





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
 import os

 import jax.numpy as jnp
 from jax import jit

 from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
 from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

 _lowerCAmelCase				:   Any						       =						"0.12"  # assumed parallelism: 8


if is_torch_available():
 import torch

def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Optional[int]					,					_lowerCAmelCase			:			Any=None						)			->    Optional[int]:
   if rng is None:
      A_       :					List[Any]        =      random.Random()

   A_       :					Dict        =      1
   for dim in shape:
      total_dims *= dim

   A_       :					Union[str, Any]        =      []
   for _ in range(lowerCamelCase_						):
      values.append(rng.randint(0					,					vocab_size - 1						)						)

   A_       :					Any        =      np.array(lowerCamelCase_					,					dtype=jnp.intaa						).reshape(lowerCamelCase_						)

   return output

def   __snake_case    (       _lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			List[str]=None						)			->    str:
   A_       :					Dict        =      ids_tensor(lowerCamelCase_					,					vocab_size=2					,					rng=lowerCamelCase_						)
   # make sure that at least one token is attended to for each batch
   A_       :					str        =      1
   return attn_mask


@require_flax
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  None
     __UpperCamelCase            =  ()



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self.model_tester.prepare_config_and_inputs_for_common()

        # cut to half length & take max batch_size 3
        A_       :					List[str]        =      2
        A_       :					int        =      inputs['input_ids'].shape[-1] // 2
        A_       :					str        =      inputs['input_ids'][:max_batch_size, :sequence_length]

        A_       :					int        =      jnp.ones_like(snake_case	)
        A_       :					int        =      attention_mask[:max_batch_size, :sequence_length]

        # generate max 5 tokens
        A_       :					Union[str, Any]        =      input_ids.shape[-1] + 5
        if config.eos_token_id is not None and config.pad_token_id is None:
           # hack to allow generate for models such as GPT2 as is done in `generate()`
           A_       :					List[str]        =      config.eos_token_id
        return config, input_ids, attention_mask, max_length



     @is_pt_flax_cross_test
     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      self._get_input_ids_and_config()
        A_       :					List[str]        =      False
        A_       :					List[str]        =      max_length
        A_       :					Optional[Any]        =      0

        for model_class in self.all_generative_model_classes:
           A_       :					Optional[Any]        =      model_class(snake_case	)

           A_       :					List[str]        =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
           A_       :					Tuple        =      getattr(snake_case      ,   snake_case	)
           A_       :					Dict        =      pt_model_class(snake_case	).eval()
           A_       :					Tuple        =      load_flax_weights_in_pytorch_model(snake_case      ,   flax_model.params	)

           A_       :					Optional[Any]        =      flax_model.generate(snake_case	).sequences
           A_       :					Optional[Any]        =      pt_model.generate(torch.tensor(snake_case      ,   dtype=torch.long	)	)

           if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
              A_       :					Dict        =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

           self.assertListEqual(pt_generation_outputs.numpy().tolist()      ,   flax_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      self._get_input_ids_and_config()
        A_       :					Union[str, Any]        =      False
        A_       :					Tuple        =      max_length

        for model_class in self.all_generative_model_classes:
           A_       :					Union[str, Any]        =      model_class(snake_case	)

           A_       :					str        =      model.generate(snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					Tuple        =      jit(model.generate	)
           A_       :					Union[str, Any]        =      jit_generate(snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self._get_input_ids_and_config()
        A_       :					Any        =      True
        A_       :					Union[str, Any]        =      max_length

        for model_class in self.all_generative_model_classes:
           A_       :					Any        =      model_class(snake_case	)

           A_       :					int        =      model.generate(snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					str        =      jit(model.generate	)
           A_       :					str        =      jit_generate(snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        A_       :					int        =      self._get_input_ids_and_config()
        A_       :					List[str]        =      False
        A_       :					Optional[Any]        =      max_length
        A_       :					str        =      2

        for model_class in self.all_generative_model_classes:
           A_       :					List[str]        =      model_class(snake_case	)

