




'''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      _a       (		unittest.TestCase							):






					def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    int:
										_snake_case							=   list(tokenizer_class.max_model_input_sizes.keys()					)
										for model_name in sa_models[:1]:
															_snake_case							=   tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
															self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)
															self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)
															self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)

															for special_tok in tokenizer.all_special_tokens:
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)
																				_snake_case							=   tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE					)
																				self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)






					def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    str:
										_snake_case							=   pickle.dumps(_SCREAMING_SNAKE_CASE					)
										self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)

										_snake_case							=   pickle.loads(_SCREAMING_SNAKE_CASE					)

										# 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(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)
															else:
																				self.assertEqual(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)






					@slow
					def        _lowercase			(				self					)							->    int:
										self.check_tokenizer_from_pretrained(_SCREAMING_SNAKE_CASE					)






					def        _lowercase			(				self					)							->    int:
										self.assertEqual(TensorType("tf"					)      ,TensorType.TENSORFLOW					)
										self.assertEqual(TensorType("pt"					)      ,TensorType.PYTORCH					)
										self.assertEqual(TensorType("np"					)      ,TensorType.NUMPY					)






					@require_tokenizers
					def        _lowercase			(				self					)							->    int:
										import numpy as np

										_snake_case							=   BertTokenizer.from_pretrained("bert-base-cased"					)
										_snake_case							=   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        _lowercase			(				self					)							->    Any:
										import tensorflow as tf

										def tf_array_equals(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					):
															return tf.reduce_all(tf.equal(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)

										_snake_case							=   BertTokenizer.from_pretrained("bert-base-cased"					)
										_snake_case							=   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					)      ,_SCREAMING_SNAKE_CASE					)

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






					@require_torch
					@require_tokenizers
					def        _lowercase			(				self					)							->    Optional[int]:
										import torch

										_snake_case							=   BertTokenizer.from_pretrained("bert-base-cased"					)
										_snake_case							=   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        _lowercase			(				self					)							->    int:
										_snake_case							=   BertTokenizer.from_pretrained("bert-base-cased"					)
										_snake_case							=   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        _lowercase			(				self					)							->    Dict:
										_snake_case							=   BertTokenizerFast.from_pretrained("bert-base-cased"					)
										_snake_case							=   tokenizer_r(["Test", "\xad", "test"]      ,is_split_into_words=_SCREAMING_SNAKE_CASE					)

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






					def        _lowercase			(				self					)							->    Union[str, Any]:
										_snake_case							=   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
										_snake_case							=   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:
															_snake_case							=   batch.convert_to_tensors(tensor_type="np"					)
										self.assertFalse(len(cs.err					)      ,msg=f"""should have no warning, but got {cs.err}"""					)

										_snake_case							=   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
										_snake_case							=   batch.convert_to_tensors(tensor_type="np"      ,prepend_batch_axis=_SCREAMING_SNAKE_CASE					)
										self.assertEqual(tensor_batch["inputs"].shape      ,(1, 3)					)
										self.assertEqual(tensor_batch["labels"].shape      ,(1,)					)






					@require_torch
					def        _lowercase			(				self					)							->    int:
										_snake_case							=   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
										_snake_case							=   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:
															_snake_case							=   batch.convert_to_tensors(tensor_type="pt"					)
										self.assertFalse(len(cs.err					)      ,msg=f"""should have no warning, but got {cs.err}"""					)

										_snake_case							=   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
										_snake_case							=   batch.convert_to_tensors(tensor_type="pt"      ,prepend_batch_axis=_SCREAMING_SNAKE_CASE					)
										self.assertEqual(tensor_batch["inputs"].shape      ,(1, 3)					)
										self.assertEqual(tensor_batch["labels"].shape      ,(1,)					)






