






"""simple docstring"""
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class _lowerCAmelCase						(    __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				MvpTokenizer
						SCREAMING_SNAKE_CASE_:					str 					=				MvpTokenizerFast
						SCREAMING_SNAKE_CASE_:					List[Any] 					=				True
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				filter_roberta_detectors







						def 			A      (    self							)		->							List[Any]:
										super().setUp()
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    [
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										_SCREAMING_SNAKE_CASE   :		Any       		=    dict(zip(lowerCAmelCase_   ,				range(len(lowerCAmelCase_							)							)							)							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    {'unk_token': '<unk>'}

										_SCREAMING_SNAKE_CASE   :		int       		=    os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['vocab_file']							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['merges_file']							)
										with open(self.vocab_file   ,				'w'   ,				encoding='utf-8'							) as fp:
														fp.write(json.dumps(lowerCAmelCase_							) + '\n'							)
										with open(self.merges_file   ,				'w'   ,				encoding='utf-8'							) as fp:
														fp.write('\n'.join(lowerCAmelCase_							)							)







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							List[Any]:
										kwargs.update(self.special_tokens_map							)
										return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							List[str]:
										kwargs.update(self.special_tokens_map							)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Tuple:
										return "lower newer", "lower newer"







						@cached_property
						def 			A      (    self							)		->							int:
										return MvpTokenizer.from_pretrained('RUCAIBox/mvp'							)







						@cached_property
						def 			A      (    self							)		->							Union[str, Any]:
										return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'							)







						@require_torch
						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    tokenizer(lowerCAmelCase_   ,				max_length=len(lowerCAmelCase_							)   ,				padding=lowerCAmelCase_   ,				return_tensors='pt'							)
														self.assertIsInstance(lowerCAmelCase_   ,				lowerCAmelCase_							)

														self.assertEqual((2, 9)   ,				batch.input_ids.shape							)
														self.assertEqual((2, 9)   ,				batch.attention_mask.shape							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    batch.input_ids.tolist()[0]
														self.assertListEqual(lowerCAmelCase_   ,				lowerCAmelCase_							)
														# Test that special tokens are reset







						@require_torch
						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tokenizer(lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				return_tensors='pt'							)
														# check if input_ids are returned and no labels
														self.assertIn('input_ids'   ,				lowerCAmelCase_							)
														self.assertIn('attention_mask'   ,				lowerCAmelCase_							)
														self.assertNotIn('labels'   ,				lowerCAmelCase_							)
														self.assertNotIn('decoder_attention_mask'   ,				lowerCAmelCase_							)







						@require_torch
						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    [
										    'Summary of the text.',
										    'Another summary.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tokenizer(text_target=lowerCAmelCase_   ,				max_length=3_2   ,				padding='max_length'   ,				return_tensors='pt'							)
														self.assertEqual(3_2   ,				targets['input_ids'].shape[1]							)







						@require_torch
						def 			A      (    self							)		->							Optional[Any]:
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE   :		str       		=    tokenizer(
														    ['I am a small frog' * 1_0_2_4, 'I am a small frog']   ,				padding=lowerCAmelCase_   ,				truncation=lowerCAmelCase_   ,				return_tensors='pt'							)
														self.assertIsInstance(lowerCAmelCase_   ,				lowerCAmelCase_							)
														self.assertEqual(batch.input_ids.shape   ,				(2, 1_0_2_4)							)







						@require_torch
						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    ['A long paragraph for summarization.']
										_SCREAMING_SNAKE_CASE   :		Any       		=    [
										    'Summary of the text.',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    tokenizer(lowerCAmelCase_   ,				text_target=lowerCAmelCase_   ,				return_tensors='pt'							)
														_SCREAMING_SNAKE_CASE   :		int       		=    inputs['input_ids']
														_SCREAMING_SNAKE_CASE   :		int       		=    inputs['labels']
														self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
														self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
														self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
														self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)







						def 			A      (    self							)		->							Optional[Any]:
										pass







						def 			A      (    self							)		->							Optional[Any]:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																		_SCREAMING_SNAKE_CASE   :		int       		=    self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_   ,				**lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.tokenizer_class.from_pretrained(lowerCAmelCase_   ,				**lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		int       		=    'A, <mask> AllenNLP sentence.'
																		_SCREAMING_SNAKE_CASE   :		Any       		=    tokenizer_r.encode_plus(lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_   ,				return_token_type_ids=lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Any       		=    tokenizer_p.encode_plus(lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_   ,				return_token_type_ids=lowerCAmelCase_							)

																		# token_type_ids should put 0 everywhere
																		self.assertEqual(sum(tokens_r['token_type_ids']							)   ,				sum(tokens_p['token_type_ids']							)							)

																		# attention_mask should put 1 everywhere, so sum over length should be 1
																		self.assertEqual(
																		    sum(tokens_r['attention_mask']							) / len(tokens_r['attention_mask']							)   ,				sum(tokens_p['attention_mask']							) / len(tokens_p['attention_mask']							)   ,				)

																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']							)
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']							)

																		# Rust correctly handles the space before the mask while python doesnt
																		self.assertSequenceEqual(tokens_p['input_ids']   ,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)
																		self.assertSequenceEqual(tokens_r['input_ids']   ,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]							)

																		self.assertSequenceEqual(
																		    lowerCAmelCase_   ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)
																		self.assertSequenceEqual(
																		    lowerCAmelCase_   ,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)









"""simple docstring"""
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 				lowercase__		(					lowerCamelCase       ):
				return EnvironmentCommand()






class _lowerCAmelCase						(    __UpperCAmelCase       ):







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    parser.add_parser('env'							)
										download_parser.set_defaults(func=lowerCAmelCase_							)







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		int       		=    huggingface_hub.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'not installed'
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'NA'
										if is_torch_available():
														import torch

														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.__version__
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.cuda.is_available()

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'not installed'
										if is_transformers_available():
														import transformers

														_SCREAMING_SNAKE_CASE   :		Tuple       		=    transformers.__version__

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'not installed'
										if is_accelerate_available():
														import accelerate

														_SCREAMING_SNAKE_CASE   :		str       		=    accelerate.__version__

										_SCREAMING_SNAKE_CASE   :		str       		=    'not installed'
										if is_xformers_available():
														import xformers

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    xformers.__version__

										_SCREAMING_SNAKE_CASE   :		Any       		=    {
										    '`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(lowerCAmelCase_							)							)

										return info







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							int:
										return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]							) + "\n"

