






from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel



@require_tf
class 						lowerCamelCase   :







		'''simple docstring'''



		_snake_case					:					Union[str, Any] 		=						MarianConfig
		_snake_case					:					List[Any] 		=						{}
		_snake_case					:					Any 		=						'''gelu'''



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase=1_3       ,	_UpperCamelCase=7       ,	_UpperCamelCase=True       ,	_UpperCamelCase=False       ,	_UpperCamelCase=9_9       ,	_UpperCamelCase=3_2       ,	_UpperCamelCase=2       ,	_UpperCamelCase=4       ,	_UpperCamelCase=3_7       ,	_UpperCamelCase=0.1       ,	_UpperCamelCase=0.1       ,	_UpperCamelCase=2_0       ,	_UpperCamelCase=2       ,	_UpperCamelCase=1       ,	_UpperCamelCase=0       ,	)			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		parent
							UpperCAmelCase_					:   Optional[Any]   =		batch_size
							UpperCAmelCase_					:   Dict   =		seq_length
							UpperCAmelCase_					:   str   =		is_training
							UpperCAmelCase_					:   Any   =		use_labels
							UpperCAmelCase_					:   Optional[Any]   =		vocab_size
							UpperCAmelCase_					:   str   =		hidden_size
							UpperCAmelCase_					:   Union[str, Any]   =		num_hidden_layers
							UpperCAmelCase_					:   List[str]   =		num_attention_heads
							UpperCAmelCase_					:   Dict   =		intermediate_size

							UpperCAmelCase_					:   int   =		hidden_dropout_prob
							UpperCAmelCase_					:   Dict   =		attention_probs_dropout_prob
							UpperCAmelCase_					:   Tuple   =		max_position_embeddings
							UpperCAmelCase_					:   Union[str, Any]   =		eos_token_id
							UpperCAmelCase_					:   Any   =		pad_token_id
							UpperCAmelCase_					:   Any   =		bos_token_id



		def       __UpperCAmelCase    (							self     )			->							Any:
							UpperCAmelCase_					:   List[str]   =		ids_tensor([self.batch_size, self.seq_length - 1]       ,	self.vocab_size     )
							UpperCAmelCase_					:   List[str]   =		tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )       ,	1     )
							UpperCAmelCase_					:   Tuple   =		tf.concat([input_ids, eos_tensor]       ,	axis=1     )

							UpperCAmelCase_					:   List[str]   =		ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size     )

							UpperCAmelCase_					:   Dict   =		self.config_cls(
							    vocab_size=self.vocab_size       ,	d_model=self.hidden_size       ,	encoder_layers=self.num_hidden_layers       ,	decoder_layers=self.num_hidden_layers       ,	encoder_attention_heads=self.num_attention_heads       ,	decoder_attention_heads=self.num_attention_heads       ,	encoder_ffn_dim=self.intermediate_size       ,	decoder_ffn_dim=self.intermediate_size       ,	dropout=self.hidden_dropout_prob       ,	attention_dropout=self.attention_probs_dropout_prob       ,	max_position_embeddings=self.max_position_embeddings       ,	eos_token_ids=[2]       ,	bos_token_id=self.bos_token_id       ,	pad_token_id=self.pad_token_id       ,	decoder_start_token_id=self.pad_token_id       ,	**self.config_updates       ,	)
							UpperCAmelCase_					:   Optional[int]   =		prepare_marian_inputs_dict(_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase     )
							return config, inputs_dict



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   str   =		TFMarianModel(config=_UpperCamelCase     ).get_decoder()
							UpperCAmelCase_					:   Tuple   =		inputs_dict['input_ids']

							UpperCAmelCase_					:   str   =		input_ids[:1, :]
							UpperCAmelCase_					:   Any   =		inputs_dict['attention_mask'][:1, :]
							UpperCAmelCase_					:   Optional[int]   =		inputs_dict['head_mask']
							UpperCAmelCase_					:   Union[str, Any]   =		1

							# first forward pass
							UpperCAmelCase_					:   Any   =		model(_UpperCamelCase       ,	attention_mask=_UpperCamelCase       ,	head_mask=_UpperCamelCase       ,	use_cache=_UpperCamelCase     )

