



import json
import os
from typing import Optional, Tuple

import regex as re

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


UpperCAmelCase_									=							logging.get_logger(__name__)

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

UpperCAmelCase_									=							{
    "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"},
}

UpperCAmelCase_									=							{
    "ctrl": 2_5_6,
}

UpperCAmelCase_									=							{
    "Pregnancy": 1_6_8_6_2_9,
    "Christianity": 7_6_7_5,
    "Explain": 1_0_6_4_2_3,
    "Fitness": 6_3_4_4_0,
    "Saving": 6_3_1_6_3,
    "Ask": 2_7_1_7_1,
    "Ass": 9_5_9_8_5,
    "Joke": 1_6_3_5_0_9,
    "Questions": 4_5_6_2_2,
    "Thoughts": 4_9_6_0_5,
    "Retail": 5_2_3_4_2,
    "Feminism": 1_6_4_3_3_8,
    "Writing": 1_1_9_9_2,
    "Atheism": 1_9_2_2_6_3,
    "Netflix": 4_8_6_1_6,
    "Computing": 3_9_6_3_9,
    "Opinion": 4_3_2_1_3,
    "Alone": 4_4_9_6_7,
    "Funny": 5_8_9_1_7,
    "Gaming": 4_0_3_5_8,
    "Human": 4_0_8_8,
    "India": 1_3_3_1,
    "Joker": 7_7_1_3_8,
    "Diet": 3_6_2_0_6,
    "Legal": 1_1_8_5_9,
    "Norman": 4_9_3_9,
    "Tip": 7_2_6_8_9,
    "Weight": 5_2_3_4_3,
    "Movies": 4_6_2_7_3,
    "Running": 2_3_4_2_5,
    "Science": 2_0_9_0,
    "Horror": 3_7_7_9_3,
    "Confession": 6_0_5_7_2,
    "Finance": 1_2_2_5_0,
    "Politics": 1_6_3_6_0,
    "Scary": 1_9_1_9_8_5,
    "Support": 1_2_6_5_4,
    "Technologies": 3_2_5_1_6,
    "Teenage": 6_6_1_6_0,
    "Event": 3_2_7_6_9,
    "Learned": 6_7_4_6_0,
    "Notion": 1_8_2_7_7_0,
    "Wikipedia": 3_7_5_8_3,
    "Books": 6_6_6_5,
    "Extract": 7_6_0_5_0,
    "Confessions": 1_0_2_7_0_1,
    "Conspiracy": 7_5_9_3_2,
    "Links": 6_3_6_7_4,
    "Narcissus": 1_5_0_4_2_5,
    "Relationship": 5_4_7_6_6,
    "Relationships": 1_3_4_7_9_6,
    "Reviews": 4_1_6_7_1,
    "News": 4_2_5_6,
    "Translation": 2_6_8_2_0,
    "multilingual": 1_2_8_4_0_6,
}
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple				)->Union[str, Any]:
      _lowerCAmelCase 			=  set()
      _lowerCAmelCase 			=  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)				)
            _lowerCAmelCase 			=  char

      _lowerCAmelCase 			=  set(_SCREAMING_SNAKE_CASE				)
      return pairs





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     VOCAB_FILES_NAMES
   SCREAMING_SNAKE_CASE__							=     PRETRAINED_VOCAB_FILES_MAP
   SCREAMING_SNAKE_CASE__							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   SCREAMING_SNAKE_CASE__							=     CONTROL_CODES





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

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





   @property
   def 	__lowerCAmelCase							(	self			):
         return len(self.encoder			)





   def 	__lowerCAmelCase							(	self			):
         return dict(self.encoder       ,     **self.added_tokens_encoder			)





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

         if not pairs:
               return token

         while True:
               _lowerCAmelCase 			=  min(_lowerCAmelCase       ,     key=lambda _lowerCAmelCase			: self.bpe_ranks.get(_lowerCAmelCase       ,     float('''inf'''			)			)			)
               if bigram not in self.bpe_ranks:
                     break
               _lowerCAmelCase			,   _lowerCAmelCase 			=  bigram
               _lowerCAmelCase 			=  []
               _lowerCAmelCase 			=  0
               while i < len(_lowerCAmelCase			):
                     try:
                           _lowerCAmelCase 			=  word.index(_lowerCAmelCase       ,     _lowerCAmelCase			)
                     except ValueError:
                           new_word.extend(word[i:]			)
                           break
                     else:
                           new_word.extend(word[i:j]			)
                           _lowerCAmelCase 			=  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
               _lowerCAmelCase 			=  tuple(_lowerCAmelCase			)
               _lowerCAmelCase 			=  new_word
               if len(_lowerCAmelCase			) == 1:
                     break
               else:
                     _lowerCAmelCase 			=  get_pairs(_lowerCAmelCase			)
         _lowerCAmelCase 			=  '''@@ '''.join(_lowerCAmelCase			)
         _lowerCAmelCase 			=  word[:-4]
         _lowerCAmelCase 			=  word
         return word





