



"""simple docstring"""






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
		from tokenizers import Tokenizer
		from tokenizers.models import WordPiece

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Optional[Any]	)->   str:
								lowerCamelCase__						:						int						      =list(tokenizer_class.max_model_input_sizes.keys()	)
								for model_name in sa_models[:1]:
											lowerCamelCase__						:						int						      =tokenizer_class.from_pretrained(lowerCamelCase	)
											self.assertIsNotNone(lowerCamelCase	)
											self.assertIsInstance(lowerCamelCase,    lowerCamelCase	)
											self.assertIsInstance(lowerCamelCase,    lowerCamelCase	)

											for special_tok in tokenizer.all_special_tokens:
														self.assertIsInstance(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Optional[Any]						      =tokenizer.convert_tokens_to_ids(lowerCamelCase	)
														self.assertIsInstance(lowerCamelCase,    lowerCamelCase	)



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    BatchEncoding,    lowerCamelCase				:    Optional[Callable] = None	)->   List[Any]:
								lowerCamelCase__						:						List[Any]						      =pickle.dumps(lowerCamelCase	)
								self.assertIsNotNone(lowerCamelCase	)

								lowerCamelCase__						:						Union[str, Any]						      =pickle.loads(lowerCamelCase	)

								# Ensure is_fast is correctly restored
								self.assertEqual(be_restored.is_fast,    be_original.is_fast	)

								# Ensure encodings are potentially correctly restored
								if be_original.is_fast:
											self.assertIsNotNone(be_restored.encodings	)
								else:
											self.assertIsNone(be_restored.encodings	)

								# Ensure the keys are the same
								for original_v, restored_v in zip(be_original.values(),    be_restored.values()	):
											if equal_op:
														self.assertTrue(equal_op(lowerCamelCase,    lowerCamelCase	)	)
											else:
														self.assertEqual(lowerCamelCase,    lowerCamelCase	)



					@slow
					def 						snake_case			(			self				:    Optional[Any]	)->   Tuple:
								self.check_tokenizer_from_pretrained(lowerCamelCase	)



					def 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:
								self.assertEqual(TensorType('''tf'''	),    TensorType.TENSORFLOW	)
								self.assertEqual(TensorType('''pt'''	),    TensorType.PYTORCH	)
								self.assertEqual(TensorType('''np'''	),    TensorType.NUMPY	)



					@require_tokenizers
					def 						snake_case			(			self				:    Union[str, Any]	)->   Dict:
								import numpy as np

								lowerCamelCase__						:						Union[str, Any]						      =BertTokenizer.from_pretrained('''bert-base-cased'''	)
								lowerCamelCase__						:						str						      =BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								# Python no tensor
								with self.subTest('''BatchEncoding (Python, return_tensors=None)'''	):
											self.assert_dump_and_restore(tokenizer_p('''Small example to encode'''	)	)

								with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)'''	):
											self.assert_dump_and_restore(
											    tokenizer_p('''Small example to encode''',    return_tensors=TensorType.NUMPY	),    np.array_equal	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=None)'''	):
											self.assert_dump_and_restore(tokenizer_r('''Small example to encode'''	)	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)'''	):
											self.assert_dump_and_restore(
											    tokenizer_r('''Small example to encode''',    return_tensors=TensorType.NUMPY	),    np.array_equal	)



					@require_tf
					@require_tokenizers
					def 						snake_case			(			self				:    Tuple	)->   Tuple:
								import tensorflow as tf

								def tf_array_equals(lowerCamelCase				:    Optional[Any],    lowerCamelCase				:    Any	):
											return tf.reduce_all(tf.equal(lowerCamelCase,    lowerCamelCase	)	)

								lowerCamelCase__						:						List[str]						      =BertTokenizer.from_pretrained('''bert-base-cased'''	)
								lowerCamelCase__						:						List[Any]						      =BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)'''	):
											self.assert_dump_and_restore(
											    tokenizer_p('''Small example to encode''',    return_tensors=TensorType.TENSORFLOW	),    lowerCamelCase	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''	):
											self.assert_dump_and_restore(
											    tokenizer_r('''Small example to encode''',    return_tensors=TensorType.TENSORFLOW	),    lowerCamelCase	)



