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 			__magic_name__	:





   """simple docstring"""




   lowerCAmelCase   :     List[str]	      = MBartConfig
   lowerCAmelCase   :     Optional[Any]	      = {}
   lowerCAmelCase   :     Tuple	      = '''gelu'''



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



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			parent
    _UpperCamelCase:  List[str]    						=			batch_size
    _UpperCamelCase:  Any    						=			seq_length
    _UpperCamelCase:  str    						=			is_training
    _UpperCamelCase:  int    						=			use_labels
    _UpperCamelCase:  str    						=			vocab_size
    _UpperCamelCase:  Optional[int]    						=			hidden_size
    _UpperCamelCase:  List[str]    						=			num_hidden_layers
    _UpperCamelCase:  Dict    						=			num_attention_heads
    _UpperCamelCase:  Optional[int]    						=			intermediate_size
    _UpperCamelCase:  int    						=			hidden_dropout_prob
    _UpperCamelCase:  Dict    						=			attention_probs_dropout_prob
    _UpperCamelCase:  int    						=			max_position_embeddings
    _UpperCamelCase:  Optional[Any]    						=			eos_token_id
    _UpperCamelCase:  Optional[Any]    						=			pad_token_id
    _UpperCamelCase:  List[str]    						=			bos_token_id



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			ids_tensor([self.batch_size, self.seq_length - 1]		,	self.vocab_size				)
    _UpperCamelCase:  Union[str, Any]    						=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,	1				)
    _UpperCamelCase:  str    						=			tf.concat([input_ids, eos_tensor]		,	axis=1				)

    _UpperCamelCase:  Union[str, Any]    						=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size				)

    _UpperCamelCase:  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		,	)
    _UpperCamelCase:  Tuple    						=			prepare_mbart_inputs_dict(_lowercase		,	_lowercase		,	_lowercase				)
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	int		,	_lowercase       :	Optional[Any]		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			TFMBartModel(config=_lowercase				).get_decoder()
    _UpperCamelCase:  Any    						=			inputs_dict['''input_ids''']

    _UpperCamelCase:  int    						=			input_ids[:1, :]
    _UpperCamelCase:  Any    						=			inputs_dict['''attention_mask'''][:1, :]
    _UpperCamelCase:  Tuple    						=			inputs_dict['''head_mask''']
    _UpperCamelCase:  int    						=			1

    # first forward pass
    _UpperCamelCase:  Any    						=			model(_lowercase		,	attention_mask=_lowercase		,	head_mask=_lowercase		,	use_cache=_lowercase				)

    _UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			outputs.to_tuple()
    _UpperCamelCase:  str    						=			past_key_values[1]




def    lowerCAmelCase_  (		lowercase:				Dict	,       lowercase:				Union[str, Any]	,       lowercase:				List[Any]	,       lowercase:				str=None	,       lowercase:				Tuple=None	,       lowercase:				Any=None	,       lowercase:				Union[str, Any]=None	,       lowercase:				Union[str, Any]=None	,       )     ->					Dict:







 '''simple docstring'''
 if attention_mask is None:
  _UpperCamelCase:  Union[str, Any]    						=			tf.cast(tf.math.not_equal(lowercase	,       config.pad_token_id		)	,       tf.inta		)
 if decoder_attention_mask is None:
  _UpperCamelCase:  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:
  _UpperCamelCase:  Optional[int]    						=			tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  _UpperCamelCase:  Any    						=			tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
 if cross_attn_head_mask is None:
  _UpperCamelCase:  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 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     List[Any]	      = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
   lowerCAmelCase   :     Optional[Any]	      = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
   lowerCAmelCase   :     Any	      = (
       {
           '''conversational''': TFMBartForConditionalGeneration,
           '''feature-extraction''': TFMBartModel,
           '''summarization''': TFMBartForConditionalGeneration,
           '''text2text-generation''': TFMBartForConditionalGeneration,
           '''translation''': TFMBartForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase   :     Union[str, Any]	      = True
   lowerCAmelCase   :     int	      = False
   lowerCAmelCase   :     int	      = False



   def 	lowerCAmelCase    (						self       :	Any		,	_lowercase       :	Optional[int]		,	_lowercase       :	int		,	_lowercase       :	List[Any]		,	_lowercase       :	Union[str, Any]		,	_lowercase       :	Tuple				):



    """simple docstring"""



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

    return False



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			TFMBartModelTester(self				)
    _UpperCamelCase:  List[Any]    						=			ConfigTester(self		,	config_class=_lowercase				)



   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_lowercase				)




