





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 SCREAMING_SNAKE_CASE_    :
						__magic_name__:      Union[str, Any]			   =							MarianConfig
						__magic_name__:      List[Any]			   =							{}
						__magic_name__:      Any			   =							"gelu"






						def __init__(   self   :   List[Any]		, _A   :   Dict		, _A   :   Dict=13		, _A   :   Dict=7		, _A   :   Optional[Any]=True		, _A   :   Union[str, Any]=False		, _A   :   Union[str, Any]=99		, _A   :   Optional[Any]=32		, _A   :   str=2		, _A   :   Optional[Any]=4		, _A   :   Tuple=37		, _A   :   List[Any]=0.1		, _A   :   List[Any]=0.1		, _A   :   Tuple=20		, _A   :   Optional[Any]=2		, _A   :   Optional[int]=1		, _A   :   Tuple=0		, )				->     List[Any]:







							"""simple docstring"""


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

							snake_case_		:      Optional[int]           =						hidden_dropout_prob
							snake_case_		:      List[str]           =						attention_probs_dropout_prob
							snake_case_		:      List[str]           =						max_position_embeddings
							snake_case_		:      Dict           =						eos_token_id
							snake_case_		:      Optional[int]           =						pad_token_id
							snake_case_		:      List[str]           =						bos_token_id






						def UpperCAmelCase_					(   self   :   Dict						)				->     Dict:







							"""simple docstring"""


							snake_case_		:      int           =						ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size						)
							snake_case_		:      List[str]           =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)		, 1						)
							snake_case_		:      Dict           =						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_		:      Optional[Any]           =						prepare_marian_inputs_dict(_A		, _A		, _A						)
							return config, inputs_dict






						def UpperCAmelCase_					(   self   :   Dict		, _A   :   int		, _A   :   Optional[Any]						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      Dict           =						TFMarianModel(config=_A						).get_decoder()
							snake_case_		:      str           =						inputs_dict['input_ids']

							snake_case_		:      List[str]           =						input_ids[:1, :]
							snake_case_		:      int           =						inputs_dict['attention_mask'][:1, :]
							snake_case_		:      str           =						inputs_dict['head_mask']
							snake_case_		:      Optional[int]           =						1

							# first forward pass
							snake_case_		:      Optional[int]           =						model(_A		, attention_mask=_A		, head_mask=_A		, use_cache=_A						)

							snake_case_    ,snake_case_		:      str           =						outputs.to_tuple()

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

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

							snake_case_		:      Optional[Any]           =						model(_A		, attention_mask=_A						)[0]
							snake_case_		:      Union[str, Any]           =						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
							snake_case_		:      str           =						int(ids_tensor((1,)		, output_from_past.shape[-1]						)						)
							snake_case_		:      Optional[Any]           =						output_from_no_past[:, -3:, random_slice_idx]
							snake_case_		:      Union[str, Any]           =						output_from_past[:, :, random_slice_idx]

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


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ,			__a=None ,			__a=None ,			__a=None ,			__a=None ,			__a=None ,			):
	if attention_mask is None:
		snake_case_		:      Optional[Any]           =						tf.cast(tf.math.not_equal(__a ,			config.pad_token_id ) ,			tf.inta )
	if decoder_attention_mask is None:
		snake_case_		:      Dict           =						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_		:      Dict           =						tf.ones((config.encoder_layers, config.encoder_attention_heads) )
	if decoder_head_mask is None:
		snake_case_		:      Optional[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 SCREAMING_SNAKE_CASE_    (       snake_case_							,					snake_case_							,					unittest.TestCase       ):
						__magic_name__:      Dict			   =							(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
						__magic_name__:      Union[str, Any]			   =							(TFMarianMTModel,) if is_tf_available() else ()
						__magic_name__:      int			   =							(
						    {
						        "conversational": TFMarianMTModel,
						        "feature-extraction": TFMarianModel,
						        "summarization": TFMarianMTModel,
						        "text2text-generation": TFMarianMTModel,
						        "translation": TFMarianMTModel,
						    }
						    if is_tf_available()
						    else {}
						)
						__magic_name__:      List[str]			   =							True
						__magic_name__:      Union[str, Any]			   =							False
						__magic_name__:      int			   =							False