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  []

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

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





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         return self.encoder.get(_lowerCAmelCase       ,     self.encoder.get(self.unk_token			)			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         return self.decoder.get(_lowerCAmelCase       ,     self.unk_token			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  ''' '''.join(_lowerCAmelCase			).replace('''@@ '''       ,     ''''''			).strip()
         return out_string





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None			):
         if not os.path.isdir(_lowerCAmelCase			):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
               return
         _lowerCAmelCase 			=  os.path.join(
             _lowerCAmelCase       ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
         _lowerCAmelCase 			=  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'''			)

         _lowerCAmelCase 			=  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!'''			)
                           _lowerCAmelCase 			=  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 unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     DiTPipeline
   SCREAMING_SNAKE_CASE__							=     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
   SCREAMING_SNAKE_CASE__							=     PipelineTesterMixin.required_optional_params - {
       '''latents''',
       '''num_images_per_prompt''',
       '''callback''',
       '''callback_steps''',
   }
   SCREAMING_SNAKE_CASE__							=     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
   SCREAMING_SNAKE_CASE__							=     False





   def 	__lowerCAmelCase							(	self			):
         torch.manual_seed(0			)
         _lowerCAmelCase 			=  TransformeraDModel(
             sample_size=16       ,     num_layers=2       ,     patch_size=4       ,     attention_head_dim=8       ,     num_attention_heads=2       ,     in_channels=4       ,     out_channels=8       ,     attention_bias=_lowerCAmelCase       ,     activation_fn='''gelu-approximate'''       ,     num_embeds_ada_norm=1_000       ,     norm_type='''ada_norm_zero'''       ,     norm_elementwise_affine=_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  AutoencoderKL()
         _lowerCAmelCase 			=  DDIMScheduler()
         _lowerCAmelCase 			=  {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
         return components





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=0			):
         if str(_lowerCAmelCase			).startswith('''mps'''			):
               _lowerCAmelCase 			=  torch.manual_seed(_lowerCAmelCase			)
         else:
               _lowerCAmelCase 			=  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
         _lowerCAmelCase 			=  {
             '''class_labels''': [1],
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''cpu'''

         _lowerCAmelCase 			=  self.get_dummy_components()
         _lowerCAmelCase 			=  self.pipeline_class(**_lowerCAmelCase			)
         pipe.to(_lowerCAmelCase			)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

         _lowerCAmelCase 			=  self.get_dummy_inputs(_lowerCAmelCase			)
         _lowerCAmelCase 			=  pipe(**_lowerCAmelCase			).images
         _lowerCAmelCase 			=  image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,     (1, 16, 16, 3)			)
         _lowerCAmelCase 			=  np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]			)
         _lowerCAmelCase 			=  np.abs(image_slice.flatten() - expected_slice			).max()
         self.assertLessEqual(_lowerCAmelCase       ,     1E-3			)





   def 	__lowerCAmelCase							(	self			):
         self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase       ,     expected_max_diff=1E-3			)





   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available()       ,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,     )
   def 	__lowerCAmelCase							(	self			):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)






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





   def 	__lowerCAmelCase							(	self			):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  torch.manual_seed(0			)

         _lowerCAmelCase 			=  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''			)
         pipe.to('''cuda'''			)

         _lowerCAmelCase 			=  ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
         _lowerCAmelCase 			=  pipe.get_label_ids(_lowerCAmelCase			)

         _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     num_inference_steps=40       ,     output_type='''np'''			).images

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  load_numpy(
                   F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''			)
               assert np.abs((expected_image - image).max()			) < 1E-2





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''			)
         _lowerCAmelCase 			=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
         pipe.to('''cuda'''			)

         _lowerCAmelCase 			=  ['''vase''', '''umbrella''']
         _lowerCAmelCase 			=  pipe.get_label_ids(_lowerCAmelCase			)

         _lowerCAmelCase 			=  torch.manual_seed(0			)
         _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     num_inference_steps=25       ,     output_type='''np'''			).images

         for word, image in zip(_lowerCAmelCase       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  load_numpy(
                   '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                   F'''/dit/{word}_512.npy'''			)

               assert np.abs((expected_image - image).max()			) < 1E-1