					@require_torch
					@require_tokenizers
					def 						snake_case			(			self				:    Union[str, Any]	)->   Union[str, Any]:
								import torch

								lowerCamelCase__						:						Union[str, Any]						      =BertTokenizer.from_pretrained('''bert-base-cased'''	)
								lowerCamelCase__						:						Dict						      =BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)'''	):
											self.assert_dump_and_restore(
											    tokenizer_p('''Small example to encode''',    return_tensors=TensorType.PYTORCH	),    torch.equal	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)'''	):
											self.assert_dump_and_restore(
											    tokenizer_r('''Small example to encode''',    return_tensors=TensorType.PYTORCH	),    torch.equal	)



					@require_tokenizers
					def 						snake_case			(			self				:    Dict	)->   List[str]:
								lowerCamelCase__						:						Tuple						      =BertTokenizer.from_pretrained('''bert-base-cased'''	)
								lowerCamelCase__						:						List[str]						      =BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								with self.subTest('''Python Tokenizer'''	):
											self.assertFalse(tokenizer_p('''Small example to_encode'''	).is_fast	)

								with self.subTest('''Rust Tokenizer'''	):
											self.assertTrue(tokenizer_r('''Small example to_encode'''	).is_fast	)



					@require_tokenizers
					def 						snake_case			(			self				:    Any	)->   int:
								lowerCamelCase__						:						List[Any]						      =BertTokenizerFast.from_pretrained('''bert-base-cased'''	)
								lowerCamelCase__						:						Optional[int]						      =tokenizer_r(['''Test''', '''\xad''', '''test'''],    is_split_into_words=lowerCamelCase	)

								self.assertEqual(encoded.word_to_tokens(0	),    TokenSpan(start=1,    end=2	)	)
								self.assertEqual(encoded.word_to_tokens(1	),    lowerCamelCase	)
								self.assertEqual(encoded.word_to_tokens(2	),    TokenSpan(start=2,    end=3	)	)



					def 						snake_case			(			self				:    Tuple	)->   Any:
								lowerCamelCase__						:						Optional[Any]						      =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								lowerCamelCase__						:						Union[str, Any]						      =batch.convert_to_tensors(tensor_type='''np'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
											lowerCamelCase__						:						Any						      =batch.convert_to_tensors(tensor_type='''np'''	)
								self.assertFalse(len(cs.err	),    msg=F'''should have no warning, but got {cs.err}'''	)

								lowerCamelCase__						:						List[Any]						      =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								lowerCamelCase__						:						Dict						      =batch.convert_to_tensors(tensor_type='''np''',    prepend_batch_axis=lowerCamelCase	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (1,)	)



					@require_torch
					def 						snake_case			(			self				:    int	)->   Dict:
								lowerCamelCase__						:						Tuple						      =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								lowerCamelCase__						:						Tuple						      =batch.convert_to_tensors(tensor_type='''pt'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
											lowerCamelCase__						:						Dict						      =batch.convert_to_tensors(tensor_type='''pt'''	)
								self.assertFalse(len(cs.err	),    msg=F'''should have no warning, but got {cs.err}'''	)

								lowerCamelCase__						:						Optional[Any]						      =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								lowerCamelCase__						:						int						      =batch.convert_to_tensors(tensor_type='''pt''',    prepend_batch_axis=lowerCamelCase	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (1,)	)



					@require_tf
					def 						snake_case			(			self				:    List[Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						Dict						      =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								lowerCamelCase__						:						Optional[Any]						      =batch.convert_to_tensors(tensor_type='''tf'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
											lowerCamelCase__						:						Union[str, Any]						      =batch.convert_to_tensors(tensor_type='''tf'''	)
								self.assertFalse(len(cs.err	),    msg=F'''should have no warning, but got {cs.err}'''	)

								lowerCamelCase__						:						int						      =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								lowerCamelCase__						:						str						      =batch.convert_to_tensors(tensor_type='''tf''',    prepend_batch_axis=lowerCamelCase	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (1,)	)



					@require_flax
					def 						snake_case			(			self				:    Optional[int]	)->   Optional[int]:
								lowerCamelCase__						:						Optional[int]						      =BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								lowerCamelCase__						:						Tuple						      =batch.convert_to_tensors(tensor_type='''jax'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
											lowerCamelCase__						:						Dict						      =batch.convert_to_tensors(tensor_type='''jax'''	)
								self.assertFalse(len(cs.err	),    msg=F'''should have no warning, but got {cs.err}'''	)

								lowerCamelCase__						:						Any						      =BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								lowerCamelCase__						:						str						      =batch.convert_to_tensors(tensor_type='''jax''',    prepend_batch_axis=lowerCamelCase	)
								self.assertEqual(tensor_batch['''inputs'''].shape,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape,    (1,)	)



