


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  __A   :

			'''simple docstring'''







			lowerCAmelCase_												=						MarianConfig
			lowerCAmelCase_												=						{}
			lowerCAmelCase_												=						"""gelu"""





			def __init__(   self	,  __lowerCAmelCase	,  __lowerCAmelCase=1_3	,  __lowerCAmelCase=7	,  __lowerCAmelCase=True	,  __lowerCAmelCase=False	,  __lowerCAmelCase=9_9	,  __lowerCAmelCase=3_2	,  __lowerCAmelCase=2	,  __lowerCAmelCase=4	,  __lowerCAmelCase=3_7	,  __lowerCAmelCase=0.1	,  __lowerCAmelCase=0.1	,  __lowerCAmelCase=2_0	,  __lowerCAmelCase=2	,  __lowerCAmelCase=1	,  __lowerCAmelCase=0	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		parent
				lowerCamelCase__        =		batch_size
				lowerCamelCase__        =		seq_length
				lowerCamelCase__        =		is_training
				lowerCamelCase__        =		use_labels
				lowerCamelCase__        =		vocab_size
				lowerCamelCase__        =		hidden_size
				lowerCamelCase__        =		num_hidden_layers
				lowerCamelCase__        =		num_attention_heads
				lowerCamelCase__        =		intermediate_size

				lowerCamelCase__        =		hidden_dropout_prob
				lowerCamelCase__        =		attention_probs_dropout_prob
				lowerCamelCase__        =		max_position_embeddings
				lowerCamelCase__        =		eos_token_id
				lowerCamelCase__        =		pad_token_id
				lowerCamelCase__        =		bos_token_id





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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

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

				lowerCamelCase__        =		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	,  )
				lowerCamelCase__        =		prepare_marian_inputs_dict(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				return config, inputs_dict





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		TFMarianModel(config=__lowerCAmelCase							).get_decoder()
				lowerCamelCase__        =		inputs_dict['''input_ids''']

				lowerCamelCase__        =		input_ids[:1, :]
				lowerCamelCase__        =		inputs_dict['''attention_mask'''][:1, :]
				lowerCamelCase__        =		inputs_dict['''head_mask''']
				lowerCamelCase__        =		1

				# first forward pass
				lowerCamelCase__        =		model(__lowerCAmelCase	,  attention_mask=__lowerCAmelCase	,  head_mask=__lowerCAmelCase	,  use_cache=__lowerCAmelCase							)

				lowerCamelCase__    ,     lowerCamelCase__        =		outputs.to_tuple()

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

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

				lowerCamelCase__        =		model(__lowerCAmelCase	,  attention_mask=__lowerCAmelCase							)[0]
				lowerCamelCase__        =		model(__lowerCAmelCase	,  attention_mask=__lowerCAmelCase	,  past_key_values=__lowerCAmelCase							)[0]

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

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

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





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case	,__snake_case=None	,__snake_case=None	,__snake_case=None	,__snake_case=None	,__snake_case=None	,)    ->				List[str]:







	'''simple docstring'''







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

			'''simple docstring'''







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





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		TFMarianModelTester(self							)
				lowerCamelCase__        =		ConfigTester(self	,  config_class=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase							)






@require_tf
class  __A   (   unittest.TestCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						1000  # show more chars for failing integration tests





			@classmethod
			def 		__lowerCamelCase    (   cls							):



				'''simple docstring'''







				lowerCamelCase__        =		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'''							)
				lowerCamelCase__        =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
				assert isinstance(__lowerCAmelCase	,  __lowerCAmelCase							)
				lowerCamelCase__        =		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 		__lowerCamelCase    (   self	,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.translate_src_text(**__lowerCAmelCase							)
				self.assertListEqual(self.expected_text	,  __lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  **__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.tokenizer(self.src_text	,  **__lowerCAmelCase	,  padding=__lowerCAmelCase	,  return_tensors='''tf'''							)
				lowerCamelCase__        =		self.model.generate(
				    model_inputs.input_ids	,  attention_mask=model_inputs.attention_mask	,  num_beams=2	,  max_length=1_2_8							)
				lowerCamelCase__        =		self.tokenizer.batch_decode(generated_ids.numpy()	,  skip_special_tokens=__lowerCAmelCase							)
				return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""mt"""
			lowerCAmelCase_												=						"""en"""
			lowerCAmelCase_												=						["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
			lowerCAmelCase_												=						["""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  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""en"""
			lowerCAmelCase_												=						"""zh"""
			lowerCAmelCase_												=						["""My name is Wolfgang and I live in Berlin"""]
			lowerCAmelCase_												=						["""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  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""en"""
			lowerCAmelCase_												=						"""ROMANCE"""
			lowerCAmelCase_												=						[
			    """>>fr<< Don't spend so much time watching TV.""",
			    """>>pt<< Your message has been sent.""",
			    """>>es<< He's two years older than me.""",
			]
			lowerCAmelCase_												=						[
			    """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'''







				lowerCamelCase__        =		TranslationPipeline(self.model	,  self.tokenizer	,  framework='''tf'''							)
				lowerCamelCase__        =		pipeline(self.src_text							)
				self.assertEqual(self.expected_text	,  [x['''translation_text'''] for x in output]							)










def lowerCAmelCase__(__snake_case							)    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		str(__snake_case							).strip()
	if not bin_string:
		raise ValueError('''Empty string was passed to the function'''							)
	lowerCamelCase__        =		bin_string[0] == '''-'''
	if is_negative:
		lowerCamelCase__        =		bin_string[1:]
	if not all(char in '''01''' for char in bin_string							):
		raise ValueError('''Non-binary value was passed to the function'''							)
	lowerCamelCase__        =		0
	for char in bin_string:
		lowerCamelCase__        =		2 * decimal_number + int(__snake_case							)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
			from doctest import testmod

			testmod()



