
from __future__ import annotations

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)		->						list[float]:


  '''simple docstring'''






  lowercase__		,     lowercase__	: Optional[Any]     		=   coefficient_matrix.shape
  lowercase__		,     lowercase__	: str     		=   constant_matrix.shape

  if rowsa != colsa:
    lowercase__	: Optional[int]     		=   F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
    raise ValueError(lowercase_ )

  if colsa != 1:
    lowercase__	: List[str]     		=   F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
    raise ValueError(lowercase_ )

  if rowsa != rowsa:
    lowercase__	: List[str]     		=   (
        """Coefficient and constant matrices dimensions must be nxn and nx1 but """
        F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
    )
    raise ValueError(lowercase_ )

  if len(lowercase_ ) != rowsa:
    lowercase__	: Dict     		=   (
        """Number of initial values must be equal to number of rows in coefficient """
        F'matrix but received {len(lowercase_ )} and {rowsa}'
    )
    raise ValueError(lowercase_ )

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

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

  lowercase__		,     lowercase__	: Dict     		=   table.shape

  strictly_diagonally_dominant(lowercase_ )

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

  return [float(lowercase_ ) for i in new_val]

def 		UpperCamelCase						(   lowercase_ )		->						bool:


  '''simple docstring'''






  lowercase__		,     lowercase__	: Any     		=   table.shape

  lowercase__	: str     		=   True

  for i in range(0			,			lowercase_ ):
    lowercase__	: List[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()








import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, 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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
     import torch



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=14       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	):


   '''simple docstring'''




   lowercase__	: str     		=   parent
   lowercase__	: Optional[int]     		=   batch_size
   lowercase__	: Optional[int]     		=   seq_length
   lowercase__	: Union[str, Any]     		=   is_training
   lowercase__	: Any     		=   use_input_mask
   lowercase__	: Optional[int]     		=   use_token_type_ids
   lowercase__	: Optional[Any]     		=   use_labels
   lowercase__	: Optional[int]     		=   vocab_size
   lowercase__	: Optional[Any]     		=   hidden_size
   lowercase__	: Any     		=   rotary_dim
   lowercase__	: Optional[Any]     		=   num_hidden_layers
   lowercase__	: Tuple     		=   num_attention_heads
   lowercase__	: Tuple     		=   intermediate_size
   lowercase__	: List[str]     		=   hidden_act
   lowercase__	: Optional[Any]     		=   hidden_dropout_prob
   lowercase__	: int     		=   attention_probs_dropout_prob
   lowercase__	: Any     		=   max_position_embeddings
   lowercase__	: Optional[int]     		=   initializer_range
   lowercase__	: Optional[int]     		=   None
   lowercase__	: str     		=   vocab_size - 1
   lowercase__	: Any     		=   vocab_size - 1
   lowercase__	: Dict     		=   vocab_size - 1


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: Any     		=   None
   if self.use_input_mask:
     lowercase__	: Dict     		=   random_attention_mask([self.batch_size, self.seq_length])

   lowercase__	: List[Any]     		=   GPTJConfig(
       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=SCREAMING_SNAKE_CASE_       ,	bos_token_id=self.bos_token_id       ,	eos_token_id=self.eos_token_id       ,	pad_token_id=self.pad_token_id       ,	rotary_dim=self.rotary_dim       ,	)

   return (config, input_ids, input_mask)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.prepare_config_and_inputs()
   lowercase__		,     lowercase__		,     lowercase__	: Optional[Any]     		=   config_and_inputs
   lowercase__	: Optional[Any]     		=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
   return config, inputs_dict


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   20
   lowercase__	: int     		=   model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[Any]     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="""i4""")

   lowercase__	: Tuple     		=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1))
   lowercase__	: List[str]     		=   model(
       input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: Tuple     		=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="""i4""")
   lowercase__	: str     		=   model(
       input_ids[:, -1:]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=outputs_cache.past_key_values       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: Tuple     		=   model(SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   20
   lowercase__	: List[Any]     		=   model_class_name(SCREAMING_SNAKE_CASE_)

