






"""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      _snake_case       (				self				:    Any    ,     __lowerCamelCase				:    Optional[int]						)					->       Any:
										__magic_name__      =  list(tokenizer_class.max_model_input_sizes.keys()						)
										for model_name in sa_models[:1]:
																__magic_name__      =  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						)
																						__magic_name__      =  tokenizer.convert_tokens_to_ids(__lowerCamelCase						)
																						self.assertIsInstance(__lowerCamelCase    ,     __lowerCamelCase						)







				def      _snake_case       (				self				:    Optional[int]    ,     __lowerCamelCase				:    BatchEncoding    ,     __lowerCamelCase				:    Optional[Callable] = None						)					->       List[str]:
										__magic_name__      =  pickle.dumps(__lowerCamelCase						)
										self.assertIsNotNone(__lowerCamelCase						)

										__magic_name__      =  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				:    List[str]						)					->       List[Any]:
										self.check_tokenizer_from_pretrained(__lowerCamelCase						)







				def      _snake_case       (				self				:    Tuple						)					->       Optional[int]:
										self.assertEqual(TensorType("tf"						)    ,     TensorType.TENSORFLOW						)
										self.assertEqual(TensorType("pt"						)    ,     TensorType.PYTORCH						)
										self.assertEqual(TensorType("np"						)    ,     TensorType.NUMPY						)







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

										__magic_name__      =  BertTokenizer.from_pretrained("bert-base-cased"						)
										__magic_name__      =  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				:    int						)					->       Dict:
										import tensorflow as tf

										def tf_array_equals(__lowerCamelCase				:    List[str]    ,     __lowerCamelCase				:    int						):
																return tf.reduce_all(tf.equal(__lowerCamelCase    ,     __lowerCamelCase						)						)

										__magic_name__      =  BertTokenizer.from_pretrained("bert-base-cased"						)
										__magic_name__      =  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				:    Any						)					->       int:
										import torch

										__magic_name__      =  BertTokenizer.from_pretrained("bert-base-cased"						)
										__magic_name__      =  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				:    Tuple						)					->       str:
										__magic_name__      =  BertTokenizer.from_pretrained("bert-base-cased"						)
										__magic_name__      =  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				:    Union[str, Any]						)					->       Union[str, Any]:
										__magic_name__      =  BertTokenizerFast.from_pretrained("bert-base-cased"						)
										__magic_name__      =  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				:    Dict						)					->       Union[str, Any]:
										__magic_name__      =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}						)
										__magic_name__      =  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:
																__magic_name__      =  batch.convert_to_tensors(tensor_type="np"						)
										self.assertFalse(len(cs.err						)    ,     msg=f'''should have no warning, but got {cs.err}'''						)

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

										__magic_name__      =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}						)
										__magic_name__      =  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				:    Dict						)					->       Dict:
										__magic_name__      =  BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}						)
										__magic_name__      =  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:
																__magic_name__      =  batch.convert_to_tensors(tensor_type="tf"						)
										self.assertFalse(len(cs.err						)    ,     msg=f'''should have no warning, but got {cs.err}'''						)

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

										__magic_name__      =  BatchEncoding({"inputs": [1, 2, 3], "labels": 0}						)
										__magic_name__      =  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				:    Any						)					->       List[Any]:
										__magic_name__      =  [{"input_ids": np.array([0, 1, 2]						)}, {"input_ids": np.array([0, 1, 2, 3]						)}]
										__magic_name__      =  BertTokenizer.from_pretrained("bert-base-cased"						)

										__magic_name__      =  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]]						)
										__magic_name__      =  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				:    Optional[Any]						)					->       Any:
										import torch

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

										__magic_name__      =  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]]						)
										__magic_name__      =  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]						)					->       str:
										import tensorflow as tf

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

										__magic_name__      =  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]]						)
										__magic_name__      =  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				:    List[str]						)					->       List[Any]:
										__magic_name__      =  Tokenizer(WordPiece(unk_token="[UNK]"						)						)
										PreTrainedTokenizerFast(tokenizer_object=__lowerCamelCase						)







				@require_tokenizers
				def      _snake_case       (				self				:    List[Any]						)					->       Union[str, Any]:
										__magic_name__      =  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"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



