


from __future__ import annotations

from typing import Any
def        UpperCAmelCase_      (							*__snake_case		)  ->    list[list[int]]:







 """simple docstring"""


 if all(_check_not_integer(__snake_case		) for m in matrix_s		):
  for i in matrix_s[1:]:
   _verify_matrix_sizes(matrix_s[0]  ,		__snake_case		)
  return [[sum(__snake_case		) for t in zip(*__snake_case		)] for m in zip(*__snake_case		)]
 raise TypeError('''Expected a matrix, got int/list instead'''		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    list[list[int]]:







 """simple docstring"""


 if (
     _check_not_integer(__snake_case		)
     and _check_not_integer(__snake_case		)
     and _verify_matrix_sizes(__snake_case  ,		__snake_case		)
 ):
  return [[i - j for i, j in zip(*__snake_case		)] for m in zip(__snake_case  ,		__snake_case		)]
 raise TypeError('''Expected a matrix, got int/list instead'''		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    list[list[float]]:







 """simple docstring"""


 return [[x * n for x in row] for row in matrix]
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    list[list[int]]:







 """simple docstring"""


 if _check_not_integer(__snake_case		) and _check_not_integer(__snake_case		):
  _lowercase   ,					_lowercase							   =_verify_matrix_sizes(__snake_case  ,		__snake_case		)

 if cols[0] != rows[1]:
  _lowercase							   =(
      '''Cannot multiply matrix of dimensions '''
      F"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
  )
  raise ValueError(__snake_case		)
 return [
     [sum(m * n for m, n in zip(__snake_case  ,		__snake_case		)		) for j in zip(*__snake_case		)] for i in matrix_a
 ]
def        UpperCAmelCase_      (							__snake_case		)  ->    list[list[int]]:







 """simple docstring"""


 _lowercase							   =int(__snake_case		)
 return [[int(row == column		) for column in range(__snake_case		)] for row in range(__snake_case		)]
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case = True		)  ->    list[list[int]] | map[list[int]]:







 """simple docstring"""


 if _check_not_integer(__snake_case		):
  if return_map:
   return map(__snake_case  ,		zip(*__snake_case		)		)
  else:
   return list(map(__snake_case  ,		zip(*__snake_case		)		)		)
 raise TypeError('''Expected a matrix, got int/list instead'''		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case		)  ->    list[list[int]]:







 """simple docstring"""


 _lowercase							   =matrix[:row] + matrix[row + 1 :]
 return [row[:column] + row[column + 1 :] for row in minor]
def        UpperCAmelCase_      (							__snake_case		)  ->    Any:







 """simple docstring"""


 if len(__snake_case		) == 1:
  return matrix[0][0]

 return sum(
     x * determinant(minor(__snake_case  ,		0  ,		__snake_case		)		) * (-1) ** i
     for i, x in enumerate(matrix[0]		)		)
def        UpperCAmelCase_      (							__snake_case		)  ->    list[list[float]] | None:







 """simple docstring"""


 _lowercase							   =determinant(__snake_case		)
 if det == 0:
  return None

 _lowercase							   =[
     [determinant(minor(__snake_case  ,		__snake_case  ,		__snake_case		)		) for j in range(len(__snake_case		)		)]
     for i in range(len(__snake_case		)		)
 ]

 _lowercase							   =[
     [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]		)]
     for row in range(len(__snake_case		)		)
 ]
 _lowercase							   =list(transpose(__snake_case		)		)
 return scalar_multiply(__snake_case  ,		1 / det		)
def        UpperCAmelCase_      (							__snake_case		)  ->    bool:







 """simple docstring"""


 return not isinstance(__snake_case  ,		__snake_case		) and not isinstance(matrix[0]  ,		__snake_case		)
def        UpperCAmelCase_      (							__snake_case		)  ->    tuple[int, int]:







 """simple docstring"""


 return len(__snake_case		), len(matrix[0]		)
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    tuple[tuple[int, int], tuple[int, int]]:







 """simple docstring"""


 _lowercase							   =_shape(__snake_case		) + _shape(__snake_case		)
 if shape[0] != shape[3] or shape[1] != shape[2]:
  _lowercase							   =(
      '''operands could not be broadcast together with shape '''
      F"({shape[0], shape[1]}), ({shape[2], shape[3]})"
  )
  raise ValueError(__snake_case		)
 return (shape[0], shape[2]), (shape[1], shape[3])
def        UpperCAmelCase_      (							)  ->    None:







 """simple docstring"""


