
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 :
				_lowercase      		=MarianConfig
				_lowercase      		={}
				_lowercase      		='''gelu'''




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase=13    ,    _UpperCamelCase=7    ,    _UpperCamelCase=True    ,    _UpperCamelCase=False    ,    _UpperCamelCase=99    ,    _UpperCamelCase=32    ,    _UpperCamelCase=2    ,    _UpperCamelCase=4    ,    _UpperCamelCase=37    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=20    ,    _UpperCamelCase=2    ,    _UpperCamelCase=1    ,    _UpperCamelCase=0    ,    )    ->				int:
							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 					__a						( self					)    ->				Optional[int]:
							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(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							return config, inputs_dict




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Any:
							lowerCAmelCase_      =     TFMarianModel(config=_UpperCamelCase					).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(_UpperCamelCase    ,    attention_mask=_UpperCamelCase    ,    head_mask=_UpperCamelCase    ,    use_cache=_UpperCamelCase					)

							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(_UpperCamelCase    ,    attention_mask=_UpperCamelCase					)[0]
							lowerCAmelCase_      =     model(_UpperCamelCase    ,    attention_mask=_UpperCamelCase    ,    past_key_values=_UpperCamelCase					)[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(_UpperCamelCase    ,    _UpperCamelCase    ,    rtol=1e-3					)






def        lowerCamelCase__    (    __lowerCAmelCase  :  List[str]   ,							__lowerCAmelCase  :  Any   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Optional[Any]=None   ,							__lowerCAmelCase  :  Tuple=None   ,							__lowerCAmelCase  :  Tuple=None   ,							__lowerCAmelCase  :  Union[str, Any]=None   ,							__lowerCAmelCase  :  Any=None   ,							):




			"""simple docstring"""


			if attention_mask is None:
						lowerCAmelCase_      =     tf.cast(tf.math.not_equal(__lowerCAmelCase   ,							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      _lowerCAmelCase (     __a ,				__a ,				unittest.TestCase	):
				_lowercase      		=(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
				_lowercase      		=(TFMarianMTModel,) if is_tf_available() else ()
				_lowercase      		=(
				    {
				        '''conversational''': TFMarianMTModel,
				        '''feature-extraction''': TFMarianModel,
				        '''summarization''': TFMarianMTModel,
				        '''text2text-generation''': TFMarianMTModel,
				        '''translation''': TFMarianMTModel,
				    }
				    if is_tf_available()
				    else {}
				)
				_lowercase      		=True
				_lowercase      		=False
				_lowercase      		=False




				def 					__a						( self					)    ->				Optional[int]:
							lowerCAmelCase_      =     TFMarianModelTester(self					)
							lowerCAmelCase_      =     ConfigTester(self    ,    config_class=_UpperCamelCase					)




				def 					__a						( self					)    ->				List[Any]:
							self.config_tester.run_common_tests()




				def 					__a						( self					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase					)





@require_tf
class      _lowerCAmelCase (     unittest.TestCase	):
				_lowercase      		=10_00  # show more chars for failing integration tests




				@classmethod
				def 					__a						( cls					)    ->				None:
							lowerCAmelCase_      =     f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
							return cls




				@cached_property
				def 					__a						( self					)    ->				MarianTokenizer:
							return AutoTokenizer.from_pretrained(self.model_name					)




				@property
				def 					__a						( self					)    ->				int:
							return self.tokenizer.eos_token_id




				@cached_property
				def 					__a						( self					)    ->				Dict:
							warnings.simplefilter("error"					)
							lowerCAmelCase_      =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
							assert isinstance(_UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     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 					__a						( self    ,    **_UpperCamelCase					)    ->				Dict:
							lowerCAmelCase_      =     self.translate_src_text(**_UpperCamelCase					)
							self.assertListEqual(self.expected_text    ,    _UpperCamelCase					)




				def 					__a						( self    ,    **_UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     self.tokenizer(self.src_text    ,    **_UpperCamelCase    ,    padding=_UpperCamelCase    ,    return_tensors="tf"					)
							lowerCAmelCase_      =     self.model.generate(
							    model_inputs.input_ids    ,    attention_mask=model_inputs.attention_mask    ,    num_beams=2    ,    max_length=128					)
							lowerCAmelCase_      =     self.tokenizer.batch_decode(generated_ids.numpy()    ,    skip_special_tokens=_UpperCamelCase					)
							return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class      _lowerCAmelCase (     __a	):
				_lowercase      		='''mt'''
				_lowercase      		='''en'''
				_lowercase      		=['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
				_lowercase      		=['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




				@unittest.skip("Skipping until #12647 is resolved."					)
				@slow
				def 					__a						( self					)    ->				Optional[int]:
							self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class      _lowerCAmelCase (     __a	):
				_lowercase      		='''en'''
				_lowercase      		='''zh'''
				_lowercase      		=['''My name is Wolfgang and I live in Berlin''']
				_lowercase      		=['''我叫沃尔夫冈 我住在柏林''']




				@unittest.skip("Skipping until #12647 is resolved."					)
				@slow
				def 					__a						( self					)    ->				List[str]:
							self._assert_generated_batch_equal_expected()





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




				@unittest.skip("Skipping until #12647 is resolved."					)
				@slow
				def 					__a						( self					)    ->				Dict:
							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]					)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_A       =					{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					["XLMProphetNetTokenizer"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLMProphetNetDecoder",
			    "XLMProphetNetEncoder",
			    "XLMProphetNetForCausalLM",
			    "XLMProphetNetForConditionalGeneration",
			    "XLMProphetNetModel",
			    "XLMProphetNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_prophetnet import (
						    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMProphetNetDecoder,
						    XLMProphetNetEncoder,
						    XLMProphetNetForCausalLM,
						    XLMProphetNetForConditionalGeneration,
						    XLMProphetNetModel,
						    XLMProphetNetPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



