    List[Any]							=   VOCAB_FILES_NAMES
						A_						:    List[Any]							=   PRETRAINED_VOCAB_FILES_MAP
						A_						:    str							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_						:    List[Any]							=   ['input_ids', 'attention_mask']


						def __init__(self	:				int    ,      a__	:				List[Any]    ,      a__	:				Dict    ,      a__	:				Optional[Any]="replace"    ,      a__	:				Dict="<s>"    ,      a__	:				Optional[int]="</s>"    ,      a__	:				int="</s>"    ,      a__	:				Optional[int]="<s>"    ,      a__	:				str="<unk>"    ,      a__	:				List[str]="<pad>"    ,      a__	:				Any="<mask>"    ,      a__	:				Union[str, Any]=False    ,      **a__	:				Optional[int]    ,      ):





									"""simple docstring"""



									__snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else bos_token
									__snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else eos_token
									__snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else sep_token
									__snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else cls_token
									__snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else unk_token
									__snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									__snake_case								=       AddedToken(a__    ,      lstrip=a__    ,      rstrip=a__				) if isinstance(a__    ,      a__				) else mask_token

									super().__init__(
									    errors=a__    ,      bos_token=a__    ,      eos_token=a__    ,      unk_token=a__    ,      sep_token=a__    ,      cls_token=a__    ,      pad_token=a__    ,      mask_token=a__    ,      add_prefix_space=a__    ,      **a__    ,      )

									with open(a__    ,      encoding='''utf-8'''				) as vocab_handle:
												__snake_case								=       json.load(a__				)
									__snake_case								=       {v: k for k, v in self.encoder.items()}
									__snake_case								=       errors  # how to handle errors in decoding
									__snake_case								=       bytes_to_unicode()
									__snake_case								=       {v: k for k, v in self.byte_encoder.items()}
									with open(a__    ,      encoding='''utf-8'''				) as merges_handle:
												__snake_case								=       merges_handle.read().split('''\n'''				)[1:-1]
									__snake_case								=       [tuple(merge.split()				) for merge in bpe_merges]
									__snake_case								=       dict(zip(a__    ,      range(len(a__				)				)				)				)
									__snake_case								=       {}
									__snake_case								=       add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									__snake_case								=       re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)


						@property
						# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
						def        a			(self	:				Optional[int]				):





									"""simple docstring"""



									return len(self.encoder				)


						def        a			(self	:				Dict				):





									"""simple docstring"""



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


						def        a			(self	:				Dict    ,      a__	:				str				):





									"""simple docstring"""



									if token in self.cache:
												return self.cache[token]
									__snake_case								=       tuple(a__				)
									__snake_case								=       get_pairs(a__				)

									if not pairs:
												return token

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

															if word[i] == first and i < len(a__				) - 1 and word[i + 1] == second:
																		new_word.append(first + second				)
																		i += 2
															else:
																		new_word.append(word[i]				)
																		i += 1
												__snake_case								=       tuple(a__				)
												__snake_case								=       new_word
												if len(a__				) == 1:
															break
												else:
															__snake_case								=       get_pairs(a__				)
									__snake_case								=       ''' '''.join(a__				)
									__snake_case								=       word
									return word


						def        a			(self	:				str    ,      a__	:				Optional[int]				):





									"""simple docstring"""



									__snake_case								=       []
									for token in re.findall(self.pat    ,      a__				):
												__snake_case								=       ''''''.join(
												    self.byte_encoder[b] for b in token.encode('''utf-8'''				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a__				).split(''' '''				)				)
									return bpe_tokens


						def        a			(self	:				str    ,      a__	:				List[Any]				):





									"""simple docstring"""



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


						def        a			(self	:				Dict    ,      a__	:				Tuple				):





									"""simple docstring"""



									return self.decoder.get(a__				)


						def        a			(self	:				List[str]    ,      a__	:				Any				):





									"""simple docstring"""



									__snake_case								=       ''''''.join(a__				)
									__snake_case								=       bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''    ,      errors=self.errors				)
									return text


						def        a			(self	:				List[Any]    ,      a__	:				str    ,      a__	:				Optional[str] = None				):