					@require_tf
					def        _lowercase			(				self					)							->    Union[str, Any]:
										_snake_case							=   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
										_snake_case							=   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:
															_snake_case							=   batch.convert_to_tensors(tensor_type="tf"					)
										self.assertFalse(len(cs.err					)      ,msg=f"""should have no warning, but got {cs.err}"""					)

										_snake_case							=   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
										_snake_case							=   batch.convert_to_tensors(tensor_type="tf"      ,prepend_batch_axis=_SCREAMING_SNAKE_CASE					)
										self.assertEqual(tensor_batch["inputs"].shape      ,(1, 3)					)
										self.assertEqual(tensor_batch["labels"].shape      ,(1,)					)






					@require_flax
					def        _lowercase			(				self					)							->    List[Any]:
										_snake_case							=   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}					)
										_snake_case							=   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:
															_snake_case							=   batch.convert_to_tensors(tensor_type="jax"					)
										self.assertFalse(len(cs.err					)      ,msg=f"""should have no warning, but got {cs.err}"""					)

										_snake_case							=   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}					)
										_snake_case							=   batch.convert_to_tensors(tensor_type="jax"      ,prepend_batch_axis=_SCREAMING_SNAKE_CASE					)
										self.assertEqual(tensor_batch["inputs"].shape      ,(1, 3)					)
										self.assertEqual(tensor_batch["labels"].shape      ,(1,)					)






					def        _lowercase			(				self					)							->    str:
										_snake_case							=   [{"input_ids": np.array([0, 1, 2]					)}, {"input_ids": np.array([0, 1, 2, 3]					)}]
										_snake_case							=   BertTokenizer.from_pretrained("bert-base-cased"					)

										_snake_case							=   tokenizer.pad(_SCREAMING_SNAKE_CASE      ,padding=_SCREAMING_SNAKE_CASE					)
										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]]					)
										_snake_case							=   tokenizer.pad(_SCREAMING_SNAKE_CASE      ,padding=_SCREAMING_SNAKE_CASE      ,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        _lowercase			(				self					)							->    Tuple:
										import torch

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

										_snake_case							=   tokenizer.pad(_SCREAMING_SNAKE_CASE      ,padding=_SCREAMING_SNAKE_CASE					)
										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]]					)
										_snake_case							=   tokenizer.pad(_SCREAMING_SNAKE_CASE      ,padding=_SCREAMING_SNAKE_CASE      ,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        _lowercase			(				self					)							->    str:
										import tensorflow as tf

										_snake_case							=   [{"input_ids": tf.constant([0, 1, 2]					)}, {"input_ids": tf.constant([0, 1, 2, 3]					)}]
										_snake_case							=   BertTokenizer.from_pretrained("bert-base-cased"					)

										_snake_case							=   tokenizer.pad(_SCREAMING_SNAKE_CASE      ,padding=_SCREAMING_SNAKE_CASE					)
										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]]					)
										_snake_case							=   tokenizer.pad(_SCREAMING_SNAKE_CASE      ,padding=_SCREAMING_SNAKE_CASE      ,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        _lowercase			(				self					)							->    Optional[Any]:
										_snake_case							=   Tokenizer(WordPiece(unk_token="[UNK]"					)					)
										PreTrainedTokenizerFast(tokenizer_object=_SCREAMING_SNAKE_CASE					)






					@require_tokenizers
					def        _lowercase			(				self					)							->    Dict:
										_snake_case							=   Tokenizer(WordPiece(unk_token="[UNK]"					)					)
										with tempfile.TemporaryDirectory() as tmpdirname:
															bert_tokenizer.save(os.path.join(_SCREAMING_SNAKE_CASE      ,"tokenizer.json"					)					)
															PreTrainedTokenizerFast(tokenizer_file=os.path.join(_SCREAMING_SNAKE_CASE      ,"tokenizer.json"					)					)








'''simple docstring'''





UpperCamelCase_		:    Optional[Any]									=		'''
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
'''

UpperCamelCase_		:    List[Any]									=		[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]


