





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        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      MBartConfig
 lowercase_		=      {}
 lowercase_		=      "gelu"

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



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

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

   lowerCamelCase__:				Tuple        =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__:				str        =prepare_mbart_inputs_dict(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_)
   return config, inputs_dict







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Any)	->Optional[Any]:



   '''simple docstring'''



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

   lowerCamelCase__:				Any        =input_ids[:1, :]
   lowerCamelCase__:				List[Any]        =inputs_dict["attention_mask"][:1, :]
   lowerCamelCase__:				str        =inputs_dict["head_mask"]
   lowerCamelCase__:				Union[str, Any]        =1

   # first forward pass
   lowerCamelCase__:				Optional[Any]        =model(UpperCAmelCase_	,			attention_mask=UpperCAmelCase_	,			head_mask=UpperCAmelCase_	,			use_cache=UpperCAmelCase_)

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





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a=None     ,   __a=None     ,   __a=None     ,   __a=None     ,   __a=None     ,   )					->		Optional[int]:







  """simple docstring"""





  if attention_mask is None:
    lowerCamelCase__:				Tuple        =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__:				Any        =tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
  if decoder_head_mask is None:
    lowerCamelCase__:				List[str]        =tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
  if cross_attn_head_mask is None:
    lowerCamelCase__:				Optional[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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 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 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       int)	->Optional[Any]:



   '''simple docstring'''



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

   return False

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =TFMBartModelTester(self)
   lowerCamelCase__:				int        =ConfigTester(self	,			config_class=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->int:



   '''simple docstring'''



   self.config_tester.run_common_tests()







 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =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        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''
 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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Optional[int]:



   '''simple docstring'''



   return AutoTokenizer.from_pretrained(self.model_name)

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
   return model

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			**UpperCAmelCase_    :       Dict)	->List[Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			**UpperCAmelCase_    :       List[str])	->Optional[int]:



   '''simple docstring'''



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







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Any:



   '''simple docstring'''



   self._assert_generated_batch_equal_expected()








from __future__ import annotations

from typing import Any



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       float = 0)	->None:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Any        =row, column
   lowerCamelCase__:				List[str]        =[[default_value for c in range(UpperCAmelCase_)] for r in range(UpperCAmelCase_)]

 def __str__(self    :       Tuple)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

   # Make string identifier
   lowerCamelCase__:				List[str]        =0
   for row_vector in self.array:
     for obj in row_vector:
       lowerCamelCase__:				int        =max(UpperCAmelCase_	,			len(str(UpperCAmelCase_)))
   lowerCamelCase__:				Any        =F"""%{max_element_length}s"""

   # Make string and return
   def single_line(UpperCAmelCase_    :       list[float]) -> str:
     nonlocal string_format_identifier
     lowerCamelCase__:				Tuple        ="["
     line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
     line += "]"
     return line

   s += "\n".join(single_line(UpperCAmelCase_) for row_vector in self.array)
   return s

 def __repr__(self    :       Optional[int])	->str:



   '''simple docstring'''



   return str(self)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       tuple[int, int])	->bool:



   '''simple docstring'''



   if not (isinstance(UpperCAmelCase_	,			(list, tuple)) and len(UpperCAmelCase_) == 2):
     return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
     return False
   else:
     return True

 def __getitem__(self    :       int	,			UpperCAmelCase_    :       tuple[int, int])	->Any:



   '''simple docstring'''



   assert self.validate_indicies(UpperCAmelCase_)
   return self.array[loc[0]][loc[1]]

 def __setitem__(self    :       Optional[Any]	,			UpperCAmelCase_    :       tuple[int, int]	,			UpperCAmelCase_    :       float)	->None:



   '''simple docstring'''



   assert self.validate_indicies(UpperCAmelCase_)
   lowerCamelCase__:				str        =value

 def __add__(self    :       Dict	,			UpperCAmelCase_    :       Matrix)	->Matrix:



   '''simple docstring'''



   assert isinstance(UpperCAmelCase_	,			UpperCAmelCase_)
   assert self.row == another.row and self.column == another.column

   # Add
   lowerCamelCase__:				Dict        =Matrix(self.row	,			self.column)
   for r in range(self.row):
     for c in range(self.column):
       lowerCamelCase__:				List[str]        =self[r, c] + another[r, c]
   return result

 def __neg__(self    :       str)	->Matrix:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =Matrix(self.row	,			self.column)
   for r in range(self.row):
     for c in range(self.column):
       lowerCamelCase__:				Union[str, Any]        =-self[r, c]
   return result

 def __sub__(self    :       str	,			UpperCAmelCase_    :       Matrix)	->Matrix:



   '''simple docstring'''



   return self + (-another)

 def __mul__(self    :       List[str]	,			UpperCAmelCase_    :       int | float | Matrix)	->Matrix:



   '''simple docstring'''



   if isinstance(UpperCAmelCase_	,			(int, float)):  # Scalar multiplication
     lowerCamelCase__:				List[Any]        =Matrix(self.row	,			self.column)
     for r in range(self.row):
       for c in range(self.column):
         lowerCamelCase__:				Union[str, Any]        =self[r, c] * another
     return result
   elif isinstance(UpperCAmelCase_	,			UpperCAmelCase_):  # Matrix multiplication
     assert self.column == another.row
     lowerCamelCase__:				Dict        =Matrix(self.row	,			another.column)
     for r in range(self.row):
       for c in range(another.column):
         for i in range(self.column):
           result[r, c] += self[r, i] * another[i, c]
     return result
   else:
     lowerCamelCase__:				int        =F"""Unsupported type given for another ({type(UpperCAmelCase_)})"""
     raise TypeError(UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Matrix:



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =Matrix(self.column	,			self.row)
   for r in range(self.row):
     for c in range(self.column):
       lowerCamelCase__:				Optional[int]        =self[r, c]
   return result







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Matrix	,			UpperCAmelCase_    :       Matrix)	->Any:



   '''simple docstring'''



   assert isinstance(UpperCAmelCase_	,			UpperCAmelCase_) and isinstance(UpperCAmelCase_	,			UpperCAmelCase_)
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   lowerCamelCase__:				Tuple        =v.transpose()
   lowerCamelCase__:				Optional[Any]        =(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
     return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





     def 					lowerCAmelCase_			(	)					->		None:







       """simple docstring"""





       lowerCamelCase__:				List[str]        =Matrix(3     ,   3     ,   0		)
       for i in range(3		):
         lowerCamelCase__:				Union[str, Any]        =1
       print(F"""a^(-1) is {ainv}"""		)
       # u, v
       lowerCamelCase__:				Optional[int]        =Matrix(3     ,   1     ,   0		)
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				int        =1, 2, -3
       lowerCamelCase__:				Optional[Any]        =Matrix(3     ,   1     ,   0		)
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Any        =4, -2, 5
       print(F"""u is {u}"""		)
       print(F"""v is {v}"""		)
       print(F"""uv^T is {u * v.transpose()}"""		)
       # Sherman Morrison
       print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(__a     ,   __a		)}"""		)





     def 					lowerCAmelCase_			(	)					->		None:







       """simple docstring"""





       import doctest

       doctest.testmod()






     testa()