									"""simple docstring"""



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

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

									__snake_case								=       0
									with open(a__    ,      '''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 a__				: 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!'''				)
																		__snake_case								=       token_index
															writer.write(''' '''.join(a__				) + '''\n'''				)
															index += 1

									return vocab_file, merge_file


						def        a			(self	:				Optional[Any]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None				):





									"""simple docstring"""



									if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									__snake_case								=       [self.cls_token_id]
									__snake_case								=       [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep


						def        a			(self	:				Optional[int]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None    ,      a__	:				bool = False				):





									"""simple docstring"""



									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=a__    ,      token_ids_a=a__    ,      already_has_special_tokens=a__				)

									if token_ids_a is None:
												return [1] + ([0] * len(a__				)) + [1]
									return [1] + ([0] * len(a__				)) + [1, 1] + ([0] * len(a__				)) + [1]


						def        a			(self	:				Optional[int]    ,      a__	:				List[int]    ,      a__	:				Optional[List[int]] = None				):





									"""simple docstring"""



									__snake_case								=       [self.sep_token_id]
									__snake_case								=       [self.cls_token_id]

									if token_ids_a is None:
												return len(cls + token_ids_a + sep				) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]


						def        a			(self	:				Any    ,      a__	:				Any    ,      a__	:				str=False    ,      **a__	:				Any				):





									"""simple docstring"""



									__snake_case								=       kwargs.pop('''add_prefix_space'''    ,      self.add_prefix_space				)
									if (is_split_into_words or add_prefix_space) and (len(a__				) > 0 and not text[0].isspace()):
												__snake_case								=       ''' ''' + text
									return (text, kwargs)


						def        a			(self	:				List[str]    ,      a__	:				Union[Dict[str, EncodedInput], BatchEncoding]    ,      a__	:				Optional[int] = None    ,      a__	:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,      a__	:				Optional[int] = None    ,      a__	:				Optional[bool] = None    ,      ):





									"""simple docstring"""



									__snake_case								=       super()._pad(
									    encoded_inputs=a__    ,      max_length=a__    ,      padding_strategy=a__    ,      pad_to_multiple_of=a__    ,      return_attention_mask=a__    ,      )

									# Load from model defaults
									if return_attention_mask is None:
												__snake_case								=       '''attention_mask''' in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
												__snake_case								=       encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												__snake_case								=       len(encoded_inputs['''global_attention_mask''']				) != len(a__				)

												if needs_to_be_padded:
															__snake_case								=       len(a__				) - len(encoded_inputs['''global_attention_mask''']				)

															if self.padding_side == "right":
																		# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																		__snake_case								=       (
																		    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																		)
															elif self.padding_side == "left":
																		__snake_case								=       [-1] * difference + encoded_inputs[
																		    '''global_attention_mask'''
																		]
															else:
																		raise ValueError('''Invalid padding strategy:''' + str(self.padding_side				)				)

									return encoded_inputs








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 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				int				):





         """simple docstring"""



         super().tearDown()
         gc.collect()


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-canny'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

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

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

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

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

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2


      def        a			(self	:				Dict				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       FlaxControlNetModel.from_pretrained(
             '''lllyasviel/sd-controlnet-openpose'''    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case	,     __snake_case								=       FlaxStableDiffusionControlNetPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''    ,      controlnet=a__    ,      from_pt=a__    ,      dtype=jnp.bfloataa				)
         __snake_case								=       controlnet_params

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

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

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

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

         __snake_case								=       pipe(
             prompt_ids=a__    ,      image=a__    ,      params=a__    ,      prng_seed=a__    ,      num_inference_steps=50    ,      jit=a__    ,      ).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         __snake_case								=       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
         __snake_case								=       images[0, 253:256, 253:256, -1]

         __snake_case								=       jnp.asarray(jax.device_get(image_slice.flatten()				)				)
         __snake_case								=       jnp.array(
             [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]				)
         print(f"""output_slice: {output_slice}"""				)
         assert jnp.abs(output_slice - expected_slice				).max() < 1E-2