@require_sentencepiece
@require_tokenizers
@require_tf
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Tuple	      = [
       ''' UN Chief Says There Is No Military Solution in Syria''',
   ]
   lowerCAmelCase   :     Optional[int]	      = [
       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
   ]
   lowerCAmelCase   :     List[Any]	      = '''facebook/mbart-large-en-ro'''



   @cached_property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    return AutoTokenizer.from_pretrained(self.model_name				)



   @cached_property
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
    return model



   def 	lowerCAmelCase    (						self       :	Optional[Any]		,	**_lowercase       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			self.translate_src_text(**_lowercase				)
    self.assertListEqual(self.expected_text		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Dict		,	**_lowercase       :	List[Any]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			self.tokenizer(self.src_text		,	**_lowercase		,	return_tensors='''tf'''				)
    _UpperCamelCase:  Any    						=			self.model.generate(
        model_inputs.input_ids		,	attention_mask=model_inputs.attention_mask		,	num_beams=2				)
    _UpperCamelCase:  str    						=			self.tokenizer.batch_decode(_lowercase		,	skip_special_tokens=_lowercase				)
    return generated_words



   @slow
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    self._assert_generated_batch_equal_expected()
from __future__ import annotations




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				list[str] | None = None	,       lowercase:				dict[str, float] | None = None	,       lowercase:				bool = False	,       )     ->					tuple[int, float, str]:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			cipher_alphabet or [chr(lowercase		) for i in range(97	,       123		)]

 # If the argument is None or the user provided an empty dictionary
 if not frequencies_dict:
  # Frequencies of letters in the english language (how much they show up)
  _UpperCamelCase:  Any    						=			{
      '''a''': 0.08497,
      '''b''': 0.01492,
      '''c''': 0.02202,
      '''d''': 0.04253,
      '''e''': 0.11162,
      '''f''': 0.02228,
      '''g''': 0.02015,
      '''h''': 0.06094,
      '''i''': 0.07546,
      '''j''': 0.00153,
      '''k''': 0.01292,
      '''l''': 0.04025,
      '''m''': 0.02406,
      '''n''': 0.06749,
      '''o''': 0.07507,
      '''p''': 0.01929,
      '''q''': 0.00095,
      '''r''': 0.07587,
      '''s''': 0.06327,
      '''t''': 0.09356,
      '''u''': 0.02758,
      '''v''': 0.00978,
      '''w''': 0.02560,
      '''x''': 0.00150,
      '''y''': 0.01994,
      '''z''': 0.00077,
  }
 else:
  # Custom frequencies dictionary
  _UpperCamelCase:  str    						=			frequencies_dict

 if not case_sensitive:
  _UpperCamelCase:  Union[str, Any]    						=			ciphertext.lower()

 # Chi squared statistic values
 _UpperCamelCase:  dict[int, tuple[float, str]]    						=			{}

 # cycle through all of the shifts
 for shift in range(len(lowercase		)		):
  _UpperCamelCase:  Tuple    						=			''''''

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    _UpperCamelCase:  Optional[int]    						=			(alphabet_letters.index(letter.lower()		) - shift) % len(
        lowercase		)
    decrypted_with_shift += (
        alphabet_letters[new_key].upper()
        if case_sensitive and letter.isupper()
        else alphabet_letters[new_key]
    )
   except ValueError:
    # Append the character if it isn't in the alphabet
    decrypted_with_shift += letter

  _UpperCamelCase:  Optional[int]    						=			0.0

  # Loop through each letter in the decoded message with the shift
  for letter in decrypted_with_shift:
   if case_sensitive:
    _UpperCamelCase:  Optional[int]    						=			letter.lower()
    if letter in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  Any    						=			decrypted_with_shift.lower().count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  int    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  int    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value
   else:
    if letter.lower() in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  List[Any]    						=			decrypted_with_shift.count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  Union[str, Any]    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  List[str]    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
  _UpperCamelCase:  List[Any]    						=			(
      chi_squared_statistic,
      decrypted_with_shift,
  )

 # Get the most likely cipher by finding the cipher with the smallest chi squared
 # statistic
 def chi_squared_statistic_values_sorting_key(lowercase:				int		) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 _UpperCamelCase:  int    						=			min(
     lowercase	,       key=lowercase	,       )

 # Get all the data from the most likely cipher (key, decoded message)
 (
     (
     _UpperCamelCase
 )						,				(
     _UpperCamelCase
 )						,				
 ):  str    						=			chi_squared_statistic_values[most_likely_cipher]

 # Return the data on the most likely shift
 return (
     most_likely_cipher,
     most_likely_cipher_chi_squared_value,
     decoded_most_likely_cipher,
 )