
"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class __lowerCAmelCase		:
     lowercase						       =						MBartConfig
     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     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	seq_length
      __UpperCamelCase      =	is_training
      __UpperCamelCase      =	use_labels
      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	intermediate_size
      __UpperCamelCase      =	hidden_dropout_prob
      __UpperCamelCase      =	attention_probs_dropout_prob
      __UpperCamelCase      =	max_position_embeddings
      __UpperCamelCase      =	eos_token_id
      __UpperCamelCase      =	pad_token_id
      __UpperCamelCase      =	bos_token_id





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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

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

      __UpperCamelCase      =	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      =	prepare_mbart_inputs_dict(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      return config, inputs_dict







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	TFMBartModel(config=__UpperCAmelCase				).get_decoder()
      __UpperCamelCase      =	inputs_dict['input_ids']

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

      # first forward pass
      __UpperCamelCase      =	model(__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase     ,					head_mask=__UpperCAmelCase     ,					use_cache=__UpperCAmelCase				)

      __UpperCamelCase  ,     __UpperCamelCase      =	outputs.to_tuple()
      __UpperCamelCase      =	past_key_values[1]






def 							A						(    snake_case				:int    , snake_case				:Union[str, Any]    , snake_case				:Dict    , snake_case				:Union[str, Any]=None    , snake_case				:Union[str, Any]=None    , snake_case				:Dict=None    , snake_case				:List[Any]=None    , snake_case				:Tuple=None    , )				->     Optional[int]:
 if attention_mask is None:
  __UpperCamelCase      =	tf.cast(tf.math.not_equal(snake_case    , config.pad_token_id		)    , tf.inta		)
 if decoder_attention_mask is None:
  __UpperCamelCase      =	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      =	tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  __UpperCamelCase      =	tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
 if cross_attn_head_mask is None:
  __UpperCamelCase      =	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		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
     lowercase						       =						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
     lowercase						       =						(
         {
             "conversational": TFMBartForConditionalGeneration,
             "feature-extraction": TFMBartModel,
             "summarization": TFMBartForConditionalGeneration,
             "text2text-generation": TFMBartForConditionalGeneration,
             "translation": TFMBartForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     lowercase						       =						True
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
       # Exception encountered when calling layer '...'
       return True

      return False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFMBartModelTester(self				)
      __UpperCamelCase      =	ConfigTester(self     ,					config_class=__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.config_tester.run_common_tests()







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase				)


@require_sentencepiece
@require_tokenizers
@require_tf
class __lowerCAmelCase		(				unittest.TestCase      ):
     lowercase						       =						[
         " UN Chief Says There Is No Military Solution in Syria",
     ]
     lowercase						       =						[
         "Şeful ONU declară că nu există o soluţie militară în Siria",
     ]
     lowercase						       =						"facebook/mbart-large-en-ro"





     @cached_property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return AutoTokenizer.from_pretrained(self.model_name				)





     @cached_property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
      return model





     def 						UpperCAmelCase (     self     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.translate_src_text(**__UpperCAmelCase				)
      self.assertListEqual(self.expected_text     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.tokenizer(self.src_text     ,					**__UpperCAmelCase     ,					return_tensors='tf'				)
      __UpperCamelCase      =	self.model.generate(
          model_inputs.input_ids     ,					attention_mask=model_inputs.attention_mask     ,					num_beams=2				)
      __UpperCamelCase      =	self.tokenizer.batch_decode(__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase				)
      return generated_words







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._assert_generated_batch_equal_expected()





"""simple docstring"""

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	ArgumentParser('Transformers CLI tool'    , usage='transformers-cli <command> [<args>]'		)
 __UpperCamelCase      =	parser.add_subparsers(help='transformers-cli command helpers'		)

 # Register commands
 ConvertCommand.register_subcommand(snake_case		)
 DownloadCommand.register_subcommand(snake_case		)
 EnvironmentCommand.register_subcommand(snake_case		)
 RunCommand.register_subcommand(snake_case		)
 ServeCommand.register_subcommand(snake_case		)
 UserCommands.register_subcommand(snake_case		)
 AddNewModelCommand.register_subcommand(snake_case		)
 AddNewModelLikeCommand.register_subcommand(snake_case		)
 LfsCommands.register_subcommand(snake_case		)
 PTtoTFCommand.register_subcommand(snake_case		)

 # Let's go
 __UpperCamelCase      =	parser.parse_args()

 if not hasattr(snake_case    , 'func'		):
  parser.print_help()
  exit(1		)

 # Run
 __UpperCamelCase      =	args.func(snake_case		)
 service.run()


if __name__ == "__main__":
      main()