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     str:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						TFMarianModelTester(self						)
							snake_case_		:      Optional[Any]           =						ConfigTester(self		, config_class=_A						)






						def UpperCAmelCase_					(   self   :   List[Any]						)				->     List[str]:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      int           =						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_A						)
@require_tf
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):
						__magic_name__:      str			   =							1000  # show more chars for failing integration tests






						@classmethod
						def UpperCAmelCase_					(   cls   :   List[str]						)				->     None:







							"""simple docstring"""


							snake_case_		:      List[str]           =						F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
							return cls






						@cached_property
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     MarianTokenizer:







							"""simple docstring"""


							return AutoTokenizer.from_pretrained(self.model_name						)






						@property
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     int:







							"""simple docstring"""


							return self.tokenizer.eos_token_id






						@cached_property
						def UpperCAmelCase_					(   self   :   str						)				->     Optional[Any]:







							"""simple docstring"""


							warnings.simplefilter('error'						)
							snake_case_		:      TFMarianMTModel           =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
							assert isinstance(_A		, _A						)
							snake_case_		:      List[str]           =						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 UpperCAmelCase_					(   self   :   Any		, **_A   :   Tuple						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						self.translate_src_text(**_A						)
							self.assertListEqual(self.expected_text		, _A						)






						def UpperCAmelCase_					(   self   :   List[Any]		, **_A   :   int						)				->     Any:







							"""simple docstring"""


							snake_case_		:      List[str]           =						self.tokenizer(self.src_text		, **_A		, padding=_A		, return_tensors='tf'						)
							snake_case_		:      Optional[int]           =						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=_A						)
							return generated_words
@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      str			   =							"mt"
						__magic_name__:      Dict			   =							"en"
						__magic_name__:      Optional[Any]			   =							["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
						__magic_name__:      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 UpperCAmelCase_					(   self   :   int						)				->     List[Any]:







							"""simple docstring"""


							self._assert_generated_batch_equal_expected()
@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      List[str]			   =							"en"
						__magic_name__:      int			   =							"zh"
						__magic_name__:      List[Any]			   =							["My name is Wolfgang and I live in Berlin"]
						__magic_name__:      int			   =							["我叫沃尔夫冈 我住在柏林"]






						@unittest.skip('Skipping until #12647 is resolved.'						)
						@slow
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     Dict:







							"""simple docstring"""


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







							"""simple docstring"""


							self._assert_generated_batch_equal_expected()






						@unittest.skip('Skipping until #12647 is resolved.'						)
						@slow
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     int:







							"""simple docstring"""


							snake_case_		:      Dict           =						TranslationPipeline(self.model		, self.tokenizer		, framework='tf'						)
							snake_case_		:      List[str]           =						pipeline(self.src_text						)
							self.assertEqual(self.expected_text		, [x['translation_text'] for x in output]						)









'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  import torch

  from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 					__UpperCAmelCase (		unittest.TestCase	):

      '''simple docstring'''





      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	CamembertModel.from_pretrained('''camembert-base'''						)
             model.to(_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	torch.tensor(
                 [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,		device=_UpperCAmelCase  ,		dtype=torch.long  ,		)  # J'aime le camembert !
             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	model(_UpperCAmelCase						)['''last_hidden_state''']
             UpperCAmelCase__	:   List[str]												=	torch.Size((1, 10, 768)						)
             self.assertEqual(output.shape  ,		_UpperCAmelCase						)
             # compare the actual values for a slice.
             UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                 [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]  ,		device=_UpperCAmelCase  ,		dtype=torch.float  ,		)
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		_UpperCAmelCase  ,		atol=1E-4						)						)