					def 						snake_case			(			self				:    Optional[Any]	)->   int:
								lowerCamelCase__						:						str						      =[{'''input_ids''': np.array([0, 1, 2]	)}, {'''input_ids''': np.array([0, 1, 2, 3]	)}]
								lowerCamelCase__						:						Optional[Any]						      =BertTokenizer.from_pretrained('''bert-base-cased'''	)

								lowerCamelCase__						:						List[str]						      =tokenizer.pad(lowerCamelCase,    padding=lowerCamelCase	)
								self.assertTrue(isinstance(batch['''input_ids'''],    np.ndarray	)	)
								self.assertEqual(batch['''input_ids'''].tolist(),    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
								lowerCamelCase__						:						Tuple						      =tokenizer.pad(lowerCamelCase,    padding=lowerCamelCase,    return_tensors='''np'''	)
								self.assertTrue(isinstance(batch['''input_ids'''],    np.ndarray	)	)
								self.assertEqual(batch['''input_ids'''].tolist(),    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)



					@require_torch
					def 						snake_case			(			self				:    List[Any]	)->   List[str]:
								import torch

								lowerCamelCase__						:						str						      =[{'''input_ids''': torch.tensor([0, 1, 2]	)}, {'''input_ids''': torch.tensor([0, 1, 2, 3]	)}]
								lowerCamelCase__						:						str						      =BertTokenizer.from_pretrained('''bert-base-cased'''	)

								lowerCamelCase__						:						Union[str, Any]						      =tokenizer.pad(lowerCamelCase,    padding=lowerCamelCase	)
								self.assertTrue(isinstance(batch['''input_ids'''],    torch.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].tolist(),    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
								lowerCamelCase__						:						Optional[Any]						      =tokenizer.pad(lowerCamelCase,    padding=lowerCamelCase,    return_tensors='''pt'''	)
								self.assertTrue(isinstance(batch['''input_ids'''],    torch.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].tolist(),    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)



					@require_tf
					def 						snake_case			(			self				:    Optional[Any]	)->   Union[str, Any]:
								import tensorflow as tf

								lowerCamelCase__						:						str						      =[{'''input_ids''': tf.constant([0, 1, 2]	)}, {'''input_ids''': tf.constant([0, 1, 2, 3]	)}]
								lowerCamelCase__						:						Dict						      =BertTokenizer.from_pretrained('''bert-base-cased'''	)

								lowerCamelCase__						:						int						      =tokenizer.pad(lowerCamelCase,    padding=lowerCamelCase	)
								self.assertTrue(isinstance(batch['''input_ids'''],    tf.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].numpy().tolist(),    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
								lowerCamelCase__						:						Union[str, Any]						      =tokenizer.pad(lowerCamelCase,    padding=lowerCamelCase,    return_tensors='''tf'''	)
								self.assertTrue(isinstance(batch['''input_ids'''],    tf.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].numpy().tolist(),    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)



					@require_tokenizers
					def 						snake_case			(			self				:    Tuple	)->   str:
								lowerCamelCase__						:						Any						      =Tokenizer(WordPiece(unk_token='''[UNK]'''	)	)
								PreTrainedTokenizerFast(tokenizer_object=lowerCamelCase	)




					@require_tokenizers
					def 						snake_case			(			self				:    Optional[Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						str						      =Tokenizer(WordPiece(unk_token='''[UNK]'''	)	)
								with tempfile.TemporaryDirectory() as tmpdirname:
											bert_tokenizer.save(os.path.join(lowerCamelCase,    '''tokenizer.json'''	)	)
											PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowerCamelCase,    '''tokenizer.json'''	)	)









"""simple docstring"""






_lowercase					:					str 		=		{
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

_lowercase					:					Dict 		=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowercase					:					Union[str, Any] 		=		[0 for x in range(len(graph))]
_lowercase					:					Union[str, Any] 		=		[]






def 	snake_case__  (						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			lowerCamelCase__						:						int						      =1
			while stack:
						lowerCamelCase__						:						int						      =stack.pop()
						print(__lowerCamelCase				,						clothes[current_clothing]      )
						order += 1






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

			stack.append(__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Optional[Any]      ):
			"""simple docstring"""
			for v in range(len(__lowerCamelCase      )      ):
						if not visited[v]:
									depth_first_search(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )


if __name__ == "__main__":
		topological_sort(graph, visited)
		print(stack)
		print_stack(stack, clothes)




