
'''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        lowerCAmelCase__      (   __A		,	unittest.TestCase	):
						UpperCamelCase_							: Optional[int]	=		OpenAIGPTTokenizer
						UpperCamelCase_							: List[str]	=		OpenAIGPTTokenizerFast
						UpperCamelCase_							: int	=		True
						UpperCamelCase_							: List[str]	=		False

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								_UpperCamelCase						    =	[
								    '''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>''',
								]
								_UpperCamelCase						    =	dict(zip(a       ,							range(len(a				)				)				)				)
								_UpperCamelCase						    =	['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

								_UpperCamelCase						    =	os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["""vocab_file"""]				)
								_UpperCamelCase						    =	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  A_				(			self       ,							a				)					->      Any:


								'''simple docstring'''

								return "lower newer", "lower newer"

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	OpenAIGPTTokenizer(self.vocab_file       ,							self.merges_file				)

								_UpperCamelCase						    =	'''lower'''
								_UpperCamelCase						    =	['''low''', '''er</w>''']
								_UpperCamelCase						    =	tokenizer.tokenize(a				)
								self.assertListEqual(a       ,							a				)

								_UpperCamelCase						    =	tokens + ['''<unk>''']
								_UpperCamelCase						    =	[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)       ,							a				)

						def  A_				(			self       ,							a=15				)					->      Optional[int]:


								'''simple docstring'''

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

												# Simple input
												_UpperCamelCase						    =	'''This is a simple input'''
												_UpperCamelCase						    =	['''This is a simple input 1''', '''This is a simple input 2''']
												_UpperCamelCase						    =	('''This is a simple input''', '''This is a pair''')
												_UpperCamelCase						    =	[
												    ('''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  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								pass







@require_ftfy
@require_spacy
@require_tokenizers
class        lowerCAmelCase__      (   __A	):
						pass





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		return EnvironmentCommand()







class        lowerCAmelCase__      (   __lowercase	):

						@staticmethod
						def  A_				(			a				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	parser.add_parser("""env"""				)
								download_parser.set_defaults(func=a				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	huggingface_hub.__version__

								_UpperCamelCase						    =	"""not installed"""
								_UpperCamelCase						    =	"""NA"""
								if is_torch_available():
										import torch

										_UpperCamelCase						    =	torch.__version__
										_UpperCamelCase						    =	torch.cuda.is_available()

								_UpperCamelCase						    =	"""not installed"""
								if is_transformers_available():
										import transformers

										_UpperCamelCase						    =	transformers.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_accelerate_available():
										import accelerate

										_UpperCamelCase						    =	accelerate.__version__

								_UpperCamelCase						    =	"""not installed"""
								if is_xformers_available():
										import xformers

										_UpperCamelCase						    =	xformers.__version__

								_UpperCamelCase						    =	{
								    """`diffusers` version""": version,
								    """Platform""": platform.platform(),
								    """Python version""": platform.python_version(),
								    """PyTorch version (GPU?)""": F'{pt_version} ({pt_cuda_available})',
								    """Huggingface_hub version""": hub_version,
								    """Transformers version""": transformers_version,
								    """Accelerate version""": accelerate_version,
								    """xFormers version""": xformers_version,
								    """Using GPU in script?""": """<fill in>""",
								    """Using distributed or parallel set-up in script?""": """<fill in>""",
								}

								print("""\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"""				)
								print(self.format_dict(a				)				)

								return info







						@staticmethod
						def  A_				(			a				)					->      Dict:


								'''simple docstring'''

								return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]				) + "\n"



