


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     _lowercase				:


       """simple docstring"""




       A__      		=    MBartConfig
       A__      		=    {}
       A__      		=    "gelu"





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


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



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

             lowerCamelCase__	:       Optional[Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

             lowerCamelCase__	:       Dict					=						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__	:       Any					=						prepare_mbart_inputs_dict(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             return config, inputs_dict





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFMBartModel(config=__lowerCamelCase ).get_decoder()
             lowerCamelCase__	:       Any					=						inputs_dict["input_ids"]

             lowerCamelCase__	:       Optional[int]					=						input_ids[:1, :]
             lowerCamelCase__	:       Any					=						inputs_dict["attention_mask"][:1, :]
             lowerCamelCase__	:       List[str]					=						inputs_dict["head_mask"]
             lowerCamelCase__	:       Dict					=						1

             # first forward pass
             lowerCamelCase__	:       str					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   head_mask=__lowerCamelCase      ,   use_cache=__lowerCamelCase )

             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						outputs.to_tuple()
             lowerCamelCase__	:       Optional[int]					=						past_key_values[1]



def    lowercase_	(	_A    :					Any					,      _A    :					Dict					,      _A    :					Any					,      _A    :					List[Any]=None					,      _A    :					str=None					,      _A    :					Any=None					,      _A    :					List[Any]=None					,      _A    :					Optional[Any]=None					,      ):

      """simple docstring"""




      if attention_mask is None:
            lowerCamelCase__	:       List[str]					=						tf.cast(tf.math.not_equal(_A					,      config.pad_token_id		)					,      tf.inta		)
      if decoder_attention_mask is None:
            lowerCamelCase__	:       Optional[int]					=						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__	:       int					=						tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
            lowerCamelCase__	:       Any					=						tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
            lowerCamelCase__	:       Dict					=						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     _lowercase				(							lowercase__							,						lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
       A__      		=    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
       A__      		=    (
           {
               "conversational": TFMBartForConditionalGeneration,
               "feature-extraction": TFMBartModel,
               "summarization": TFMBartForConditionalGeneration,
               "text2text-generation": TFMBartForConditionalGeneration,
               "translation": TFMBartForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       A__      		=    True
       A__      		=    False
       A__      		=    False





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    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'''



             lowerCamelCase__	:       List[str]					=						TFMBartModelTester(self )
             lowerCamelCase__	:       str					=						ConfigTester(self      ,   config_class=__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase )







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


       """simple docstring"""




       A__      		=    [
           " UN Chief Says There Is No Military Solution in Syria",
       ]
       A__      		=    [
           "Şeful ONU declară că nu există o soluţie militară în Siria",
       ]
       A__      		=    "facebook/mbart-large-en-ro"





       @cached_property
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return AutoTokenizer.from_pretrained(self.model_name )





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


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
             return model





       def 		lowerCAmelCase  (     self							:    Any      ,   **__lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.translate_src_text(**__lowerCamelCase )
             self.assertListEqual(self.expected_text      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   **__lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.tokenizer(self.src_text      ,   **__lowerCamelCase      ,   return_tensors="tf" )
             lowerCamelCase__	:       Tuple					=						self.model.generate(
                 model_inputs.input_ids      ,   attention_mask=model_inputs.attention_mask      ,   num_beams=2 )
             lowerCamelCase__	:       List[Any]					=						self.tokenizer.batch_decode(__lowerCamelCase      ,   skip_special_tokens=__lowerCamelCase )
             return generated_words





       @slow
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             self._assert_generated_batch_equal_expected()






from __future__ import annotations

def    lowercase_	(	_A    :					str					,      _A    :					list[str] | None = None					,      _A    :					dict[str, float] | None = None					,      _A    :					bool = False					,      ):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						cipher_alphabet or [chr(_A		) 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)
            lowerCamelCase__	:       Dict					=						{
                "a": 0.08_497,
                "b": 0.01_492,
                "c": 0.02_202,
                "d": 0.04_253,
                "e": 0.11_162,
                "f": 0.02_228,
                "g": 0.02_015,
                "h": 0.06_094,
                "i": 0.07_546,
                "j": 0.00_153,
                "k": 0.01_292,
                "l": 0.04_025,
                "m": 0.02_406,
                "n": 0.06_749,
                "o": 0.07_507,
                "p": 0.01_929,
                "q": 0.00_095,
                "r": 0.07_587,
                "s": 0.06_327,
                "t": 0.09_356,
                "u": 0.02_758,
                "v": 0.00_978,
                "w": 0.02_560,
                "x": 0.00_150,
                "y": 0.01_994,
                "z": 0.00_077,
            }
      else:
            # Custom frequencies dictionary
            lowerCamelCase__	:       Optional[int]					=						frequencies_dict

      if not case_sensitive:
            lowerCamelCase__	:       str					=						ciphertext.lower()

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

      # cycle through all of the shifts
      for shift in range(len(_A		)		):
            lowerCamelCase__	:       Optional[Any]					=						""

            # decrypt the message with the shift
            for letter in ciphertext:
                  try:
                        # Try to index the letter in the alphabet
                        lowerCamelCase__	:       Dict					=						(alphabet_letters.index(letter.lower()		) - shift) % len(
                            _A		)
                        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

            lowerCamelCase__	:       str					=						0.0

            # Loop through each letter in the decoded message with the shift
            for letter in decrypted_with_shift:
                  if case_sensitive:
                        lowerCamelCase__	:       List[str]					=						letter.lower()
                        if letter in frequencies:
                              # Get the amount of times the letter occurs in the message
                              lowerCamelCase__	:       List[str]					=						decrypted_with_shift.lower().count(_A		)

                              # Get the excepcted amount of times the letter should appear based
                              # on letter frequencies
                              lowerCamelCase__	:       List[Any]					=						frequencies[letter] * occurrences

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       str					=						((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
                              lowerCamelCase__	:       Any					=						decrypted_with_shift.count(_A		)

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

                              # Complete the chi squared statistic formula
                              lowerCamelCase__	:       int					=						((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
            lowerCamelCase__	:       Optional[int]					=						(
                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(_A    :					int		) -> tuple[float, str]:
            return chi_squared_statistic_values[key]

      lowerCamelCase__	:       int					=						min(
          _A					,      key=_A					,      )

      # Get all the data from the most likely cipher (key, decoded message)
      (
          (
          lowerCamelCase__
      )					,     (
          lowerCamelCase__
      )					,     
      )	:       int					=						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,
      )


