
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	:
					a__: str			  =				MarianConfig
					a__: Dict			  =				{}
					a__: Union[str, Any]			  =				"""gelu"""

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

											lowercase       :    Any											=  hidden_dropout_prob
											lowercase       :    Optional[Any]											=  attention_probs_dropout_prob
											lowercase       :    Optional[int]											=  max_position_embeddings
											lowercase       :    List[str]											=  eos_token_id
											lowercase       :    str											=  pad_token_id
											lowercase       :    str											=  bos_token_id

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

											lowercase       :    Dict											=  ids_tensor([self.batch_size, self.seq_length] ,      self.vocab_size					)

											lowercase       :    List[Any]											=  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 ,      )
											lowercase       :    int											=  prepare_marian_inputs_dict(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					)
											return config, inputs_dict




					def        _lowerCAmelCase	(			self      :					Tuple ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Tuple					):
											lowercase       :    List[Any]											=  TFMarianModel(config=lowerCAmelCase					).get_decoder()
											lowercase       :    Tuple											=  inputs_dict['''input_ids''']

											lowercase       :    int											=  input_ids[:1, :]
											lowercase       :    Union[str, Any]											=  inputs_dict['''attention_mask'''][:1, :]
											lowercase       :    str											=  inputs_dict['''head_mask''']
											lowercase       :    Tuple											=  1

											# first forward pass
											lowercase       :    List[str]											=  model(lowerCAmelCase ,      attention_mask=lowerCAmelCase ,      head_mask=lowerCAmelCase ,      use_cache=lowerCAmelCase					)

											lowercase       :    Optional[Any]											=  outputs.to_tuple()

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

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

											lowercase       :    Any											=  model(lowerCAmelCase ,      attention_mask=lowerCAmelCase					)[0]
											lowercase       :    List[Any]											=  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
											lowercase       :    int											=  int(ids_tensor((1,) ,      output_from_past.shape[-1]					)					)
											lowercase       :    Optional[Any]											=  output_from_no_past[:, -3:, random_slice_idx]
											lowercase       :    int											=  output_from_past[:, :, random_slice_idx]

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





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

					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    Dict											=  TFMarianModelTester(self					)
											lowercase       :    Optional[Any]											=  ConfigTester(self ,      config_class=lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					Tuple					):
											self.config_tester.run_common_tests()




					def        _lowerCAmelCase	(			self      :					Dict					):
											lowercase       :    Union[str, Any]											=  self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase					)







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

					@classmethod
					def        _lowerCAmelCase	(			cls      :					str					):
											lowercase       :    Optional[int]											=  f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
											return cls

					@cached_property
					def        _lowerCAmelCase	(			self      :					str					):
											return AutoTokenizer.from_pretrained(self.model_name					)

					@property
					def        _lowerCAmelCase	(			self      :					int					):
											return self.tokenizer.eos_token_id

					@cached_property
					def        _lowerCAmelCase	(			self      :					int					):
											warnings.simplefilter('''error'''					)
											lowercase       :    TFMarianMTModel											=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
											assert isinstance(lowerCAmelCase ,      lowerCAmelCase					)
											lowercase       :    int											=  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      :					List[Any] ,      **lowerCAmelCase      :					Dict					):
											lowercase       :    Optional[Any]											=  self.translate_src_text(**lowerCAmelCase					)
											self.assertListEqual(self.expected_text ,      lowerCAmelCase					)




					def        _lowerCAmelCase	(			self      :					List[str] ,      **lowerCAmelCase      :					Union[str, Any]					):
											lowercase       :    List[Any]											=  self.tokenizer(self.src_text ,      **lowerCAmelCase ,      padding=lowerCAmelCase ,      return_tensors='''tf'''					)
											lowercase       :    Any											=  self.model.generate(
											    model_inputs.input_ids ,      attention_mask=model_inputs.attention_mask ,      num_beams=2 ,      max_length=128					)
											lowercase       :    Optional[Any]											=  self.tokenizer.batch_decode(generated_ids.numpy() ,      skip_special_tokens=lowerCAmelCase					)
											return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: Any			  =				"""mt"""
					a__: List[Any]			  =				"""en"""
					a__: Union[str, Any]			  =				["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
					a__: str			  =				["""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      :					List[Any]					):
											self._assert_generated_batch_equal_expected()







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

					@unittest.skip('''Skipping until #12647 is resolved.'''					)
					@slow
					def        _lowerCAmelCase	(			self      :					Dict					):
											self._assert_generated_batch_equal_expected()







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




					@unittest.skip('''Skipping until #12647 is resolved.'''					)
					@slow
					def        _lowerCAmelCase	(			self      :					Union[str, Any]					):
											lowercase       :    Dict											=  TranslationPipeline(self.model ,      self.tokenizer ,      framework='''tf'''					)
											lowercase       :    Optional[Any]											=  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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

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