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

   lowercase__	: Dict     		=   model.init_cache(input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   jnp.broadcast_to(
       jnp.arange(input_ids.shape[-1] - 1)[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1))

   lowercase__	: Any     		=   model(
       input_ids[:, :-1]       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: int     		=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="""i4""")
   lowercase__	: Tuple     		=   model(
       input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=SCREAMING_SNAKE_CASE_       ,	position_ids=SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	attention_mask=SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   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    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Dict	   =      (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __lowerCAmelCase	:  str	   =      (FlaxGPTJForCausalLM,) if is_flax_available() else ()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   FlaxGPTJModelTester(self)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__		,     lowercase__		,     lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs()
     self.model_tester.check_use_cache_forward_with_attn_mask(
         SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   GPTaTokenizer.from_pretrained("""gpt2"""       ,	pad_token="""<|endoftext|>"""       ,	padding_side="""left""")
   lowercase__	: List[str]     		=   tokenizer(["""Hello this is a long string""", """Hey"""]       ,	return_tensors="""np"""       ,	padding=SCREAMING_SNAKE_CASE_       ,	truncation=SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""")
   lowercase__	: Optional[Any]     		=   False
   lowercase__	: List[str]     		=   model.config.eos_token_id

   lowercase__	: List[Any]     		=   jax.jit(model.generate)

   lowercase__	: Tuple     		=   jit_generate(
       inputs["""input_ids"""]       ,	attention_mask=inputs["""attention_mask"""]       ,	pad_token_id=tokenizer.pad_token_id).sequences

   lowercase__	: List[str]     		=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,	skip_special_tokens=SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   [
       """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
       """Hey, I'm a little late to the party. I'm going to""",
   ]

   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: int     		=   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__	: List[Any]     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: Any     		=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       lowercase__	: int     		=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
       lowercase__	: str     		=   getattr(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

       lowercase__		,     lowercase__	: Dict     		=   pt_inputs["""input_ids"""].shape
       lowercase__	: int     		=   np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,))
       for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_):
         lowercase__	: str     		=   0
         lowercase__	: List[Any]     		=   1
         lowercase__	: Dict     		=   0
         lowercase__	: Any     		=   1
       lowercase__	: List[Any]     		=   pt_model_class(SCREAMING_SNAKE_CASE_).eval()
       lowercase__	: Optional[int]     		=   model_class(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.floataa)

       lowercase__	: List[str]     		=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: List[Any]     		=   fx_state

       with torch.no_grad():
         lowercase__	: Optional[int]     		=   pt_model(**SCREAMING_SNAKE_CASE_).to_tuple()

       lowercase__	: Dict     		=   fx_model(**SCREAMING_SNAKE_CASE_).to_tuple()
       self.assertEqual(len(SCREAMING_SNAKE_CASE_)       ,	len(SCREAMING_SNAKE_CASE_)       ,	"""Output lengths differ between Flax and PyTorch""")
       for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
         self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4E-2)

       with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: List[str]     		=   model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_pt=SCREAMING_SNAKE_CASE_)

       lowercase__	: str     		=   fx_model_loaded(**SCREAMING_SNAKE_CASE_).to_tuple()
       self.assertEqual(
           len(SCREAMING_SNAKE_CASE_)       ,	len(SCREAMING_SNAKE_CASE_)       ,	"""Output lengths differ between Flax and PyTorch""")
       for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
         self.assert_almost_equals(fx_output_loaded[:, -1]       ,	pt_output[:, -1].numpy()       ,	4E-2)


 @is_pt_flax_cross_test
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   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__	: Tuple     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: str     		=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

       # load corresponding PyTorch class
       lowercase__	: int     		=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
       lowercase__	: Optional[int]     		=   getattr(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

       lowercase__	: str     		=   pt_model_class(SCREAMING_SNAKE_CASE_).eval()
       lowercase__	: Union[str, Any]     		=   model_class(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.floataa)

       lowercase__	: Optional[int]     		=   load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_       ,	fx_model.params)
       lowercase__		,     lowercase__	: str     		=   pt_inputs["""input_ids"""].shape
       lowercase__	: List[Any]     		=   np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,))
       for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_):
         lowercase__	: Tuple     		=   0
         lowercase__	: int     		=   1
         lowercase__	: str     		=   0
         lowercase__	: str     		=   1

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

       with torch.no_grad():
         lowercase__	: Dict     		=   pt_model(**SCREAMING_SNAKE_CASE_).to_tuple()

       lowercase__	: Optional[Any]     		=   fx_model(**SCREAMING_SNAKE_CASE_).to_tuple()
       self.assertEqual(len(SCREAMING_SNAKE_CASE_)       ,	len(SCREAMING_SNAKE_CASE_)       ,	"""Output lengths differ between Flax and PyTorch""")
       for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
         self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4E-2)

       with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(SCREAMING_SNAKE_CASE_)
         lowercase__	: Tuple     		=   pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_       ,	from_flax=SCREAMING_SNAKE_CASE_)

       with torch.no_grad():
         lowercase__	: Tuple     		=   pt_model_loaded(**SCREAMING_SNAKE_CASE_).to_tuple()

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



 @tooslow
 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__	: Any     		=   model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""")
     lowercase__	: int     		=   model(np.ones((1, 1)))
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)