 _lowercase							   =[[12, 10], [3, 9]]
 _lowercase							   =[[3, 4], [7, 4]]
 _lowercase							   =[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
 _lowercase							   =[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
 print(F"Add Operation, {add(__snake_case  ,		__snake_case		) = } \n"		)
 print(
     F"Multiply Operation, {multiply(__snake_case  ,		__snake_case		) = } \n"  ,		)
 print(F"Identity: {identity(5		)}\n"		)
 print(F"Minor of {matrix_c} = {minor(__snake_case  ,		1  ,		2		)} \n"		)
 print(F"Determinant of {matrix_b} = {determinant(__snake_case		)} \n"		)
 print(F"Inverse of {matrix_d} = {inverse(__snake_case		)}\n"		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch







class    lowerCamelCase__     :





     def __init__(self	,  UpperCAmelCase	,  UpperCAmelCase=1_4	,  UpperCAmelCase=7	,  UpperCAmelCase=True	,  UpperCAmelCase=True	,  UpperCAmelCase=False	,  UpperCAmelCase=True	,  UpperCAmelCase=9_9	,  UpperCAmelCase=3_2	,  UpperCAmelCase=5	,  UpperCAmelCase=4	,  UpperCAmelCase=3_7	,  UpperCAmelCase="gelu"	,  UpperCAmelCase=0.1	,  UpperCAmelCase=0.1	,  UpperCAmelCase=5_1_2	,  UpperCAmelCase=0.02	,  )  ->     Any:
      _lowercase							   =parent
      _lowercase							   =batch_size
      _lowercase							   =seq_length
      _lowercase							   =is_training
      _lowercase							   =use_input_mask
      _lowercase							   =use_token_type_ids
      _lowercase							   =use_labels
      _lowercase							   =vocab_size
      _lowercase							   =hidden_size
      _lowercase							   =num_hidden_layers
      _lowercase							   =num_attention_heads
      _lowercase							   =intermediate_size
      _lowercase							   =hidden_act
      _lowercase							   =hidden_dropout_prob
      _lowercase							   =attention_probs_dropout_prob
      _lowercase							   =max_position_embeddings
      _lowercase							   =initializer_range
      _lowercase							   =None
      _lowercase							   =vocab_size - 1
      _lowercase							   =vocab_size - 1
      _lowercase							   =vocab_size - 1





     def   __A      (self )  ->     int:
      _lowercase							   =ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

      _lowercase							   =None
      if self.use_input_mask:
       _lowercase							   =random_attention_mask([self.batch_size, self.seq_length] )

      _lowercase							   =GPTaConfig(
          vocab_size=self.vocab_size	,  n_embd=self.hidden_size	,  n_layer=self.num_hidden_layers	,  n_head=self.num_attention_heads	,  n_positions=self.max_position_embeddings	,  use_cache=UpperCAmelCase	,  bos_token_id=self.bos_token_id	,  eos_token_id=self.eos_token_id	,  pad_token_id=self.pad_token_id	,  )

      return (config, input_ids, input_mask)





     def   __A      (self )  ->     List[Any]:
      _lowercase							   =self.prepare_config_and_inputs()
      _lowercase   ,					_lowercase   ,					_lowercase							   =config_and_inputs
      _lowercase							   ={'''input_ids''': input_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict





     def   __A      (self )  ->     Tuple:
      _lowercase   ,					_lowercase   ,					_lowercase							   =self.prepare_config_and_inputs()

      _lowercase							   =floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      _lowercase							   =ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2 )

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





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Dict:
      _lowercase							   =2_0
      _lowercase							   =model_class_name(UpperCAmelCase )

      _lowercase							   =model.init_cache(input_ids.shape[0]	,  UpperCAmelCase )
      _lowercase							   =jnp.ones((input_ids.shape[0], max_decoder_length)	,  dtype='''i4''' )

      _lowercase							   =jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
      _lowercase							   =model(
          input_ids[:, :-1]	,  attention_mask=UpperCAmelCase	,  past_key_values=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,  dtype='''i4''' )
      _lowercase							   =model(
          input_ids[:, -1:]	,  attention_mask=UpperCAmelCase	,  past_key_values=outputs_cache.past_key_values	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =model(UpperCAmelCase )

      _lowercase							   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
      self.parent.assertTrue(diff < 1e-3	,  msg=f"Max diff is {diff}" )





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     List[str]:
      _lowercase							   =2_0
      _lowercase							   =model_class_name(UpperCAmelCase )

      _lowercase							   =jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]	,  axis=-1	,  )

      _lowercase							   =model.init_cache(input_ids.shape[0]	,  UpperCAmelCase )
      _lowercase							   =jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]	,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

      _lowercase							   =model(
          input_ids[:, :-1]	,  attention_mask=UpperCAmelCase	,  past_key_values=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )
      _lowercase							   =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,  dtype='''i4''' )
      _lowercase							   =model(
          input_ids[:, -1:]	,  past_key_values=outputs_cache.past_key_values	,  attention_mask=UpperCAmelCase	,  position_ids=UpperCAmelCase	,  )

      _lowercase							   =model(UpperCAmelCase	,  attention_mask=UpperCAmelCase )

      _lowercase							   =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
      self.parent.assertTrue(diff < 1e-3	,  msg=f"Max diff is {diff}" )







@require_flax
class    lowerCamelCase__     ( lowerCAmelCase     ,							lowerCAmelCase     ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__       =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE__       =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()





     def   __A      (self )  ->     str:
      _lowercase							   =FlaxGPTaModelTester(self )





     def   __A      (self )  ->     Optional[int]:
      for model_class_name in self.all_model_classes:
       _lowercase   ,					_lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )





     def   __A      (self )  ->     Dict:
      for model_class_name in self.all_model_classes:
       _lowercase   ,					_lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )





