



"""simple docstring"""

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		UpperCamelCase__     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					OpenAIGPTTokenizer
						_lowerCAmelCase							=					OpenAIGPTTokenizerFast
						_lowerCAmelCase							=					True
						_lowerCAmelCase							=					False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													__a		:		List[str]									=       [
													    """l""",
													    """o""",
													    """w""",
													    """e""",
													    """r""",
													    """s""",
													    """t""",
													    """i""",
													    """d""",
													    """n""",
													    """w</w>""",
													    """r</w>""",
													    """t</w>""",
													    """lo""",
													    """low""",
													    """er</w>""",
													    """low</w>""",
													    """lowest</w>""",
													    """newer</w>""",
													    """wider</w>""",
													    """<unk>""",
													]
													__a		:		Optional[Any]									=       dict(zip(__A						,     range(len(__A							)							)							)							)
													__a		:		str									=       ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

													__a		:		Optional[Any]									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]							)
													__a		:		List[Any]									=       os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""merges_file"""]							)
													with open(self.vocab_file						,     """w"""							) as fp:
																				fp.write(json.dumps(__A							)							)
													with open(self.merges_file						,     """w"""							) as fp:
																				fp.write("""\n""".join(__A							)							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													return "lower newer", "lower newer"



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       OpenAIGPTTokenizer(self.vocab_file						,     self.merges_file							)

													__a		:		str									=       """lower"""
													__a		:		int									=       ["""low""", """er</w>"""]
													__a		:		List[Any]									=       tokenizer.tokenize(__A							)
													self.assertListEqual(__A						,     __A							)

													__a		:		Dict									=       tokens + ["""<unk>"""]
													__a		:		List[Any]									=       [14, 15, 20]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__A							)						,     __A							)



						def     lowerCAmelCase__(self						,     _lowercase=15							):





													'''simple docstring'''

													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																											__a		:		Tuple									=       self.rust_tokenizer_class.from_pretrained(__A						,     **__A							)

																											# Simple input
																											__a		:		Union[str, Any]									=       """This is a simple input"""
																											__a		:		Optional[Any]									=       ["""This is a simple input 1""", """This is a simple input 2"""]
																											__a		:		Any									=       ("""This is a simple input""", """This is a pair""")
																											__a		:		List[str]									=       [
																											    ("""This is a simple input 1""", """This is a simple input 2"""),
																											    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																											]

																											# Simple input tests
																											self.assertRaises(__A						,     tokenizer_r.encode						,     __A						,     max_length=__A						,     padding="""max_length"""							)

																											# Simple input
																											self.assertRaises(__A						,     tokenizer_r.encode_plus						,     __A						,     max_length=__A						,     padding="""max_length"""							)

																											# Simple input
																											self.assertRaises(
																											    __A						,     tokenizer_r.batch_encode_plus						,     __A						,     max_length=__A						,     padding="""max_length"""						,     )

																											# Pair input
																											self.assertRaises(__A						,     tokenizer_r.encode						,     __A						,     max_length=__A						,     padding="""max_length"""							)

																											# Pair input
																											self.assertRaises(__A						,     tokenizer_r.encode_plus						,     __A						,     max_length=__A						,     padding="""max_length"""							)

																											# Pair input
																											self.assertRaises(
																											    __A						,     tokenizer_r.batch_encode_plus						,     __A						,     max_length=__A						,     padding="""max_length"""						,     )





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass







@require_ftfy
@require_spacy
@require_tokenizers
class SCREAMING_SNAKE_CASE__					(		UpperCamelCase__      ):
						pass





"""simple docstring"""

import torch

from diffusers import DiffusionPipeline




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													super().__init__()

													self.register_modules(unet=_lowercase						,     scheduler=_lowercase							)





						def __call__(self							):





													'''simple docstring'''

													__a		:		Dict									=       torch.randn(
													    (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     )
													__a		:		Optional[Any]									=       1

													__a		:		List[str]									=       self.unet(_lowercase						,     _lowercase							).sample
													__a		:		Union[str, Any]									=       self.scheduler.step(_lowercase						,     _lowercase						,     _lowercase							).prev_sample

													__a		:		Optional[int]									=       scheduler_output - scheduler_output + torch.ones_like(_lowercase							)

													return result

