

from typing import Any

import numpy as np






class 							A_				:


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	np.ndarray    ,						UpperCAmelCase    :	int      )							->			None:
      # Check if RHS is negative
      if np.any(tableau[:, -1]    ,						where=tableau[:, -1] < 0      ):
       raise ValueError('RHS must be > 0'      )

      __lowerCAmelCase:     List[str]     			=   tableau
      __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   tableau.shape

      # Number of decision variables x1, x2, x3...
      __lowerCAmelCase:     Tuple     			=   n_vars

      # Number of artificial variables to be minimised
      __lowerCAmelCase:     Union[str, Any]     			=   len(np.where(tableau[self.n_vars : -1] == -1      )[0]      )

      # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
      __lowerCAmelCase:     List[str]     			=   (self.n_art_vars > 0) + 1

      # Number of slack variables added to make inequalities into equalities
      __lowerCAmelCase:     Union[str, Any]     			=   self.n_rows - self.n_stages

      # Objectives for each stage
      __lowerCAmelCase:     Optional[Any]     			=   ['max']

      # In two stage simplex, first minimise then maximise
      if self.n_art_vars:
       self.objectives.append('min'      )

      __lowerCAmelCase:     Any     			=   ['']

      # Index of current pivot row and column
      __lowerCAmelCase:     List[Any]     			=   None
      __lowerCAmelCase:     int     			=   None

      # Does objective row only contain (non)-negative values?
      __lowerCAmelCase:     List[str]     			=   False


     @staticmethod
     def 						UpperCAmelCase						(							*UpperCAmelCase    :	int      )							->			list[str]:
      if len(UpperCAmelCase      ) != 3:
       raise ValueError('Must provide n_vars, n_slack, and n_art_vars'      )

      if not all(x >= 0 and isinstance(UpperCAmelCase    ,						UpperCAmelCase      ) for x in args      ):
       raise ValueError('All arguments must be non-negative integers'      )

      # decision | slack | artificial
      __lowerCAmelCase:     Tuple     			=   ['x', 's', 'a']
      __lowerCAmelCase:     Union[str, Any]     			=   []
      for i in range(3      ):
       for j in range(args[i]      ):
        titles.append(string_starts[i] + str(j + 1      )      )
      titles.append('RHS'      )
      return titles


     def 						UpperCAmelCase						(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	np.ndarray      )							->			tuple[Any, Any]:
      __lowerCAmelCase:     int     			=   self.objectives[-1]

      # Find entries of highest magnitude in objective rows
      __lowerCAmelCase:     Tuple     			=   (objective == 'min') - (objective == 'max')
      __lowerCAmelCase:     Dict     			=   np.argmax(sign * tableau[0, : self.n_vars]      )

      # Choice is only valid if below 0 for maximise, and above for minimise
      if sign * self.tableau[0, col_idx] <= 0:
       __lowerCAmelCase:     int     			=   True
       return 0, 0

      # Pivot row is chosen as having the lowest quotient when elements of
      # the pivot column divide the right-hand side

      # Slice excluding the objective rows
      __lowerCAmelCase:     List[Any]     			=   slice(self.n_stages    ,						self.n_rows      )

      # RHS
      __lowerCAmelCase:     List[str]     			=   tableau[s, -1]

      # Elements of pivot column within slice
      __lowerCAmelCase:     int     			=   tableau[s, col_idx]

      # Array filled with nans
      __lowerCAmelCase:     Optional[Any]     			=   np.full(self.n_rows - self.n_stages    ,						np.nan      )

      # If element in pivot column is greater than zeron_stages, return
      # quotient or nan otherwise
      __lowerCAmelCase:     List[str]     			=   np.divide(UpperCAmelCase    ,						UpperCAmelCase    ,						out=UpperCAmelCase    ,						where=divisor > 0      )

      # Arg of minimum quotient excluding the nan values. n_stages is added
      # to compensate for earlier exclusion of objective columns
      __lowerCAmelCase:     List[Any]     			=   np.nanargmin(UpperCAmelCase      ) + self.n_stages
      return row_idx, col_idx


     def 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	np.ndarray    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int      )							->			np.ndarray:
      __lowerCAmelCase:     Union[str, Any]     			=   tableau[row_idx].copy()

      __lowerCAmelCase:     Tuple     			=   piv_row[col_idx]

      # Entry becomes 1
      piv_row *= 1 / piv_val

      # Variable in pivot column becomes basic, ie the only non-zero entry
      for idx, coeff in enumerate(tableau[:, col_idx]      ):
       tableau[idx] += -coeff * piv_row
      __lowerCAmelCase:     Optional[int]     			=   piv_row
      return tableau


     def 						UpperCAmelCase						(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	np.ndarray      )							->			np.ndarray:
      self.objectives.pop()

      if not self.objectives:
       return tableau

      # Slice containing ids for artificial columns
      __lowerCAmelCase:     Optional[int]     			=   slice(-self.n_art_vars - 1    ,						-1      )

