



from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class       _lowercase   :
				'''simple docstring'''







				lowercase__   						=						MBartConfig
				lowercase__   						=						{}
				lowercase__   						=						"""gelu"""

				def __init__( self     , snake_case__     , snake_case__=13     , snake_case__=7     , snake_case__=True     , snake_case__=False     , snake_case__=99     , snake_case__=32     , snake_case__=2     , snake_case__=4     , snake_case__=37     , snake_case__=0.1     , snake_case__=0.1     , snake_case__=20     , snake_case__=2     , snake_case__=1     , snake_case__=0     , ):

									'''simple docstring'''


									UpperCamelCase_		  =   parent
									UpperCamelCase_		  =   batch_size
									UpperCamelCase_		  =   seq_length
									UpperCamelCase_		  =   is_training
									UpperCamelCase_		  =   use_labels
									UpperCamelCase_		  =   vocab_size
									UpperCamelCase_		  =   hidden_size
									UpperCamelCase_		  =   num_hidden_layers
									UpperCamelCase_		  =   num_attention_heads
									UpperCamelCase_		  =   intermediate_size
									UpperCamelCase_		  =   hidden_dropout_prob
									UpperCamelCase_		  =   attention_probs_dropout_prob
									UpperCamelCase_		  =   max_position_embeddings
									UpperCamelCase_		  =   eos_token_id
									UpperCamelCase_		  =   pad_token_id
									UpperCamelCase_		  =   bos_token_id

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   ids_tensor([self.batch_size, self.seq_length - 1]     , self.vocab_size    )
									UpperCamelCase_		  =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )     , 1    )
									UpperCamelCase_		  =   tf.concat([input_ids, eos_tensor]     , axis=1    )

									UpperCamelCase_		  =   ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size    )

									UpperCamelCase_		  =   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_		  =   prepare_mbart_inputs_dict(snake_case__     , snake_case__     , snake_case__    )
									return config, inputs_dict

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   TFMBartModel(config=snake_case__    ).get_decoder()
									UpperCamelCase_		  =   inputs_dict["input_ids"]

									UpperCamelCase_		  =   input_ids[:1, :]
									UpperCamelCase_		  =   inputs_dict["attention_mask"][:1, :]
									UpperCamelCase_		  =   inputs_dict["head_mask"]
									UpperCamelCase_		  =   1

									# first forward pass
									UpperCamelCase_		  =   model(snake_case__     , attention_mask=snake_case__     , head_mask=snake_case__     , use_cache=snake_case__    )

									UpperCamelCase_  ,    UpperCamelCase_		  =   outputs.to_tuple()
									UpperCamelCase_		  =   past_key_values[1]




def    _lowerCAmelCase    (_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase=None   ,	_lowerCAmelCase=None   ,	_lowerCAmelCase=None   ,	_lowerCAmelCase=None   ,	_lowerCAmelCase=None   ,	):
					if attention_mask is None:
										UpperCamelCase_		  =   tf.cast(tf.math.not_equal(_lowerCAmelCase   ,	config.pad_token_id)   ,	tf.inta)
					if decoder_attention_mask is None:
										UpperCamelCase_		  =   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_		  =   tf.ones((config.encoder_layers, config.encoder_attention_heads))
					if decoder_head_mask is None:
										UpperCamelCase_		  =   tf.ones((config.decoder_layers, config.decoder_attention_heads))
					if cross_attn_head_mask is None:
										UpperCamelCase_		  =   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       _lowercase   (a_      ,   a_      ,   unittest.TestCase      ):
				'''simple docstring'''







				lowercase__   						=						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
				lowercase__   						=						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
				lowercase__   						=						(
				    {
				        """conversational""": TFMBartForConditionalGeneration,
				        """feature-extraction""": TFMBartModel,
				        """summarization""": TFMBartForConditionalGeneration,
				        """text2text-generation""": TFMBartForConditionalGeneration,
				        """translation""": TFMBartForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				lowercase__   						=						True
				lowercase__   						=						False
				lowercase__   						=						False

				def       _lowerCamelCase					( self     , snake_case__     , snake_case__     , snake_case__     , snake_case__     , snake_case__    ):

									'''simple docstring'''


									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
														# Exception encountered when calling layer '...'
														return True

									return False

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   TFMBartModelTester(self    )
									UpperCamelCase_		  =   ConfigTester(self     , config_class=snake_case__    )

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									self.config_tester.run_common_tests()

				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*snake_case__    )




@require_sentencepiece
@require_tokenizers
@require_tf
class       _lowercase   (unittest.TestCase      ):
				'''simple docstring'''







				lowercase__   						=						[
				    """ UN Chief Says There Is No Military Solution in Syria""",
				]
				lowercase__   						=						[
				    """Şeful ONU declară că nu există o soluţie militară în Siria""",
				]
				lowercase__   						=						"""facebook/mbart-large-en-ro"""

				@cached_property
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									return AutoTokenizer.from_pretrained(self.model_name    )

				@cached_property
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									UpperCamelCase_		  =   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
									return model

				def       _lowerCamelCase					( self     , **snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.translate_src_text(**snake_case__    )
									self.assertListEqual(self.expected_text     , snake_case__    )

				def       _lowerCamelCase					( self     , **snake_case__    ):

									'''simple docstring'''


									UpperCamelCase_		  =   self.tokenizer(self.src_text     , **snake_case__     , return_tensors="tf"    )
									UpperCamelCase_		  =   self.model.generate(
									    model_inputs.input_ids     , attention_mask=model_inputs.attention_mask     , num_beams=2    )
									UpperCamelCase_		  =   self.tokenizer.batch_decode(snake_case__     , skip_special_tokens=snake_case__    )
									return generated_words

				@slow
				def       _lowerCamelCase					( self    ):

									'''simple docstring'''


									self._assert_generated_batch_equal_expected()





import re



def    _lowerCAmelCase    (_lowerCAmelCase):
					UpperCamelCase_		  =   re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$")
					if match := re.search(_lowerCAmelCase   ,	_lowerCAmelCase):
										return match.string == phone
					return False


if __name__ == "__main__":
	print(indian_phone_validator("""+918827897895"""))
