






"""simple docstring"""




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_xglm import XGLMTokenizer
else:
		_a				     =  None


_a				     =  logging.get_logger(__name__)

_a				     =  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_a				     =  {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

_a				     =  {
    '''facebook/xglm-564M''': 2048,
}





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				VOCAB_FILES_NAMES
	UpperCamelCase__								=				PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCamelCase__								=				["input_ids", "attention_mask"]
	UpperCamelCase__								=				XGLMTokenizer

	def __init__(							self	,   UpperCAmelCase=None	,   UpperCAmelCase=None	,   UpperCAmelCase="<s>"	,   UpperCAmelCase="</s>"	,   UpperCAmelCase="</s>"	,   UpperCAmelCase="<s>"	,   UpperCAmelCase="<unk>"	,   UpperCAmelCase="<pad>"	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      7
							_UpperCAmelCase     =      [F"""<madeupword{i}>""" for i in range(self.num_madeup_words   )]

							_UpperCAmelCase     =      kwargs.get('additional_special_tokens'	,   []   )
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    UpperCAmelCase	,   tokenizer_file=UpperCAmelCase	,   bos_token=UpperCAmelCase	,   eos_token=UpperCAmelCase	,   sep_token=UpperCAmelCase	,   cls_token=UpperCAmelCase	,   unk_token=UpperCAmelCase	,   pad_token=UpperCAmelCase	,   **UpperCAmelCase	,   )

							_UpperCAmelCase     =      vocab_file
							_UpperCAmelCase     =      False if not self.vocab_file else True

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							if token_ids_a is None:
													return [self.sep_token_id] + token_ids_a
							_UpperCAmelCase     =      [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							_UpperCAmelCase     =      [self.sep_token_id]

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

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							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(UpperCAmelCase   ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""   )
													return
							_UpperCAmelCase     =      os.path.join(
							    UpperCAmelCase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

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

							return (out_vocab_file,)








import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference





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





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'runwayml/stable-diffusion-v1-5'
							_UpperCAmelCase     =      TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase	,   torch_dtype=torch.floataa   ).to('cuda'   )
							_UpperCAmelCase     =      DDIMScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase     =      torch.Generator(device='cuda'   ).manual_seed(0   )

							_UpperCAmelCase     =      'A bear is playing a guitar on Times Square'
							_UpperCAmelCase     =      pipe(prompt=UpperCAmelCase	,   generator=UpperCAmelCase   ).images

							_UpperCAmelCase     =      load_pt(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'   )

							assert_mean_pixel_difference(UpperCAmelCase	,   UpperCAmelCase   )