      # Delete the artificial variable columns
      __lowerCAmelCase:     List[Any]     			=   np.delete(UpperCAmelCase    ,						UpperCAmelCase    ,						axis=1      )

      # Delete the objective row of the first stage
      __lowerCAmelCase:     int     			=   np.delete(UpperCAmelCase    ,						0    ,						axis=0      )

      __lowerCAmelCase:     Optional[Any]     			=   1
      self.n_rows -= 1
      __lowerCAmelCase:     Union[str, Any]     			=   0
      __lowerCAmelCase:     Optional[int]     			=   False
      return tableau


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			dict[Any, Any]:
      # Stop simplex algorithm from cycling.
      for _ in range(1_0_0      ):
       # Completion of each stage removes an objective. If both stages
       # are complete, then no objectives are left
       if not self.objectives:
        __lowerCAmelCase:     Any     			=   self.generate_col_titles(
            self.n_vars    ,						self.n_slack    ,						self.n_art_vars      )

        # Find the values of each variable at optimal solution
        return self.interpret_tableau(self.tableau    ,						self.col_titles      )

       __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   self.find_pivot(self.tableau      )

       # If there are no more negative values in objective row
       if self.stop_iter:
        # Delete artificial variable columns and rows. Update attributes
        __lowerCAmelCase:     Optional[int]     			=   self.change_stage(self.tableau      )
       else:
        __lowerCAmelCase:     List[str]     			=   self.pivot(self.tableau    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return {}


     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	np.ndarray    ,						UpperCAmelCase    :	list[str]      )							->			dict[str, float]:
      __lowerCAmelCase:     Optional[int]     			=   {'P': abs(tableau[0, -1]      )}

      for i in range(self.n_vars      ):
       # Gives ids of nonzero entries in the ith column
       __lowerCAmelCase:     Optional[Any]     			=   np.nonzero(tableau[:, i]      )
       __lowerCAmelCase:     Optional[int]     			=   len(nonzero[0]      )

       # First entry in the nonzero ids
       __lowerCAmelCase:     Union[str, Any]     			=   nonzero[0][0]
       __lowerCAmelCase:     int     			=   tableau[nonzero_rowidx, i]

       # If there is only one nonzero value in column, which is one
       if n_nonzero == nonzero_val == 1:
        __lowerCAmelCase:     Optional[int]     			=   tableau[nonzero_rowidx, -1]
        __lowerCAmelCase:     Tuple     			=   rhs_val

        # Check for basic variables
      for title in col_titles:
       # Don't add RHS or slack variables to output dict
       if title[0] not in "R-s-a":
        output_dict.setdefault(UpperCAmelCase    ,						0      )
      return output_dict


if __name__ == "__main__":
  import doctest

  doctest.testmod()








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_				:
     _lowercase    :  str          =			MarianConfig
     _lowercase    :  Optional[int]          =			{}
     _lowercase    :  Optional[Any]          =			'gelu'


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]=1_3    ,						UpperCAmelCase    :	Optional[int]=7    ,						UpperCAmelCase    :	List[str]=True    ,						UpperCAmelCase    :	Dict=False    ,						UpperCAmelCase    :	Union[str, Any]=9_9    ,						UpperCAmelCase    :	Tuple=3_2    ,						UpperCAmelCase    :	List[str]=2    ,						UpperCAmelCase    :	Dict=4    ,						UpperCAmelCase    :	Tuple=3_7    ,						UpperCAmelCase    :	Union[str, Any]=0.1    ,						UpperCAmelCase    :	Dict=0.1    ,						UpperCAmelCase    :	Any=2_0    ,						UpperCAmelCase    :	Union[str, Any]=2    ,						UpperCAmelCase    :	Any=1    ,						UpperCAmelCase    :	str=0    ,						)							->			str:
      __lowerCAmelCase:     Tuple     			=   parent
      __lowerCAmelCase:     Any     			=   batch_size
      __lowerCAmelCase:     List[Any]     			=   seq_length
      __lowerCAmelCase:     Dict     			=   is_training
      __lowerCAmelCase:     List[Any]     			=   use_labels
      __lowerCAmelCase:     List[str]     			=   vocab_size
      __lowerCAmelCase:     Optional[Any]     			=   hidden_size
      __lowerCAmelCase:     Any     			=   num_hidden_layers
      __lowerCAmelCase:     Optional[Any]     			=   num_attention_heads
      __lowerCAmelCase:     int     			=   intermediate_size

      __lowerCAmelCase:     List[Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     List[str]     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Tuple     			=   max_position_embeddings
      __lowerCAmelCase:     Tuple     			=   eos_token_id
      __lowerCAmelCase:     List[Any]     			=   pad_token_id
      __lowerCAmelCase:     str     			=   bos_token_id


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

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

      __lowerCAmelCase:     Optional[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    ,						)
      __lowerCAmelCase:     Any     			=   prepare_marian_inputs_dict(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return config, inputs_dict


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	int      )							->			Union[str, Any]:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModel(config=UpperCAmelCase      ).get_decoder()
      __lowerCAmelCase:     str     			=   inputs_dict['input_ids']

