import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
    import tensorflow as tf

if is_tensorflow_text_available():
    from transformers.models.bert import TFBertTokenizer


__lowercase							 =					['''bert-base-uncased''', '''bert-base-cased''']
__lowercase							 =					'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():


    class 	lowerCamelCase_     (   tf.keras.Model ):






        '''simple docstring'''






        def __init__(	self , __lowercase)       ->	List[Any]:
            super().__init__()
            __UpperCamelCase     :Dict			  =		tokenizer
            __UpperCamelCase     :Any			  =		AutoConfig.from_pretrained(__lowercase)
            __UpperCamelCase     :Optional[Any]			  =		TFAutoModel.from_config(__lowercase)







        def 			UpperCamelCase__   (	self , __lowercase)       ->	List[str]:
            __UpperCamelCase     :str			  =		self.tokenizer(__lowercase)
            __UpperCamelCase     :Union[str, Any]			  =		self.bert(**__lowercase)
            return out["pooler_output"]


@require_tf
@require_tensorflow_text
class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Dict:
        super().setUp()

        __UpperCamelCase     :int			  =		[
            BertTokenizer.from_pretrained(__lowercase) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
        ]  # repeat for when fast_bert_tokenizer=false
        __UpperCamelCase     :List[str]			  =		[TFBertTokenizer.from_pretrained(__lowercase) for checkpoint in TOKENIZER_CHECKPOINTS] + [
            TFBertTokenizer.from_pretrained(__lowercase , use_fast_bert_tokenizer=__lowercase)
            for checkpoint in TOKENIZER_CHECKPOINTS
        ]
        assert len(self.tokenizers) == len(self.tf_tokenizers)

        __UpperCamelCase     :List[Any]			  =		[
            '''This is a straightforward English test sentence.''',
            '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
            '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
            '''And some much more rare Chinese: 齉 堃 齉堃''',
            '''Je vais aussi écrire en français pour tester les accents''',
            '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
        ]
        __UpperCamelCase     :Any			  =		list(zip(self.test_sentences , self.test_sentences[::-1]))







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers):
            for test_inputs in (self.test_sentences, self.paired_sentences):
                __UpperCamelCase     :Tuple			  =		tokenizer(__lowercase , return_tensors='''tf''' , padding='''longest''')
                __UpperCamelCase     :Optional[int]			  =		tf_tokenizer(__lowercase)

                for key in python_outputs.keys():
                    self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                    self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa) == tf_outputs[key]))







    @slow
    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        for tf_tokenizer in self.tf_tokenizers:
            __UpperCamelCase     :Optional[Any]			  =		tf_tokenizer(self.paired_sentences)
            __UpperCamelCase     :Union[str, Any]			  =		tf_tokenizer(
                text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
            for key in merged_outputs.keys():
                self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa) == separated_outputs[key]))







    @slow
    def 			UpperCamelCase__   (	self)       ->	str:
        for tf_tokenizer in self.tf_tokenizers:
            __UpperCamelCase     :int			  =		tf.function(__lowercase)
            for test_inputs in (self.test_sentences, self.paired_sentences):
                __UpperCamelCase     :Any			  =		tf.constant(__lowercase)
                __UpperCamelCase     :List[str]			  =		compiled_tokenizer(__lowercase)
                __UpperCamelCase     :str			  =		tf_tokenizer(__lowercase)

                for key in eager_outputs.keys():
                    self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))







    @slow
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        for tf_tokenizer in self.tf_tokenizers:
            __UpperCamelCase     :Union[str, Any]			  =		ModelToSave(tokenizer=__lowercase)
            __UpperCamelCase     :Dict			  =		tf.convert_to_tensor(self.test_sentences)
            __UpperCamelCase     :int			  =		model(__lowercase)  # Build model with some sample inputs
            with TemporaryDirectory() as tempdir:
                __UpperCamelCase     :int			  =		Path(__lowercase) / '''saved.model'''
                model.save(__lowercase)
                __UpperCamelCase     :List[Any]			  =		tf.keras.models.load_model(__lowercase)
            __UpperCamelCase     :int			  =		loaded_model(__lowercase)
            # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
            self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) , 1E-5)