							UpperCAmelCase_ ,						UpperCAmelCase_					:   Optional[Any]   =		outputs.to_tuple()

							# create hypothetical next token and extent to next_input_ids
							UpperCAmelCase_					:   int   =		ids_tensor((self.batch_size, 3)       ,	config.vocab_size     )
							UpperCAmelCase_					:   int   =		tf.cast(ids_tensor((self.batch_size, 3)       ,	2     )       ,	tf.inta     )

							# append to next input_ids and
							UpperCAmelCase_					:   Optional[int]   =		tf.concat([input_ids, next_tokens]       ,	axis=-1     )
							UpperCAmelCase_					:   List[Any]   =		tf.concat([attention_mask, next_attn_mask]       ,	axis=-1     )

							UpperCAmelCase_					:   Any   =		model(_UpperCamelCase       ,	attention_mask=_UpperCamelCase     )[0]
							UpperCAmelCase_					:   Any   =		model(_UpperCamelCase       ,	attention_mask=_UpperCamelCase       ,	past_key_values=_UpperCamelCase     )[0]

							self.parent.assertEqual(next_tokens.shape[1]       ,	output_from_past.shape[1]     )

							# select random slice
							UpperCAmelCase_					:   Optional[Any]   =		int(ids_tensor((1,)       ,	output_from_past.shape[-1]     )     )
							UpperCAmelCase_					:   Dict   =		output_from_no_past[:, -3:, random_slice_idx]
							UpperCAmelCase_					:   str   =		output_from_past[:, :, random_slice_idx]

							# test that outputs are equal for slice
							tf.debugging.assert_near(_UpperCamelCase       ,	_UpperCamelCase       ,	rtol=1E-3     )





def   lowercase__   (  __snake_case					:							int  , __snake_case					:							Any  , __snake_case					:							Dict  , __snake_case					:							Any=None  , __snake_case					:							List[Any]=None  , __snake_case					:							Optional[Any]=None  , __snake_case					:							int=None  , __snake_case					:							Union[str, Any]=None  , ):


					'''simple docstring'''


					if attention_mask is None:
										UpperCAmelCase_					:   Any   =		tf.cast(tf.math.not_equal(__snake_case  , config.pad_token_id						)  , tf.inta						)
					if decoder_attention_mask is None:
										UpperCAmelCase_					:   List[str]   =		tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape  , dtype=tf.inta						),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  , config.pad_token_id						)  , tf.inta						),
										    ]  , axis=-1  , )
					if head_mask is None:
										UpperCAmelCase_					:   List[Any]   =		tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
					if decoder_head_mask is None:
										UpperCAmelCase_					:   Union[str, Any]   =		tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
					if cross_attn_head_mask is None:
										UpperCAmelCase_					:   Tuple   =		tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}



@require_tf
class 						lowerCamelCase   (_snake_case				,						_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					str 		=						(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
		_snake_case					:					int 		=						(TFMarianMTModel,) if is_tf_available() else ()
		_snake_case					:					Dict 		=						(
		    {
		        '''conversational''': TFMarianMTModel,
		        '''feature-extraction''': TFMarianModel,
		        '''summarization''': TFMarianMTModel,
		        '''text2text-generation''': TFMarianMTModel,
		        '''translation''': TFMarianMTModel,
		    }
		    if is_tf_available()
		    else {}
		)
		_snake_case					:					int 		=						True
		_snake_case					:					int 		=						False
		_snake_case					:					Optional[Any] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Any:
							UpperCAmelCase_					:   Tuple   =		TFMarianModelTester(self     )
							UpperCAmelCase_					:   List[str]   =		ConfigTester(self       ,	config_class=_UpperCamelCase     )



		def       __UpperCAmelCase    (							self     )			->							List[Any]:
							self.config_tester.run_common_tests()



		def       __UpperCAmelCase    (							self     )			->							Any:
							UpperCAmelCase_					:   Union[str, Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase     )





@require_tf
class 						lowerCamelCase   (unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					List[str] 		=						1_0_0_0  # show more chars for failing integration tests



		@classmethod
		def       __UpperCAmelCase    (							cls     )			->							None:
							UpperCAmelCase_					:   Optional[int]   =		f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
							return cls



