
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


lowercase__						:List[Any]					    =       logging.get_logger(__name__)

lowercase__						:Tuple					    =       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase__						:Tuple					    =       {
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

lowercase__						:Tuple					    =       {
    "openai-gpt": 512,
}







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Tuple			   =VOCAB_FILES_NAMES
							lowercase_			:			List[Any]			   =PRETRAINED_VOCAB_FILES_MAP
							lowercase_			:			Union[str, Any]			   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_			:			int			   =['''input_ids''', '''attention_mask''']
							lowercase_			:			Optional[int]			   =OpenAIGPTTokenizer




							def __init__(							self  ,A__=None  ,A__=None  ,A__=None  ,A__="<unk>"  ,**A__):
												super().__init__(A__  ,A__  ,tokenizer_file=A__  ,unk_token=A__  ,**A__)




							@property
							def 					A__ (							self):
												return True





							def 					A__ (							self  ,A__  ,A__ = None):
												lowercase		      =		self._tokenizer.model.save(A__  ,name=A__)
												return tuple(A__)






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 							lowercase						(   unittest.TestCase						):




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

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

												lowercase		      =		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(A__  ,A__)