import numpy as np



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 1e-12  ,			SCREAMING_SNAKE_CASE = 100  ,			):
    '''simple docstring'''







    assert np.shape(SCREAMING_SNAKE_CASE	)[0] == np.shape(SCREAMING_SNAKE_CASE	)[1]
    # Ensure proper dimensionality.
    assert np.shape(SCREAMING_SNAKE_CASE	)[0] == np.shape(SCREAMING_SNAKE_CASE	)[0]
    # Ensure inputs are either both complex or both real
    assert np.iscomplexobj(SCREAMING_SNAKE_CASE	) == np.iscomplexobj(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :List[Any]			  =		np.iscomplexobj(SCREAMING_SNAKE_CASE	)
    if is_complex:
        # Ensure complex input_matrix is Hermitian
        assert np.array_equal(SCREAMING_SNAKE_CASE  ,			input_matrix.conj().T	)

    # Set convergence to False. Will define convergence when we exceed max_iterations
    # or when we have small changes from one iteration to next.

    __UpperCamelCase     :str			  =		False
    __UpperCamelCase     :int			  =		0
    __UpperCamelCase     :Optional[Any]			  =		0
    __UpperCamelCase     :Union[str, Any]			  =		1e12

    while not convergence:
        # Multiple matrix by the vector.
        __UpperCamelCase     :List[str]			  =		np.dot(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        # Normalize the resulting output vector.
        __UpperCamelCase     :Tuple			  =		w / np.linalg.norm(SCREAMING_SNAKE_CASE	)
        # Find rayleigh quotient
        # (faster than usual b/c we know vector is normalized already)
        __UpperCamelCase     :int			  =		vector.conj().T if is_complex else vector.T
        __UpperCamelCase     :Optional[int]			  =		np.dot(SCREAMING_SNAKE_CASE  ,			np.dot(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)	)

        # Check convergence.
        __UpperCamelCase     :Optional[Any]			  =		np.abs(lambda_ - lambda_previous	) / lambda_
        iterations += 1

        if error <= error_tol or iterations >= max_iterations:
            __UpperCamelCase     :Dict			  =		True

        __UpperCamelCase     :List[Any]			  =		lambda_

    if is_complex:
        __UpperCamelCase     :Tuple			  =		np.real(lambda_	)

    return lambda_, vector



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]	)
    __UpperCamelCase     :Optional[Any]			  =		np.array([41, 4, 20]	)
    __UpperCamelCase     :Any			  =		real_input_matrix.astype(np.complexaaa	)
    __UpperCamelCase     :Dict			  =		np.triu(1j * complex_input_matrix  ,			1	)
    complex_input_matrix += imag_matrix
    complex_input_matrix += -1 * imag_matrix.T
    __UpperCamelCase     :Optional[int]			  =		np.array([41, 4, 20]	).astype(np.complexaaa	)

    for problem_type in ["real", "complex"]:
        if problem_type == "real":
            __UpperCamelCase     :Any			  =		real_input_matrix
            __UpperCamelCase     :int			  =		real_vector
        elif problem_type == "complex":
            __UpperCamelCase     :Tuple			  =		complex_input_matrix
            __UpperCamelCase     :Optional[Any]			  =		complex_vector

        # Our implementation.
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		power_iteration(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

        # Numpy implementation.

        # Get eigenvalues and eigenvectors using built-in numpy
        # eigh (eigh used for symmetric or hermetian matrices).
        __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		np.linalg.eigh(SCREAMING_SNAKE_CASE	)
        # Last eigenvalue is the maximum one.
        __UpperCamelCase     :List[Any]			  =		eigen_values[-1]
        # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
        __UpperCamelCase     :str			  =		eigen_vectors[:, -1]

        # Check our implementation and numpy gives close answers.
        assert np.abs(eigen_value - eigen_value_max	) <= 1e-6
        # Take absolute values element wise of each eigenvector.
        # as they are only unique to a minus sign.
        assert np.linalg.norm(np.abs(SCREAMING_SNAKE_CASE	) - np.abs(SCREAMING_SNAKE_CASE	)	) <= 1e-6


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    test_power_iteration()