		@cached_property
		def       __UpperCAmelCase    (							self     )			->							MarianTokenizer:
							return AutoTokenizer.from_pretrained(self.model_name     )



		@property
		def       __UpperCAmelCase    (							self     )			->							int:
							return self.tokenizer.eos_token_id



		@cached_property
		def       __UpperCAmelCase    (							self     )			->							Optional[Any]:
							warnings.simplefilter('error'     )
							UpperCAmelCase_					:   TFMarianMTModel   =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
							assert isinstance(_UpperCamelCase       ,	_UpperCamelCase     )
							UpperCAmelCase_					:   Optional[int]   =		model.config
							self.assertListEqual(c.bad_words_ids       ,	[[c.pad_token_id]]     )
							self.assertEqual(c.max_length       ,	5_1_2     )
							self.assertEqual(c.decoder_start_token_id       ,	c.pad_token_id     )
							return model



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							Union[str, Any]:
							UpperCAmelCase_					:   int   =		self.translate_src_text(**_UpperCamelCase     )
							self.assertListEqual(self.expected_text       ,	_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							Tuple:
							UpperCAmelCase_					:   str   =		self.tokenizer(self.src_text       ,	**_UpperCamelCase       ,	padding=_UpperCamelCase       ,	return_tensors='tf'     )
							UpperCAmelCase_					:   List[Any]   =		self.model.generate(
							    model_inputs.input_ids       ,	attention_mask=model_inputs.attention_mask       ,	num_beams=2       ,	max_length=1_2_8     )
							UpperCAmelCase_					:   Optional[Any]   =		self.tokenizer.batch_decode(generated_ids.numpy()       ,	skip_special_tokens=_UpperCamelCase     )
							return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					Any 		=						'''mt'''
		_snake_case					:					Optional[Any] 		=						'''en'''
		_snake_case					:					Optional[Any] 		=						['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
		_snake_case					:					Union[str, Any] 		=						['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']



		@unittest.skip('Skipping until #12647 is resolved.'     )
		@slow
		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					Any 		=						'''en'''
		_snake_case					:					Dict 		=						'''zh'''
		_snake_case					:					List[str] 		=						['''My name is Wolfgang and I live in Berlin''']
		_snake_case					:					Any 		=						['''我叫沃尔夫冈 我住在柏林''']



		@unittest.skip('Skipping until #12647 is resolved.'     )
		@slow
		def       __UpperCAmelCase    (							self     )			->							Optional[Any]:
							self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 						lowerCamelCase   (_snake_case    ):







		'''simple docstring'''



		_snake_case					:					Any 		=						'''en'''
		_snake_case					:					str 		=						'''ROMANCE'''
		_snake_case					:					Any 		=						[
		    '''>>fr<< Don\'t spend so much time watching TV.''',
		    '''>>pt<< Your message has been sent.''',
		    '''>>es<< He\'s two years older than me.''',
		]
		_snake_case					:					List[str] 		=						[
		    '''Ne passez pas autant de temps à regarder la télé.''',
		    '''A sua mensagem foi enviada.''',
		    '''Es dos años más viejo que yo.''',
		]



		@unittest.skip('Skipping until #12647 is resolved.'     )
		@slow
		def       __UpperCAmelCase    (							self     )			->							Tuple:
							self._assert_generated_batch_equal_expected()



		@unittest.skip('Skipping until #12647 is resolved.'     )
		@slow
		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   str   =		TranslationPipeline(self.model       ,	self.tokenizer       ,	framework='tf'     )
							UpperCAmelCase_					:   List[str]   =		pipeline(self.src_text     )
							self.assertEqual(self.expected_text       ,	[x['translation_text'] for x in output]     )














from __future__ import annotations

from typing import Any




def   lowercase__   (  *__snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if all(_check_not_integer(__snake_case						) for m in matrix_s						):
										for i in matrix_s[1:]:
															_verify_matrix_sizes(matrix_s[0]  , __snake_case						)
										return [[sum(__snake_case						) for t in zip(*__snake_case						)] for m in zip(*__snake_case						)]
					raise TypeError('Expected a matrix, got int/list instead'						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if (
					    _check_not_integer(__snake_case						)
					    and _check_not_integer(__snake_case						)
					    and _verify_matrix_sizes(__snake_case  , __snake_case						)
					):
										return [[i - j for i, j in zip(*__snake_case						)] for m in zip(__snake_case  , __snake_case						)]
					raise TypeError('Expected a matrix, got int/list instead'						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							int | float						):


