

'''simple docstring'''

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_					:






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    MarianConfig
     UpperCAmelCase_		:      Optional[Any]    						=    {}
     UpperCAmelCase_		:      Optional[int]    						=    """gelu"""


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

      UpperCAmelCase			:      Tuple		   =					hidden_dropout_prob
      UpperCAmelCase			:      Union[str, Any]		   =					attention_probs_dropout_prob
      UpperCAmelCase			:      int		   =					max_position_embeddings
      UpperCAmelCase			:      Tuple		   =					eos_token_id
      UpperCAmelCase			:      Union[str, Any]		   =					pad_token_id
      UpperCAmelCase			:      List[Any]		   =					bos_token_id


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

      UpperCAmelCase			:      Tuple		   =					ids_tensor([self.batch_size, self.seq_length]	,     self.vocab_size			)

      UpperCAmelCase			:      int		   =					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			:      List[str]		   =					prepare_marian_inputs_dict(lowercase_	,     lowercase_	,     lowercase_			)
      return config, inputs_dict




     def   UpperCAmelCase_			(						self					:	int	,     lowercase_					:	Optional[Any]	,     lowercase_					:	Tuple			)		->      Dict:
      UpperCAmelCase			:      Tuple		   =					TFMarianModel(config=lowercase_			).get_decoder()
      UpperCAmelCase			:      int		   =					inputs_dict['input_ids']

      UpperCAmelCase			:      Tuple		   =					input_ids[:1, :]
      UpperCAmelCase			:      Union[str, Any]		   =					inputs_dict['attention_mask'][:1, :]
      UpperCAmelCase			:      Optional[int]		   =					inputs_dict['head_mask']
      UpperCAmelCase			:      Optional[Any]		   =					1

      # first forward pass
      UpperCAmelCase			:      Dict		   =					model(lowercase_	,     attention_mask=lowercase_	,     head_mask=lowercase_	,     use_cache=lowercase_			)

      UpperCAmelCase   ,					UpperCAmelCase			:      List[str]		   =					outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      UpperCAmelCase			:      str		   =					ids_tensor((self.batch_size, 3)	,     config.vocab_size			)
      UpperCAmelCase			:      Optional[int]		   =					tf.cast(ids_tensor((self.batch_size, 3)	,     2			)	,     tf.inta			)

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

      UpperCAmelCase			:      int		   =					model(lowercase_	,     attention_mask=lowercase_			)[0]
      UpperCAmelCase			:      Any		   =					model(lowercase_	,     attention_mask=lowercase_	,     past_key_values=lowercase_			)[0]

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

      # select random slice
      UpperCAmelCase			:      List[Any]		   =					int(ids_tensor((1,)	,     output_from_past.shape[-1]			)			)
      UpperCAmelCase			:      Union[str, Any]		   =					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(lowercase_	,     lowercase_	,     rtol=1E-3			)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_=None    ,			UpperCAmelCase_=None    ,			UpperCAmelCase_=None    ,			UpperCAmelCase_=None    ,			UpperCAmelCase_=None    ,			):
 if attention_mask is None:
  UpperCAmelCase			:      Optional[int]		   =					tf.cast(tf.math.not_equal(UpperCAmelCase_    ,			config.pad_token_id      )    ,			tf.inta      )
 if decoder_attention_mask is None:
  UpperCAmelCase			:      List[Any]		   =					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			:      Optional[Any]		   =					tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
 if cross_attn_head_mask is None:
  UpperCAmelCase			:      List[str]		   =					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_					(							_snake_case			,  _snake_case			,  unittest.TestCase				):






     '''simple docstring'''



     UpperCAmelCase_		:      int    						=    (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     UpperCAmelCase_		:      Optional[Any]    						=    (TFMarianMTModel,) if is_tf_available() else ()
     UpperCAmelCase_		:      List[str]    						=    (
         {
             """conversational""": TFMarianMTModel,
             """feature-extraction""": TFMarianModel,
             """summarization""": TFMarianMTModel,
             """text2text-generation""": TFMarianMTModel,
             """translation""": TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     UpperCAmelCase_		:      str    						=    True
     UpperCAmelCase_		:      Optional[int]    						=    False
     UpperCAmelCase_		:      str    						=    False


