'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.roberta.modeling_flax_roberta import (
       FlaxRobertaForCausalLM,
       FlaxRobertaForMaskedLM,
       FlaxRobertaForMultipleChoice,
       FlaxRobertaForQuestionAnswering,
       FlaxRobertaForSequenceClassification,
       FlaxRobertaForTokenClassification,
       FlaxRobertaModel,
   )






class 		__A       (unittest.TestCase   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_=13				,     UpperCamelCase_=7				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=True				,     UpperCamelCase_=99				,     UpperCamelCase_=32				,     UpperCamelCase_=5				,     UpperCamelCase_=4				,     UpperCamelCase_=37				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=16				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=4				,     ):
              __UpperCAmelCase  :				List[Any]       				=		parent
              __UpperCAmelCase  :				Any       				=		batch_size
              __UpperCAmelCase  :				Optional[int]       				=		seq_length
              __UpperCAmelCase  :				Optional[Any]       				=		is_training
              __UpperCAmelCase  :				Any       				=		use_attention_mask
              __UpperCAmelCase  :				List[str]       				=		use_token_type_ids
              __UpperCAmelCase  :				Optional[Any]       				=		use_labels
              __UpperCAmelCase  :				List[Any]       				=		vocab_size
              __UpperCAmelCase  :				List[Any]       				=		hidden_size
              __UpperCAmelCase  :				Tuple       				=		num_hidden_layers
              __UpperCAmelCase  :				Optional[int]       				=		num_attention_heads
              __UpperCAmelCase  :				Union[str, Any]       				=		intermediate_size
              __UpperCAmelCase  :				Tuple       				=		hidden_act
              __UpperCAmelCase  :				Optional[Any]       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Dict       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				List[Any]       				=		max_position_embeddings
              __UpperCAmelCase  :				Union[str, Any]       				=		type_vocab_size
              __UpperCAmelCase  :				int       				=		type_sequence_label_size
              __UpperCAmelCase  :				Dict       				=		initializer_range
              __UpperCAmelCase  :				Dict       				=		num_choices






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size      )

              __UpperCAmelCase  :				int       				=		None
              if self.use_attention_mask:
                     __UpperCAmelCase  :				Optional[int]       				=		random_attention_mask([self.batch_size, self.seq_length]      )

              __UpperCAmelCase  :				str       				=		None
              if self.use_token_type_ids:
                     __UpperCAmelCase  :				Tuple       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size      )

              __UpperCAmelCase  :				int       				=		RobertaConfig(
                  vocab_size=self.vocab_size				,     hidden_size=self.hidden_size				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     is_decoder=UpperCamelCase_				,     initializer_range=self.initializer_range				,     )

              return config, input_ids, token_type_ids, attention_mask






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		self.prepare_config_and_inputs()
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		config_and_inputs
              __UpperCAmelCase  :				Dict       				=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
              return config, inputs_dict







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.prepare_config_and_inputs()
              __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		config_and_inputs

              __UpperCAmelCase  :				Union[str, Any]       				=		True
              __UpperCAmelCase  :				Optional[int]       				=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
              __UpperCAmelCase  :				Optional[int]       				=		ids_tensor([self.batch_size, self.seq_length]				,     vocab_size=2      )

              return (
                  config,
                  input_ids,
                  token_type_ids,
                  encoder_hidden_states,
                  encoder_attention_mask,
              )






@require_flax
class 		__A       (__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					True

       snake_case       :List[str]				    =					(
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		FlaxRobertaModelTester(self      )







       @slow
       def 			_snake_case						(      self      ):
              for model_class_name in self.all_model_classes:
                     __UpperCAmelCase  :				str       				=		model_class_name.from_pretrained("roberta-base"				,     from_pt=UpperCamelCase_      )
                     __UpperCAmelCase  :				Optional[Any]       				=		model(np.ones((1, 1)      )      )
                     self.assertIsNotNone(UpperCamelCase_      )







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






