
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 							UpperCAmelCase   :




	'''simple docstring'''





	snake_case_      					=  MarianConfig
	snake_case_      					=  {}
	snake_case_      					=  "gelu"






	def __init__( self      :				Optional[int]							,A      :				Any							,A      :				List[Any]=13							,A      :				Optional[int]=7							,A      :				Any=True							,A      :				Optional[Any]=False							,A      :				int=99							,A      :				Tuple=32							,A      :				Dict=2							,A      :				Dict=4							,A      :				Tuple=37							,A      :				Union[str, Any]=0.1							,A      :				Optional[Any]=0.1							,A      :				int=20							,A      :				Tuple=2							,A      :				Dict=1							,A      :				int=0							,):
					__A         =				parent
					__A         =				batch_size
					__A         =				seq_length
					__A         =				is_training
					__A         =				use_labels
					__A         =				vocab_size
					__A         =				hidden_size
					__A         =				num_hidden_layers
					__A         =				num_attention_heads
					__A         =				intermediate_size

					__A         =				hidden_dropout_prob
					__A         =				attention_probs_dropout_prob
					__A         =				max_position_embeddings
					__A         =				eos_token_id
					__A         =				pad_token_id
					__A         =				bos_token_id






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

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

					__A         =				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							,)
					__A         =				prepare_marian_inputs_dict(A							,A							,A				)
					return config, inputs_dict







	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				Optional[Any]							,A      :				Union[str, Any]				):
					__A         =				TFMarianModel(config=A				).get_decoder()
					__A         =				inputs_dict["input_ids"]

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

					# first forward pass
					__A         =				model(A							,attention_mask=A							,head_mask=A							,use_cache=A				)

					__A    ,							__A         =				outputs.to_tuple()

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

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

					__A         =				model(A							,attention_mask=A				)[0]
					__A         =				model(A							,attention_mask=A							,past_key_values=A				)[0]

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

					# select random slice
					__A         =				int(ids_tensor((1,)							,output_from_past.shape[-1]				)				)
					__A         =				output_from_no_past[:, -3:, random_slice_idx]
					__A         =				output_from_past[:, :, random_slice_idx]

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





def 		UpperCAmelCase    (			a_	,     a_	,     a_	,     a_=None	,     a_=None	,     a_=None	,     a_=None	,     a_=None	,     )	->			Any:







				"""simple docstring"""



				if attention_mask is None:
								__A         =				tf.cast(tf.math.not_equal(a_	,     config.pad_token_id		)	,     tf.inta		)
				if decoder_attention_mask is None:
								__A         =				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:
								__A         =				tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
				if decoder_head_mask is None:
								__A         =				tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
				if cross_attn_head_mask is None:
								__A         =				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 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
	snake_case_      					=  (TFMarianMTModel,) if is_tf_available() else ()
	snake_case_      					=  (
	    {
	        "conversational": TFMarianMTModel,
	        "feature-extraction": TFMarianModel,
	        "summarization": TFMarianMTModel,
	        "text2text-generation": TFMarianMTModel,
	        "translation": TFMarianMTModel,
	    }
	    if is_tf_available()
	    else {}
	)
	snake_case_      					=  True
	snake_case_      					=  False
	snake_case_      					=  False






	def        UpperCamelCase_						( self      :				List[Any]				):
					__A         =				TFMarianModelTester(self				)
					__A         =				ConfigTester(self							,config_class=A				)






	def        UpperCamelCase_						( self      :				List[Any]				):
					self.config_tester.run_common_tests()







	def        UpperCamelCase_						( self      :				List[Any]				):
					__A         =				self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*A				)





@require_tf
class 							UpperCAmelCase   (						unittest.TestCase						):




	'''simple docstring'''





	snake_case_      					=  1000  # show more chars for failing integration tests






	@classmethod
	def        UpperCamelCase_						( cls      :				Tuple				):
					__A         =				f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
					return cls






	@cached_property
	def        UpperCamelCase_						( self      :				Any				):
					return AutoTokenizer.from_pretrained(self.model_name				)






	@property
	def        UpperCamelCase_						( self      :				Union[str, Any]				):
					return self.tokenizer.eos_token_id






	@cached_property
	def        UpperCamelCase_						( self      :				Optional[Any]				):
					warnings.simplefilter("error"				)
					__A         =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
					assert isinstance(A							,A				)
					__A         =				model.config
					self.assertListEqual(c.bad_words_ids							,[[c.pad_token_id]]				)
					self.assertEqual(c.max_length							,5_12				)
					self.assertEqual(c.decoder_start_token_id							,c.pad_token_id				)
					return model






	def        UpperCamelCase_						( self      :				Union[str, Any]							,**A      :				List[Any]				):
					__A         =				self.translate_src_text(**A				)
					self.assertListEqual(self.expected_text							,A				)







	def        UpperCamelCase_						( self      :				List[Any]							,**A      :				Optional[int]				):
					__A         =				self.tokenizer(self.src_text							,**A							,padding=A							,return_tensors="tf"				)
					__A         =				self.model.generate(
					    model_inputs.input_ids							,attention_mask=model_inputs.attention_mask							,num_beams=2							,max_length=1_28				)
					__A         =				self.tokenizer.batch_decode(generated_ids.numpy()							,skip_special_tokens=A				)
					return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  "mt"
	snake_case_      					=  "en"
	snake_case_      					=  ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
	snake_case_      					=  ["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      :				Union[str, Any]				):
					self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  "en"
	snake_case_      					=  "zh"
	snake_case_      					=  ["My name is Wolfgang and I live in Berlin"]
	snake_case_      					=  ["我叫沃尔夫冈 我住在柏林"]






	@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 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  "en"
	snake_case_      					=  "ROMANCE"
	snake_case_      					=  [
	    ">>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_      					=  [
	    "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      :				str				):
					self._assert_generated_batch_equal_expected()







	@unittest.skip("Skipping until #12647 is resolved."				)
	@slow
	def        UpperCamelCase_						( self      :				str				):
					__A         =				TranslationPipeline(self.model							,self.tokenizer							,framework="tf"				)
					__A         =				pipeline(self.src_text				)
					self.assertEqual(self.expected_text							,[x["translation_text"] for x in output]				)


from typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
