






import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_   :	Optional[Any]							      =get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class        a__      (	A_	,       unittest.TestCase		):
		UpperCAmelCase_							:       Any 					= ErnieMTokenizer
		UpperCAmelCase_							:       Optional[int] 					= False
		UpperCAmelCase_							:       List[str] 					= True
		UpperCAmelCase_							:       str 					= False
		UpperCAmelCase_							:       Optional[int] 					= False






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				super().setUp()

				# We have a SentencePiece fixture for testing
				__A    =							ErnieMTokenizer(_A							, unk_token="<unk>"							, pad_token="<pad>"						)
				tokenizer.save_pretrained(self.tmpdirname						)






		def 					_lowerCamelCase					(      self							, lowercase__						)				->				int:
				__A    =							"this is a test"
				__A    =							"this is a test"
				return input_text, output_text






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							"<pad>"
				__A    =							0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A						)							, _A						)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A						)							, _A						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							list(self.get_tokenizer().get_vocab().keys()						)

				self.assertEqual(vocab_keys[0]							, "<pad>"						)
				self.assertEqual(vocab_keys[1]							, "<unk>"						)
				self.assertEqual(vocab_keys[-1]							, "▁eloquent"						)
				self.assertEqual(len(_A						)							, 30000						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				self.assertEqual(self.get_tokenizer().vocab_size							, 30000						)






		def 					_lowerCamelCase					(      self						)				->				str:
				if not self.test_rust_tokenizer:
						return

				__A    =							self.get_tokenizer()
				__A    =							self.get_rust_tokenizer()

				__A    =							"I was born in 92000, and this is falsé."

				__A    =							tokenizer.tokenize(_A						)
				__A    =							rust_tokenizer.tokenize(_A						)
				self.assertListEqual(_A							, _A						)

				__A    =							tokenizer.encode(_A							, add_special_tokens=_A						)
				__A    =							rust_tokenizer.encode(_A							, add_special_tokens=_A						)
				self.assertListEqual(_A							, _A						)

				__A    =							self.get_rust_tokenizer()
				__A    =							tokenizer.encode(_A						)
				__A    =							rust_tokenizer.encode(_A						)
				self.assertListEqual(_A							, _A						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							ErnieMTokenizer(_A							, do_lower_case=_A							, unk_token="<unk>"							, pad_token="<pad>"						)

				__A    =							tokenizer.tokenize("This is a test"						)
				self.assertListEqual(_A							, ["▁this", "▁is", "▁a", "▁test"]						)

				self.assertListEqual(tokenizer.convert_tokens_to_ids(_A						)							, [48, 25, 21, 1289]						)

				__A    =							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
				# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
				self.assertListEqual(
				    _A							, ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]						)
				__A    =							tokenizer.convert_tokens_to_ids(_A						)
				self.assertListEqual(_A							, [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]						)

				__A    =							tokenizer.convert_ids_to_tokens(_A						)
				self.assertListEqual(
				    _A							, ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]							, )






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							ErnieMTokenizer(_A							, unk_token="<unk>"							, pad_token="<pad>"						)

				__A    =							tokenizer.encode("sequence builders"						)
				__A    =							tokenizer.encode("multi-sequence build"						)

				__A    =							tokenizer.build_inputs_with_special_tokens(_A						)
				__A    =							tokenizer.build_inputs_with_special_tokens(_A							, _A						)

				assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
				assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
				    tokenizer.sep_token_id
				] + text_a + [tokenizer.sep_token_id]






		@slow
		def 					_lowerCamelCase					(      self						)				->				Any:
				# fmt: off
				__A    =							{"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=_A							, model_name="susnato/ernie-m-base_pytorch"							, sequences=[
				        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
				        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
				        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
				        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
				        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
				        "conditioning on both left and right context in all layers.",
				        "The quick brown fox jumps over the lazy dog.",
				    ]							, )



import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_A       =					[[12, 10], [3, 9]]
_A       =					[[3, 4], [7, 4]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_A       =					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_A       =					[1]
_A       =					[2]

_A       =					logging.getLogger()
_A       =					logging.DEBUG
_A       =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Dict:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} returned integer'						)
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_addition.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) + np.array(A__						)).tolist()
        snake_case						=   matop.add(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} with different matrix dims'						)
            matop.add(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				List[Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} returned integer'						)
            matop.subtract(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) - np.array(A__						)).tolist()
        snake_case						=   matop.subtract(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        logger.info(F'\n\t{test_multiplication.__name__} returned integer'						)
        with pytest.raises(A__						):
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'						)
        snake_case						=   (np.matmul(A__					,						A__						)).tolist()
        snake_case						=   matop.multiply(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(
                F'\n\t{test_multiplication.__name__} does not meet dim requirements'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   (3.5 * np.array(A__						)).tolist()
    snake_case						=   matop.scalar_multiply(A__					,						3.5						)
    assert theo == act



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				Any:
    """simple docstring"""

    snake_case						=   (np.identity(5						)).tolist()
    snake_case						=   matop.identity(5						)
    assert theo == act





@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"					,						[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]						)
def 		lowercase_     (  A__						)				->				List[str]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_transpose.__name__} returned integer'						)
            matop.transpose(A__						)
    else:
        snake_case						=   (np.transpose(A__						)).tolist()
        snake_case						=   matop.transpose(A__					,						return_map=A__						)
        assert theo == act