           A_       :					Dict        =      model.generate(snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					Optional[Any]        =      jit(model.generate	)
           A_       :					List[str]        =      jit_generate(snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					List[str]        =      self._get_input_ids_and_config()
        A_       :					Dict        =      False
        A_       :					List[Any]        =      max_length
        A_       :					int        =      2
        A_       :					Any        =      2

        for model_class in self.all_generative_model_classes:
           A_       :					Union[str, Any]        =      model_class(snake_case	)

           A_       :					Dict        =      model.generate(snake_case	).sequences
           self.assertEqual(generation_outputs.shape[0]      ,   input_ids.shape[0] * config.num_return_sequences	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self._get_input_ids_and_config()
        A_       :					List[Any]        =      True
        A_       :					Optional[Any]        =      max_length
        A_       :					int        =      0.8
        A_       :					Optional[Any]        =      10
        A_       :					Dict        =      0.3
        A_       :					Union[str, Any]        =      1
        A_       :					Union[str, Any]        =      8
        A_       :					Optional[Any]        =      9

        for model_class in self.all_generative_model_classes:
           A_       :					Any        =      model_class(snake_case	)

           A_       :					str        =      model.generate(snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					List[str]        =      jit(model.generate	)
           A_       :					List[Any]        =      jit_generate(snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					List[str]        =      self._get_input_ids_and_config()
        A_       :					List[str]        =      max_length
        A_       :					List[Any]        =      1
        A_       :					Optional[Any]        =      8
        A_       :					List[Any]        =      9

        for model_class in self.all_generative_model_classes:
           A_       :					List[str]        =      model_class(snake_case	)

           A_       :					List[str]        =      model.generate(snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					Dict        =      jit(model.generate	)
           A_       :					List[Any]        =      jit_generate(snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      self._get_input_ids_and_config()
        A_       :					Union[str, Any]        =      max_length
        A_       :					Optional[int]        =      2
        A_       :					Dict        =      1
        A_       :					int        =      8
        A_       :					Optional[int]        =      9

        for model_class in self.all_generative_model_classes:
           A_       :					Any        =      model_class(snake_case	)

           A_       :					int        =      model.generate(snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					Any        =      jit(model.generate	)
           A_       :					str        =      jit_generate(snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Tuple        =      self._get_input_ids_and_config()

        # pad attention mask on the left
        A_       :					Any        =      attention_mask.at[(0, 0)].set(0	)

        A_       :					Union[str, Any]        =      False
        A_       :					Any        =      max_length

        for model_class in self.all_generative_model_classes:
           A_       :					List[str]        =      model_class(snake_case	)

           A_       :					Any        =      model.generate(snake_case      ,   attention_mask=snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					List[str]        =      jit(model.generate	)
           A_       :					List[str]        =      jit_generate(snake_case      ,   attention_mask=snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					Optional[int]        =      self._get_input_ids_and_config()

        # pad attention mask on the left
        A_       :					Optional[int]        =      attention_mask.at[(0, 0)].set(0	)

        A_       :					Tuple        =      True
        A_       :					int        =      max_length

        for model_class in self.all_generative_model_classes:
           A_       :					int        =      model_class(snake_case	)

           A_       :					Optional[int]        =      model.generate(snake_case      ,   attention_mask=snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					Optional[Any]        =      jit(model.generate	)
           A_       :					Any        =      jit_generate(snake_case      ,   attention_mask=snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      self._get_input_ids_and_config()

        # pad attention mask on the left
        A_       :					List[str]        =      attention_mask.at[(0, 0)].set(0	)

        A_       :					List[Any]        =      2
        A_       :					List[Any]        =      max_length

        for model_class in self.all_generative_model_classes:
           A_       :					Optional[Any]        =      model_class(snake_case	)

           A_       :					Optional[Any]        =      model.generate(snake_case      ,   attention_mask=snake_case	).sequences
           self.assertEqual(generation_outputs.shape[-1]      ,   snake_case	)

           A_       :					Tuple        =      jit(model.generate	)
           A_       :					int        =      jit_generate(snake_case      ,   attention_mask=snake_case	).sequences

           self.assertListEqual(generation_outputs.tolist()      ,   jit_generation_outputs.tolist()	)