					'''simple docstring'''


					return [[x * n for x in row] for row in matrix]




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if _check_not_integer(__snake_case						) and _check_not_integer(__snake_case						):
										UpperCAmelCase_ ,						UpperCAmelCase_					:   str   =		_verify_matrix_sizes(__snake_case  , __snake_case						)

					if cols[0] != rows[1]:
										UpperCAmelCase_					:   Dict   =		(
										    'Cannot multiply matrix of dimensions '
										    F"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
										)
										raise ValueError(__snake_case						)
					return [
					    [sum(m * n for m, n in zip(__snake_case  , __snake_case						)						) for j in zip(*__snake_case						)] for i in matrix_a
					]




def   lowercase__   (  __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		int(__snake_case						)
					return [[int(row == column						) for column in range(__snake_case						)] for row in range(__snake_case						)]




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							bool = True						):


					'''simple docstring'''


					if _check_not_integer(__snake_case						):
										if return_map:
															return map(__snake_case  , zip(*__snake_case						)						)
										else:
															return list(map(__snake_case  , zip(*__snake_case						)						)						)
					raise TypeError('Expected a matrix, got int/list instead'						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							int  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   Any   =		matrix[:row] + matrix[row + 1 :]
					return [row[:column] + row[column + 1 :] for row in minor]




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					if len(__snake_case						) == 1:
										return matrix[0][0]

					return sum(
					    x * determinant(minor(__snake_case  , 0  , __snake_case						)						) * (-1) ** i
					    for i, x in enumerate(matrix[0]						)						)




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[int]   =		determinant(__snake_case						)
					if det == 0:
										return None

					UpperCAmelCase_					:   List[str]   =		[
					    [determinant(minor(__snake_case  , __snake_case  , __snake_case						)						) for j in range(len(__snake_case						)						)]
					    for i in range(len(__snake_case						)						)
					]

					UpperCAmelCase_					:   List[str]   =		[
					    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]						)]
					    for row in range(len(__snake_case						)						)
					]
					UpperCAmelCase_					:   Tuple   =		list(transpose(__snake_case						)						)
					return scalar_multiply(__snake_case  , 1 / det						)




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					return not isinstance(__snake_case  , __snake_case						) and not isinstance(matrix[0]  , __snake_case						)




def   lowercase__   (  __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					return len(__snake_case						), len(matrix[0]						)




def   lowercase__   (  __snake_case					:							list[list[int]]  , __snake_case					:							list[list[int]]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		_shape(__snake_case						) + _shape(__snake_case						)
					if shape[0] != shape[3] or shape[1] != shape[2]:
										UpperCAmelCase_					:   Optional[int]   =		(
										    'operands could not be broadcast together with shape '
										    F"({shape[0], shape[1]}), ({shape[2], shape[3]})"
										)
										raise ValueError(__snake_case						)
					return (shape[0], shape[2]), (shape[1], shape[3])




def   lowercase__   (  ):


					'''simple docstring'''


					UpperCAmelCase_					:   Tuple   =		[[12, 10], [3, 9]]
					UpperCAmelCase_					:   Optional[int]   =		[[3, 4], [7, 4]]
					UpperCAmelCase_					:   List[str]   =		[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
					UpperCAmelCase_					:   str   =		[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
					print(F"Add Operation, {add(__snake_case  , __snake_case						) = } \n"						)
					print(
					    F"Multiply Operation, {multiply(__snake_case  , __snake_case						) = } \n"  , )
					print(F"Identity: {identity(5						)}\n"						)
					print(F"Minor of {matrix_c} = {minor(__snake_case  , 1  , 2						)} \n"						)
					print(F"Determinant of {matrix_b} = {determinant(__snake_case						)} \n"						)
					print(F"Inverse of {matrix_d} = {inverse(__snake_case						)}\n"						)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