     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      UpperCAmelCase			:      Any		   =					TFMarianModelTester(self			)
      UpperCAmelCase			:      List[str]		   =					ConfigTester(self	,     config_class=lowercase_			)


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




     def   UpperCAmelCase_			(						self					:	str			)		->      Optional[Any]:
      UpperCAmelCase			:      Optional[Any]		   =					self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*lowercase_			)




@require_tf
class 			A_					(							unittest.TestCase				):






     '''simple docstring'''



     UpperCAmelCase_		:      int    						=    1_000  # show more chars for failing integration tests


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


     @cached_property
     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      MarianTokenizer:
      return AutoTokenizer.from_pretrained(self.model_name			)


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


     @cached_property
     def   UpperCAmelCase_			(						self					:	Tuple			)		->      Tuple:
      warnings.simplefilter('error'			)
      UpperCAmelCase			:      TFMarianMTModel		   =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
      assert isinstance(lowercase_	,     lowercase_			)
      UpperCAmelCase			:      Any		   =					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   UpperCAmelCase_			(						self					:	Any	,     **lowercase_					:	Optional[int]			)		->      List[str]:
      UpperCAmelCase			:      Optional[Any]		   =					self.translate_src_text(**lowercase_			)
      self.assertListEqual(self.expected_text	,     lowercase_			)




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




@require_sentencepiece
@require_tokenizers
@require_tf
class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    """mt"""
     UpperCAmelCase_		:      Optional[Any]    						=    """en"""
     UpperCAmelCase_		:      str    						=    ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
     UpperCAmelCase_		:      Optional[Any]    						=    ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]


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




@require_sentencepiece
@require_tokenizers
@require_tf
class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Union[str, Any]    						=    """en"""
     UpperCAmelCase_		:      List[Any]    						=    """zh"""
     UpperCAmelCase_		:      int    						=    ["""My name is Wolfgang and I live in Berlin"""]
     UpperCAmelCase_		:      List[str]    						=    ["""我叫沃尔夫冈 我住在柏林"""]


     @unittest.skip('Skipping until #12647 is resolved.'			)
     @slow
     def   UpperCAmelCase_			(						self					:	List[Any]			)		->      List[Any]:
      self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Optional[int]    						=    """en"""
     UpperCAmelCase_		:      List[str]    						=    """ROMANCE"""
     UpperCAmelCase_		:      Optional[Any]    						=    [
         """>>fr<< Don't spend so much time watching TV.""",
         """>>pt<< Your message has been sent.""",
         """>>es<< He's two years older than me.""",
     ]
     UpperCAmelCase_		:      int    						=    [
         """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   UpperCAmelCase_			(						self					:	int			)		->      Optional[int]:
      self._assert_generated_batch_equal_expected()




     @unittest.skip('Skipping until #12647 is resolved.'			)
     @slow
     def   UpperCAmelCase_			(						self					:	int			)		->      Optional[int]:
      UpperCAmelCase			:      int		   =					TranslationPipeline(self.model	,     self.tokenizer	,     framework='tf'			)
      UpperCAmelCase			:      Optional[int]		   =					pipeline(self.src_text			)
      self.assertEqual(self.expected_text	,     [x['translation_text'] for x in output]			)







'''simple docstring'''

from __future__ import annotations

from collections.abc import Callable




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_ = 1_00    ,			):
 UpperCAmelCase			:      Any		   =					x_start
 UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
 UpperCAmelCase			:      str		   =					0.0

 for _ in range(UpperCAmelCase_      ):
  # Approximates small segments of curve as linear and solve
  # for trapezoidal area
  UpperCAmelCase			:      List[Any]		   =					(x_end - x_start) / steps + xa
  UpperCAmelCase			:      int		   =					fnc(UpperCAmelCase_      )
  area += abs(fxa + fxa      ) * (xa - xa) / 2

  # Increment step
  UpperCAmelCase			:      List[str]		   =					xa
  UpperCAmelCase			:      List[Any]		   =					fxa
 return area


if __name__ == "__main__":




 def  UpperCamelCase(	UpperCAmelCase_      ):
  return x**3

 print("f(x) = x^3")
 print("The area between the curve, x = -10, x = 10 and the x axis is:")
 lowercase__            =     10
 while i <= 100000:
  lowercase__            =     trapezoidal_area(f, -5, 5, i)
  print(f'''with {i} steps: {area}''')
  i *= 10




