


'''simple docstring'''






import json
import os
from typing import Dict, List, Optional, Tuple

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


a_					:    Optional[Any]          =    logging.get_logger(__name__)


a_					:    List[str]          =    {
    "vocab_file": "vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
    "merges_file": "merges.txt",
}

a_					:    Dict          =    {
    "vocab_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"
        ),
    },
    "tokenizer_config_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"
        ),
    },
    "merges_file": {
        "facebook/s2t-wav2vec2-large-en-de": (
            "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"
        ),
    },
}

a_					:    int          =    "</w>"
a_					:    Tuple          =    "@@ "






def 	_A				(lowerCAmelCase__  :Dict		)   ->  Optional[Any]:




     '''simple docstring'''



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


# Speech2Text2 has no max input length
a_					:    List[Any]          =    {"facebook/s2t-wav2vec2-large-en-de": 1_0_2_4}






class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	VOCAB_FILES_NAMES
 _lowerCAmelCase 					=	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase 					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase 					=	["""input_ids""", """attention_mask"""]
 def __init__( self      ,				__magic_name__      ,				__magic_name__="<s>"      ,				__magic_name__="<pad>"      ,				__magic_name__="</s>"      ,				__magic_name__="<unk>"      ,				__magic_name__=False      ,				__magic_name__=None      ,				**__magic_name__      ,				)					->							int:
      super().__init__(
          unk_token=__magic_name__      ,				bos_token=__magic_name__      ,				eos_token=__magic_name__      ,				pad_token=__magic_name__      ,				do_lower_case=__magic_name__      ,				**__magic_name__      ,				)

      _a			      =							do_lower_case

      with open(__magic_name__      ,				encoding='utf-8'						) as vocab_handle:
           _a			      =							json.load(__magic_name__						)
      _a			      =							{v: k for k, v in self.encoder.items()}

      if merges_file is None:
           logger.info(f'No merges files provided. {self.__class__.__name__} can only be used for decoding.'						)

           _a			      =							None
           _a			      =							None
      else:
           with open(__magic_name__      ,				encoding='utf-8'						) as merges_handle:
                _a			      =							merges_handle.read().split('\n'						)[:-1]

           _a			      =							[tuple(merge.split()[:2]						) for merge in merges]
           _a			      =							dict(zip(__magic_name__      ,				range(len(__magic_name__						)						)						)						)
           _a			      =							{}
 @property
 def 							__UpperCAmelCase		( self						)					->							int:
      return len(self.decoder						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      return dict(self.encoder      ,				**self.added_tokens_encoder						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Any:
      _a			      =							tuple(token[:-1]						) + (token[-1] + BPE_TOKEN_MERGES,)
      if token in self.cache:
           return self.cache[token]
      _a			      =							get_pairs(__magic_name__						)

      if not pairs:
           return token

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

                if word[i] == first and i < len(__magic_name__						) - 1 and word[i + 1] == second:
                     new_word.append(first + second						)
                     i += 2
                else:
                     new_word.append(word[i]						)
                     i += 1
           _a			      =							tuple(__magic_name__						)
           _a			      =							new_word
           if len(__magic_name__						) == 1:
                break
           else:
                _a			      =							get_pairs(__magic_name__						)
      _a			      =							' '.join(__magic_name__						)
      if word == "\n  " + BPE_TOKEN_MERGES:
           _a			      =							'\n' + BPE_TOKEN_MERGES

      if word.endswith(__magic_name__						):
           _a			      =							word.replace(__magic_name__      ,				''						)

      _a			      =							word.replace(' '      ,				__magic_name__						)
      _a			      =							word
      return word
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:

      if self.bpe_ranks is None:
           raise ValueError(
               'This tokenizer was instantiated without a `merges.txt` file, so'
               ' that it can only be used for decoding, not for encoding.'
               'Make sure to provide `merges.txt` file at instantiation to enable '
               'encoding.'						)

      if self.do_lower_case:
           _a			      =							text.lower()

      _a			      =							text.split()

      _a			      =							[]
      for token in text:
           if token:
                split_tokens.extend(list(self.bpe(__magic_name__						).split(' '						)						)						)

      return split_tokens
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							int:
      return self.encoder.get(__magic_name__      ,				self.encoder.get(self.unk_token						)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							str:
      _a			      =							self.decoder.get(__magic_name__      ,				self.unk_token						)
      return result
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							str:
      _a			      =							' '.join(__magic_name__						)

      # make sure @@ tokens are concatenated
      _a			      =							''.join(string.split(__magic_name__						)						)

      return string




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

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

      _a			      =							0
      if self.bpe_ranks is None:
           return (vocab_file,)

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

      return (vocab_file, merges_file)





'''simple docstring'''






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  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	DiTPipeline
 _lowerCAmelCase 					=	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
 _lowerCAmelCase 					=	PipelineTesterMixin.required_optional_params - {
     """latents""",
     """num_images_per_prompt""",
     """callback""",
     """callback_steps""",
 }
 _lowerCAmelCase 					=	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self						)					->							Dict:
      torch.manual_seed(0						)
      _a			      =							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=__magic_name__      ,				activation_fn='gelu-approximate'      ,				num_embeds_ada_norm=10_00      ,				norm_type='ada_norm_zero'      ,				norm_elementwise_affine=__magic_name__      ,				)
      _a			      =							AutoencoderKL()
      _a			      =							DDIMScheduler()
      _a			      =							{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
      return components
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__=0						)					->							Union[str, Any]:
      if str(__magic_name__						).startswith('mps'						):
           _a			      =							torch.manual_seed(__magic_name__						)
      else:
           _a			      =							torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
      _a			      =							{
          'class_labels': [1],
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }
      return inputs
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							'cpu'

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

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

      self.assertEqual(image.shape      ,				(1, 16, 16, 3)						)
      _a			      =							np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]						)
      _a			      =							np.abs(image_slice.flatten() - expected_slice						).max()
      self.assertLessEqual(__magic_name__      ,				1e-3						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      self._test_inference_batch_single_identical(relax_max_difference=__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							Any:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)





@require_torch_gpu
@slow
class  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							torch.manual_seed(0						)

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

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

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

      for word, image in zip(__magic_name__      ,				__magic_name__						):
           _a			      =							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 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
      _a			      =							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
      pipe.to('cuda'						)

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

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

      for word, image in zip(__magic_name__      ,				__magic_name__						):
           _a			      =							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