@require_flax
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"	)
        A_       :					Optional[Any]        =      FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"	)

        A_       :					Optional[int]        =      'Hello world'
        A_       :					List[Any]        =      tokenizer(snake_case      ,   return_tensors="np"	).input_ids

        # typos are quickly detected (the correct argument is `do_sample`)
        with self.assertRaisesRegex(snake_case      ,   "do_samples"	):
           model.generate(snake_case      ,   do_samples=snake_case	)

        # arbitrary arguments that will not be used anywhere are also not accepted
        with self.assertRaisesRegex(snake_case      ,   "foo"	):
           A_       :					Dict        =      {'foo': 'bar'}
           model.generate(snake_case      ,   **snake_case	)




from __future__ import annotations

from typing import Any






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = 0)			->  None:




           """simple docstring"""

           _lowercase						,							_lowercase			:				str 						=						row, column
           _lowercase			:				Any 						=						[[default_value for c in range(lowerCamelCase)] for r in range(lowerCamelCase)]
       def __str__(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

           # Make string identifier
           _lowercase			:				str 						=						0
           for row_vector in self.array:
               for obj in row_vector:
                   _lowercase			:				Optional[int] 						=						max(lowerCamelCase, len(str(lowerCamelCase)))
           _lowercase			:				List[str] 						=						F'''%{max_element_length}s'''

           # Make string and return
           def single_line(lowerCamelCase) -> str:
               nonlocal string_format_identifier
               _lowercase			:				Union[str, Any] 						=						'['
               line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
               line += "]"
               return line

           s += "\n".join(single_line(lowerCamelCase) for row_vector in self.array)
           return s
       def __repr__(   self)			->  str:




           """simple docstring"""

           return str(self)
       def      UpperCamelCase	(   self, lowerCamelCase)			->  bool:




           """simple docstring"""

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




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           return self.array[loc[0]][loc[1]]
       def __setitem__(   self, lowerCamelCase, lowerCamelCase)			->  None:




           """simple docstring"""

           assert self.validate_indicies(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						value
       def __add__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

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

           # Add
           _lowercase			:				Any 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				int 						=						self[r, c] + another[r, c]
           return result
       def __neg__(   self)			->  Matrix:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						Matrix(self.row, self.column)
           for r in range(self.row):
               for c in range(self.column):
                   _lowercase			:				List[str] 						=						-self[r, c]
           return result
       def __sub__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           return self + (-another)
       def __mul__(   self, lowerCamelCase)			->  Matrix:




           """simple docstring"""

           if isinstance(lowerCamelCase, (int, float)):  # Scalar multiplication
               _lowercase			:				Dict 						=						Matrix(self.row, self.column)
               for r in range(self.row):
                   for c in range(self.column):
                       _lowercase			:				Union[str, Any] 						=						self[r, c] * another
               return result
           elif isinstance(lowerCamelCase, lowerCamelCase):  # Matrix multiplication
               assert self.column == another.row
               _lowercase			:				str 						=						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:
               _lowercase			:				Tuple 						=						F'''Unsupported type given for another ({type(lowerCamelCase)})'''
               raise TypeError(lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Matrix:




           """simple docstring"""

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




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           assert isinstance(lowerCamelCase, lowerCamelCase) and isinstance(lowerCamelCase, lowerCamelCase)
           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
           _lowercase			:				Dict 						=						v.transpose()
           _lowercase			:				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 							UpperCamelCase_( )       ->		None:
     # a^(-1)
     _lowercase			:				Optional[int] 						=						Matrix(3				,					3				,					0					)
     for i in range(3					):
         _lowercase			:				int 						=						1
     print(F'''a^(-1) is {ainv}'''					)
     # u, v
     _lowercase			:				Dict 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						1, 2, -3
     _lowercase			:				List[Any] 						=						Matrix(3				,					1				,					0					)
     _lowercase						,							_lowercase						,							_lowercase			:				int 						=						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(lowerCamelCase_				,					lowerCamelCase_					)}'''					)



 def 							UpperCamelCase_( )       ->		None:
     import doctest

     doctest.testmod()

 testa()


