



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							:
   A					=  MarianConfig
   A					=  {}
   A					=  '''gelu'''

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

      UpperCAmelCase_:		Dict       			=    hidden_dropout_prob
      UpperCAmelCase_:		Union[str, Any]       			=    attention_probs_dropout_prob
      UpperCAmelCase_:		Union[str, Any]       			=    max_position_embeddings
      UpperCAmelCase_:		List[str]       			=    eos_token_id
      UpperCAmelCase_:		Optional[Any]       			=    pad_token_id
      UpperCAmelCase_:		Optional[Any]       			=    bos_token_id

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

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

      UpperCAmelCase_:		Optional[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,      )
      UpperCAmelCase_:		Tuple       			=    prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
      return config, inputs_dict

   def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
      UpperCAmelCase_:		Tuple       			=    TFMarianModel(config=SCREAMING_SNAKE_CASE_				).get_decoder()
      UpperCAmelCase_:		Tuple       			=    inputs_dict["""input_ids"""]

      UpperCAmelCase_:		Optional[int]       			=    input_ids[:1, :]
      UpperCAmelCase_:		Optional[int]       			=    inputs_dict["""attention_mask"""][:1, :]
      UpperCAmelCase_:		Tuple       			=    inputs_dict["""head_mask"""]
      UpperCAmelCase_:		Optional[Any]       			=    1

      # first forward pass
      UpperCAmelCase_:		Union[str, Any]       			=    model(SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_,      head_mask=SCREAMING_SNAKE_CASE_,      use_cache=SCREAMING_SNAKE_CASE_				)

      UpperCAmelCase_:		int       			=    outputs.to_tuple()

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

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

      UpperCAmelCase_:		str       			=    model(SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_				)[0]
      UpperCAmelCase_:		List[Any]       			=    model(SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_,      past_key_values=SCREAMING_SNAKE_CASE_				)[0]

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

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

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







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Any		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Dict=None		,	lowerCAmelCase__:		Union[str, Any]=None		,	lowerCAmelCase__:		str=None		,	lowerCAmelCase__:		Any=None		,	lowerCAmelCase__:		Optional[int]=None		,	):





   """simple docstring"""


   if attention_mask is None:
      UpperCAmelCase_:		Dict       			=    tf.cast(tf.math.not_equal(lowerCAmelCase__		,	config.pad_token_id     )		,	tf.inta     )
   if decoder_attention_mask is None:
      UpperCAmelCase_:		List[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:
      UpperCAmelCase_:		Any       			=    tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
   if decoder_head_mask is None:
      UpperCAmelCase_:		List[Any]       			=    tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
   if cross_attn_head_mask is None:
      UpperCAmelCase_:		Optional[int]       			=    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				):
   A					=  (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   A					=  (TFMarianMTModel,) if is_tf_available() else ()
   A					=  (
       {
           '''conversational''': TFMarianMTModel,
           '''feature-extraction''': TFMarianModel,
           '''summarization''': TFMarianMTModel,
           '''text2text-generation''': TFMarianMTModel,
           '''translation''': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   A					=  True
   A					=  False
   A					=  False

   def     __snake_case					(self				)					->      Tuple:
      UpperCAmelCase_:		Union[str, Any]       			=    TFMarianModelTester(self				)
      UpperCAmelCase_:		List[Any]       			=    ConfigTester(self,      config_class=SCREAMING_SNAKE_CASE_				)

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

   def     __snake_case					(self				)					->      Tuple:
      UpperCAmelCase_:		List[Any]       			=    self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_				)







@require_tf
class 							_a							(      unittest.TestCase				):
   A					=  1000  # show more chars for failing integration tests

   @classmethod
   def     __snake_case					(cls				)					->      None:
      UpperCAmelCase_:		List[str]       			=    f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
      return cls

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

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

   @cached_property
   def     __snake_case					(self				)					->      List[str]:
      warnings.simplefilter("""error"""				)
      UpperCAmelCase_:		TFMarianMTModel       			=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
      assert isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
      UpperCAmelCase_:		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     __snake_case					(self,      **SCREAMING_SNAKE_CASE_				)					->      str:
      UpperCAmelCase_:		List[str]       			=    self.translate_src_text(**SCREAMING_SNAKE_CASE_				)
      self.assertListEqual(self.expected_text,      SCREAMING_SNAKE_CASE_				)

   def     __snake_case					(self,      **SCREAMING_SNAKE_CASE_				)					->      Tuple:
      UpperCAmelCase_:		str       			=    self.tokenizer(self.src_text,      **SCREAMING_SNAKE_CASE_,      padding=SCREAMING_SNAKE_CASE_,      return_tensors="""tf"""				)
      UpperCAmelCase_:		Any       			=    self.model.generate(
          model_inputs.input_ids,      attention_mask=model_inputs.attention_mask,      num_beams=2,      max_length=128				)
      UpperCAmelCase_:		Optional[Any]       			=    self.tokenizer.batch_decode(generated_ids.numpy(),      skip_special_tokens=SCREAMING_SNAKE_CASE_				)
      return generated_words







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

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







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

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







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

   @unittest.skip("""Skipping until #12647 is resolved."""				)
   @slow
   def     __snake_case					(self				)					->      Optional[Any]:
      UpperCAmelCase_:		Any       			=    TranslationPipeline(self.model,      self.tokenizer,      framework="""tf"""				)
      UpperCAmelCase_:		Union[str, 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_tf_available, is_torch_available


a			:     Union[str, Any]     = {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Optional[int]     = [
	    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'FlaubertForMultipleChoice',
	    'FlaubertForQuestionAnswering',
	    'FlaubertForQuestionAnsweringSimple',
	    'FlaubertForSequenceClassification',
	    'FlaubertForTokenClassification',
	    'FlaubertModel',
	    'FlaubertWithLMHeadModel',
	    'FlaubertPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:     Dict     = [
	    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFFlaubertForMultipleChoice',
	    'TFFlaubertForQuestionAnsweringSimple',
	    'TFFlaubertForSequenceClassification',
	    'TFFlaubertForTokenClassification',
	    'TFFlaubertModel',
	    'TFFlaubertPreTrainedModel',
	    'TFFlaubertWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	a			:     Tuple     = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