     @slow
     def   __A      (self )  ->     Dict:
      _lowercase							   =GPTaTokenizer.from_pretrained('''gpt2'''	,  pad_token='''</s>'''	,  padding_side='''left''' )
      _lowercase							   =tokenizer(['''Hello this is a long string''', '''Hey''']	,  return_tensors='''np'''	,  padding=UpperCAmelCase	,  truncation=UpperCAmelCase )

      _lowercase							   =FlaxGPTaLMHeadModel.from_pretrained('''gpt2''' )
      _lowercase							   =False
      _lowercase							   =model.config.eos_token_id

      _lowercase							   =jax.jit(model.generate )

      _lowercase							   =jit_generate(inputs['''input_ids''']	,  attention_mask=inputs['''attention_mask'''] ).sequences

      _lowercase							   =tokenizer.batch_decode(UpperCAmelCase	,  skip_special_tokens=UpperCAmelCase )

      _lowercase							   =[
          '''Hello this is a long string of words. I\'m going to start with the first one.\n''',
          '''Hey, I\'m not sure if I\'m going to be able to do''',
      ]

      self.assertListEqual(UpperCAmelCase	,  UpperCAmelCase )





     @is_pt_flax_cross_test
     def   __A      (self )  ->     Optional[Any]:
      _lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
        # prepare inputs
        _lowercase							   =self._prepare_for_class(UpperCAmelCase	,  UpperCAmelCase )
        _lowercase							   ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        _lowercase							   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
        _lowercase							   =getattr(UpperCAmelCase	,  UpperCAmelCase )

        _lowercase   ,					_lowercase							   =pt_inputs['''input_ids'''].shape
        _lowercase							   =np.random.randint(0	,  seq_length - 1	,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(UpperCAmelCase ):
         _lowercase							   =0
         _lowercase							   =1
         _lowercase							   =0
         _lowercase							   =1
        _lowercase							   =pt_model_class(UpperCAmelCase ).eval()
        _lowercase							   =model_class(UpperCAmelCase	,  dtype=jnp.floataa )

        _lowercase							   =convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,  UpperCAmelCase )
        _lowercase							   =fx_state

        with torch.no_grad():
         _lowercase							   =pt_model(**UpperCAmelCase ).to_tuple()

        _lowercase							   =fx_model(**UpperCAmelCase ).to_tuple()
        self.assertEqual(len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(UpperCAmelCase )
         _lowercase							   =model_class.from_pretrained(UpperCAmelCase	,  from_pt=UpperCAmelCase )

        _lowercase							   =fx_model_loaded(**UpperCAmelCase ).to_tuple()
        self.assertEqual(
            len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output_loaded, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output_loaded[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )





     @is_pt_flax_cross_test
     def   __A      (self )  ->     Optional[Any]:
      _lowercase   ,					_lowercase							   =self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
        # prepare inputs
        _lowercase							   =self._prepare_for_class(UpperCAmelCase	,  UpperCAmelCase )
        _lowercase							   ={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        _lowercase							   =model_class.__name__[4:]  # Skip the "Flax" at the beginning
        _lowercase							   =getattr(UpperCAmelCase	,  UpperCAmelCase )

        _lowercase							   =pt_model_class(UpperCAmelCase ).eval()
        _lowercase							   =model_class(UpperCAmelCase	,  dtype=jnp.floataa )

        _lowercase							   =load_flax_weights_in_pytorch_model(UpperCAmelCase	,  fx_model.params )
        _lowercase   ,					_lowercase							   =pt_inputs['''input_ids'''].shape
        _lowercase							   =np.random.randint(0	,  seq_length - 1	,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(UpperCAmelCase ):
         _lowercase							   =0
         _lowercase							   =1
         _lowercase							   =0
         _lowercase							   =1

        # make sure weights are tied in PyTorch
        pt_model.tie_weights()

        with torch.no_grad():
         _lowercase							   =pt_model(**UpperCAmelCase ).to_tuple()

        _lowercase							   =fx_model(**UpperCAmelCase ).to_tuple()
        self.assertEqual(len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(UpperCAmelCase )
         _lowercase							   =pt_model_class.from_pretrained(UpperCAmelCase	,  from_flax=UpperCAmelCase )

        with torch.no_grad():
         _lowercase							   =pt_model_loaded(**UpperCAmelCase ).to_tuple()

        self.assertEqual(
            len(UpperCAmelCase )	,  len(UpperCAmelCase )	,  '''Output lengths differ between Flax and PyTorch''' )
        for fx_output, pt_output in zip(UpperCAmelCase	,  UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]	,  pt_output[:, -1].numpy()	,  4e-2 )





     @slow
     def   __A      (self )  ->     Union[str, Any]:
      for model_class_name in self.all_model_classes:
       _lowercase							   =model_class_name.from_pretrained('''gpt2'''	,  from_pt=UpperCAmelCase )
       _lowercase							   =model(np.ones((1, 1) ) )
       self.assertIsNotNone(UpperCAmelCase )

