
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_pegasus import PegasusTokenizer
else:
      lowercase__       =None


lowercase__       =logging.get_logger(__name__)


lowercase__       ='▁'

lowercase__       ={'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__       ={
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

lowercase__       ={
    'google/pegasus-xsum': 512,
}



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							int	       =      PegasusTokenizer
   _SCREAMING_SNAKE_CASE      :							Tuple	       =      ["input_ids", "attention_mask"]
   def __init__(self :					str					,				snake_case_ :					Dict=None					,				snake_case_ :					List[Any]=None					,				snake_case_ :					List[Any]="<pad>"					,				snake_case_ :					Tuple="</s>"					,				snake_case_ :					Any="<unk>"					,				snake_case_ :					Dict="<mask_2>"					,				snake_case_ :					List[Any]="<mask_1>"					,				snake_case_ :					str=None					,				snake_case_ :					str=1_0_3					,				**snake_case_ :					Optional[int]					,				):
    __a :  Tuple						=  offset

    if additional_special_tokens is not None:
     if not isinstance(snake_case_					,				snake_case_   ):
      raise TypeError(
          f"additional_special_tokens should be of type {type(snake_case_   )}, but is"
          f" {type(snake_case_   )}"   )

     __a :  List[str]						=  (
         ([mask_token_sent] + additional_special_tokens)
         if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
         else additional_special_tokens
     )
     # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
     additional_special_tokens_extended += [
         f"<unk_{i}>" for i in range(len(snake_case_   )					,				self.offset - 1   )
     ]

     if len(set(snake_case_   )   ) != len(snake_case_   ):
      raise ValueError(
          '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
          f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."   )
     __a :  Dict						=  additional_special_tokens_extended
    else:
     __a :  str						=  [mask_token_sent] if mask_token_sent is not None else []
     additional_special_tokens += [f"<unk_{i}>" for i in range(2					,				self.offset   )]

    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				pad_token=snake_case_					,				eos_token=snake_case_					,				unk_token=snake_case_					,				mask_token=snake_case_					,				mask_token_sent=snake_case_					,				offset=snake_case_					,				additional_special_tokens=snake_case_					,				**snake_case_					,				)
    __a :  List[str]						=  vocab_file
    __a :  Tuple						=  False if not self.vocab_file else True
   def       lowerCAmelCase			(self :					List[str]					,				snake_case_ :					Any   ):
    __a :  Optional[Any]						=  set(self.all_special_ids   )  # call it once instead of inside list comp
    all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

    if all_special_ids != set(range(len(self.additional_special_tokens   ) + 3   )   ):
     raise ValueError(
         '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
         f" {len(self.additional_special_tokens   )} additional_special_tokens, but got {all_special_ids}"   )

    return [1 if x in all_special_ids else 0 for x in seq]
   def       lowerCAmelCase			(self :					List[str]					,				snake_case_ :					List					,				snake_case_ :					Optional[List] = None					,				snake_case_ :					bool = False   ):
    if already_has_special_tokens:
     return self._special_token_mask(snake_case_   )
    elif token_ids_a is None:
     return self._special_token_mask(snake_case_   ) + [1]
    else:
     return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]
   def       lowerCAmelCase			(self :					Any					,				snake_case_ :					Tuple					,				snake_case_ :					Optional[int]=None   ):
    if token_ids_a is None:
     return token_ids_a + [self.eos_token_id]
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return token_ids_a + token_ids_a + [self.eos_token_id]







   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    if not self.can_save_slow_tokenizer:
     raise ValueError(
         '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
         '''tokenizer.'''   )

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

    if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case_   ):
     copyfile(self.vocab_file					,				snake_case_   )

    return (out_vocab_file,)


import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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 		UpperCamelCase__  (       unittest.TestCase    ):
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
   def       lowerCAmelCase			(self :					int   ):
    __a				,       __a :  Optional[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        '''stabilityai/stable-diffusion-2'''					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)

    __a :  Any						=  '''A painting of a squirrel eating a burger'''
    __a :  Dict						=  jax.device_count()
    __a :  Optional[int]						=  num_samples * [prompt]
    __a :  Optional[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Optional[Any]						=  replicate(snake_case_   )
    __a :  Optional[int]						=  shard(snake_case_   )

    __a :  int						=  jax.random.PRNGKey(0   )
    __a :  str						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  int						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  Optional[int]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  List[Any]						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  Dict						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  str						=  jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[Any]						=  '''stabilityai/stable-diffusion-2'''
    __a				,       __a :  Dict						=  FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case_					,				subfolder='''scheduler'''   )
    __a				,       __a :  List[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        snake_case_					,				scheduler=snake_case_					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)
    __a :  Union[str, Any]						=  scheduler_params

    __a :  List[Any]						=  '''A painting of a squirrel eating a burger'''
    __a :  Any						=  jax.device_count()
    __a :  Any						=  num_samples * [prompt]
    __a :  List[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Tuple						=  replicate(snake_case_   )
    __a :  Dict						=  shard(snake_case_   )

    __a :  Dict						=  jax.random.PRNGKey(0   )
    __a :  Dict						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  str						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  List[Any]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  Any						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  List[Any]						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  List[Any]						=  jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2
