




"""simple docstring"""

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__    :
			a__ :      str								=     MarianConfig
			a__ :      List[str]								=     {}
			a__ :      Tuple								=     """gelu"""


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



					"""simple docstring"""







					snake_case		:						Any								=				parent
					snake_case		:						List[str]								=				batch_size
					snake_case		:						Dict								=				seq_length
					snake_case		:						Any								=				is_training
					snake_case		:						Any								=				use_labels
					snake_case		:						Union[str, Any]								=				vocab_size
					snake_case		:						List[Any]								=				hidden_size
					snake_case		:						str								=				num_hidden_layers
					snake_case		:						Any								=				num_attention_heads
					snake_case		:						Optional[int]								=				intermediate_size

					snake_case		:						Dict								=				hidden_dropout_prob
					snake_case		:						List[Any]								=				attention_probs_dropout_prob
					snake_case		:						Optional[Any]								=				max_position_embeddings
					snake_case		:						Optional[int]								=				eos_token_id
					snake_case		:						Any								=				pad_token_id
					snake_case		:						Dict								=				bos_token_id


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







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

					snake_case		:						Optional[int]								=				ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size							)

					snake_case		:						Tuple								=				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				, )
					snake_case		:						List[Any]								=				prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
					return config, inputs_dict



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						int								=				TFMarianModel(config=SCREAMING_SNAKE_CASE							).get_decoder()
					snake_case		:						Optional[int]								=				inputs_dict["input_ids"]

					snake_case		:						List[Any]								=				input_ids[:1, :]
					snake_case		:						List[str]								=				inputs_dict["attention_mask"][:1, :]
					snake_case		:						Tuple								=				inputs_dict["head_mask"]
					snake_case		:						int								=				1

					# first forward pass
					snake_case		:						int								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, head_mask=SCREAMING_SNAKE_CASE				, use_cache=SCREAMING_SNAKE_CASE							)

					snake_case			,				snake_case		:						List[Any]								=				outputs.to_tuple()

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

					# append to next input_ids and
					snake_case		:						Any								=				tf.concat([input_ids, next_tokens]				, axis=-1							)
					snake_case		:						str								=				tf.concat([attention_mask, next_attn_mask]				, axis=-1							)

					snake_case		:						Optional[Any]								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE							)[0]
					snake_case		:						str								=				model(SCREAMING_SNAKE_CASE				, attention_mask=SCREAMING_SNAKE_CASE				, past_key_values=SCREAMING_SNAKE_CASE							)[0]

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

					# select random slice
					snake_case		:						Any								=				int(ids_tensor((1,)				, output_from_past.shape[-1]							)							)
					snake_case		:						Union[str, Any]								=				output_from_no_past[:, -3:, random_slice_idx]
					snake_case		:						List[Any]								=				output_from_past[:, :, random_slice_idx]

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







def   UpperCamelCase__     (		lowercase__  :     List[str]       ,     lowercase__  :     int       ,     lowercase__  :     Dict       ,     lowercase__  :     int=None       ,     lowercase__  :     int=None       ,     lowercase__  :     Optional[int]=None       ,     lowercase__  :     Any=None       ,     lowercase__  :     Optional[int]=None       ,     ):
		if attention_mask is None:
				snake_case		:						Dict								=				tf.cast(tf.math.not_equal(lowercase__       ,     config.pad_token_id   )       ,     tf.inta   )
		if decoder_attention_mask is None:
				snake_case		:						Union[str, Any]								=				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:
				snake_case		:						Union[str, Any]								=				tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
		if decoder_head_mask is None:
				snake_case		:						int								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
		if cross_attn_head_mask is None:
				snake_case		:						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__    (  lowerCamelCase_				,  lowerCamelCase_				,  unittest.TestCase			):
			a__ :      int								=     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
			a__ :      Tuple								=     (TFMarianMTModel,) if is_tf_available() else ()
			a__ :      List[str]								=     (
			    {
			        """conversational""": TFMarianMTModel,
			        """feature-extraction""": TFMarianModel,
			        """summarization""": TFMarianMTModel,
			        """text2text-generation""": TFMarianMTModel,
			        """translation""": TFMarianMTModel,
			    }
			    if is_tf_available()
			    else {}
			)
			a__ :      Any								=     True
			a__ :      Optional[int]								=     False
			a__ :      str								=     False


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				TFMarianModelTester(self							)
					snake_case		:						Any								=				ConfigTester(self				, config_class=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self.config_tester.run_common_tests()



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[str]								=				self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE							)







@require_tf
class 			lowerCamelCase__    (  unittest.TestCase			):
			a__ :      List[Any]								=     10_00  # show more chars for failing integration tests


			@classmethod
			def 							lowerCamelCase_				(     cls							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
					return cls


			@cached_property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return AutoTokenizer.from_pretrained(self.model_name							)


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return self.tokenizer.eos_token_id


			@cached_property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					warnings.simplefilter("error"							)
					snake_case		:						TFMarianMTModel								=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
					assert isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				model.config
					self.assertListEqual(c.bad_words_ids				, [[c.pad_token_id]]							)
					self.assertEqual(c.max_length				, 512							)
					self.assertEqual(c.decoder_start_token_id				, c.pad_token_id							)
					return model


			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				self.translate_src_text(**SCREAMING_SNAKE_CASE							)
					self.assertListEqual(self.expected_text				, SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				self.tokenizer(self.src_text				, **SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, return_tensors="tf"							)
					snake_case		:						str								=				self.model.generate(
					    model_inputs.input_ids				, attention_mask=model_inputs.attention_mask				, num_beams=2				, max_length=128							)
					snake_case		:						Dict								=				self.tokenizer.batch_decode(generated_ids.numpy()				, skip_special_tokens=SCREAMING_SNAKE_CASE							)
					return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      str								=     """mt"""
			a__ :      List[Any]								=     """en"""
			a__ :      Dict								=     ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
			a__ :      Optional[int]								=     ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]


			@unittest.skip("Skipping until #12647 is resolved."							)
			@slow
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Dict								=     """en"""
			a__ :      Optional[Any]								=     """zh"""
			a__ :      Any								=     ["""My name is Wolfgang and I live in Berlin"""]
			a__ :      List[str]								=     ["""我叫沃尔夫冈 我住在柏林"""]


			@unittest.skip("Skipping until #12647 is resolved."							)
			@slow
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      int								=     """en"""
			a__ :      Optional[int]								=     """ROMANCE"""
			a__ :      Optional[Any]								=     [
			    """>>fr<< Don't spend so much time watching TV.""",
			    """>>pt<< Your message has been sent.""",
			    """>>es<< He's two years older than me.""",
			]
			a__ :      int								=     [
			    """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 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					self._assert_generated_batch_equal_expected()



			@unittest.skip("Skipping until #12647 is resolved."							)
			@slow
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Any								=				TranslationPipeline(self.model				, self.tokenizer				, framework="tf"							)
					snake_case		:						Tuple								=				pipeline(self.src_text							)
					self.assertEqual(self.expected_text				, [x["translation_text"] for x in output]							)







"""simple docstring"""

def   UpperCamelCase__     (		lowercase__  :     int   ):
		if not isinstance(lowercase__       ,     lowercase__   ):
				snake_case		:						Optional[Any]								=				F'''Input value of [number={number}] must be an integer'''
				raise TypeError(lowercase__   )
		if number < 0 or number % 2 == 1:
				return False
		snake_case		:						str								=				int(number**0.5   )
		return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