      __lowerCAmelCase:     Tuple     			=   input_ids[:1, :]
      __lowerCAmelCase:     Optional[Any]     			=   inputs_dict['attention_mask'][:1, :]
      __lowerCAmelCase:     Dict     			=   inputs_dict['head_mask']
      __lowerCAmelCase:     List[Any]     			=   1

      # first forward pass
      __lowerCAmelCase:     Union[str, Any]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						head_mask=UpperCAmelCase    ,						use_cache=UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      __lowerCAmelCase:     int     			=   ids_tensor((self.batch_size, 3)    ,						config.vocab_size      )
      __lowerCAmelCase:     Tuple     			=   tf.cast(ids_tensor((self.batch_size, 3)    ,						2      )    ,						tf.inta      )

      # append to next input_ids and
      __lowerCAmelCase:     Any     			=   tf.concat([input_ids, next_tokens]    ,						axis=-1      )
      __lowerCAmelCase:     Optional[int]     			=   tf.concat([attention_mask, next_attn_mask]    ,						axis=-1      )

      __lowerCAmelCase:     Optional[int]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase      )[0]
      __lowerCAmelCase:     List[str]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						past_key_values=UpperCAmelCase      )[0]

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

      # select random slice
      __lowerCAmelCase:     Optional[int]     			=   int(ids_tensor((1,)    ,						output_from_past.shape[-1]      )      )
      __lowerCAmelCase:     Tuple     			=   output_from_no_past[:, -3:, random_slice_idx]
      __lowerCAmelCase:     Dict     			=   output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(UpperCAmelCase    ,						UpperCAmelCase    ,						rtol=1E-3      )







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[int]=None					,      SCREAMING_SNAKE_CASE	:    Dict=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      SCREAMING_SNAKE_CASE	:    List[str]=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      )					->					Dict:

 """simple docstring"""






 if attention_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE					,      config.pad_token_id	)					,      tf.inta	)
 if decoder_attention_mask is None:
  __lowerCAmelCase:     Tuple     			=   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:     List[str]     			=   tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  __lowerCAmelCase:     List[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 							A_				(	snake_case__							,			snake_case__							,			unittest.TestCase      ):
     _lowercase    :  Optional[int]          =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _lowercase    :  int          =			(TFMarianMTModel,) if is_tf_available() else ()
     _lowercase    :  Optional[int]          =			(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _lowercase    :  Dict          =			True
     _lowercase    :  List[Any]          =			False
     _lowercase    :  Optional[int]          =			False


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModelTester(self      )
      __lowerCAmelCase:     Any     			=   ConfigTester(self    ,						config_class=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Any:
      self.config_tester.run_common_tests()


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     Tuple     			=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase      )







@require_tf
class 							A_				(	unittest.TestCase      ):
     _lowercase    :  int          =			1_0_0_0  # show more chars for failing integration tests


     @classmethod
     def 						UpperCAmelCase						(							cls    :	Dict      )							->			None:
      __lowerCAmelCase:     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    :	Any      )							->			int:
      return self.tokenizer.eos_token_id


     @cached_property
     def 						UpperCAmelCase						(							self    :	int      )							->			Optional[int]:
      warnings.simplefilter('error'      )
      __lowerCAmelCase:     TFMarianMTModel     			=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
      assert isinstance(UpperCAmelCase    ,						UpperCAmelCase      )
      __lowerCAmelCase:     List[str]     			=   model.config
      self.assertListEqual(c.bad_words_ids    ,						[[c.pad_token_id]]      )
      self.assertEqual(c.max_length    ,						5_1_2      )
      self.assertEqual(c.decoder_start_token_id    ,						c.pad_token_id      )
      return model


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Dict      )							->			int:
      __lowerCAmelCase:     Optional[int]     			=   self.translate_src_text(**UpperCAmelCase      )
      self.assertListEqual(self.expected_text    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Optional[int]      )							->			Optional[int]:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(self.src_text    ,						**UpperCAmelCase    ,						padding=UpperCAmelCase    ,						return_tensors='tf'      )
      __lowerCAmelCase:     Tuple     			=   self.model.generate(
          model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=1_2_8      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=UpperCAmelCase      )
      return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  int          =			'mt'
     _lowercase    :  Union[str, Any]          =			'en'
     _lowercase    :  str          =			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     _lowercase    :  str          =			['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    :	Any      )							->			List[str]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Optional[int]          =			'en'
     _lowercase    :  Optional[Any]          =			'zh'
     _lowercase    :  Tuple          =			['My name is Wolfgang and I live in Berlin']
     _lowercase    :  List[Any]          =			['我叫沃尔夫冈 我住在柏林']


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







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Tuple          =			'en'
     _lowercase    :  Optional[int]          =			'ROMANCE'
     _lowercase    :  Any          =			[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     _lowercase    :  Tuple          =			[
         '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    :	Tuple      )							->			Dict:
      self._assert_generated_batch_equal_expected()


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





