





from typing import Any

import numpy as np





def    _lowercase    (  _UpperCAmelCase )  ->     bool:
				assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[1]

				# If matrix not symmetric, exit right away.
				if np.allclose(_UpperCAmelCase	,					matrix.T ) is False:
								return False

				# Get eigenvalues and eignevectors for a symmetric matrix.
				lowerCamelCase       ,     lowerCamelCase		       =np.linalg.eigh(_UpperCAmelCase )

				# Check sign of all eigenvalues.
				# np.all returns a value of type np.bool_
				return bool(np.all(eigen_values > 0 ) )





def    _lowercase    (  _UpperCAmelCase )  ->     Any:
				lowerCamelCase		       =np.random.randn(_UpperCAmelCase	,					_UpperCAmelCase )
				lowerCamelCase		       =np.dot(_UpperCAmelCase	,					random_matrix.T )
				assert _is_matrix_spd(_UpperCAmelCase )
				return spd_matrix





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase = 10_00	,					_UpperCAmelCase = 1e-8	,					)  ->     Any:
				assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[1]
				assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmelCase )[0]
				assert _is_matrix_spd(_UpperCAmelCase )

				# Initialize solution guess, residual, search direction.
				lowerCamelCase		       =np.zeros((np.shape(_UpperCAmelCase )[0], 1) )
				lowerCamelCase		       =np.copy(_UpperCAmelCase )
				lowerCamelCase		       =np.copy(_UpperCAmelCase )

				# Set initial errors in solution guess and residual.
				lowerCamelCase		       =1e9
				lowerCamelCase		       =1e9
				lowerCamelCase		       =1e9

				# Set iteration counter to threshold number of iterations.
				lowerCamelCase		       =0

				while error > tol:
								# Save this value so we only calculate the matrix-vector product once.
								lowerCamelCase		       =np.dot(_UpperCAmelCase	,					_UpperCAmelCase )

								# The main algorithm.

								# Update search direction magnitude.
								lowerCamelCase		       =np.dot(ra.T	,					_UpperCAmelCase ) / np.dot(pa.T	,					_UpperCAmelCase )
								# Update solution guess.
								lowerCamelCase		       =xa + alpha * pa
								# Calculate new residual.
								lowerCamelCase		       =ra - alpha * w
								# Calculate new Krylov subspace scale.
								lowerCamelCase		       =np.dot(r.T	,					_UpperCAmelCase ) / np.dot(ra.T	,					_UpperCAmelCase )
								# Calculate new A conjuage search direction.
								lowerCamelCase		       =r + beta * pa

								# Calculate errors.
								lowerCamelCase		       =np.linalg.norm(r - ra )
								lowerCamelCase		       =np.linalg.norm(x - xa )
								lowerCamelCase		       =np.maximum(_UpperCAmelCase	,					_UpperCAmelCase )

								# Update variables.
								lowerCamelCase		       =np.copy(_UpperCAmelCase )
								lowerCamelCase		       =np.copy(_UpperCAmelCase )
								lowerCamelCase		       =np.copy(_UpperCAmelCase )

								# Update number of iterations.
								iterations += 1
								if iterations > max_iterations:
												break

				return x





def    _lowercase    (  )  ->     None:
				lowerCamelCase		       =3
				lowerCamelCase		       =_create_spd_matrix(_UpperCAmelCase )
				lowerCamelCase		       =np.random.randn(_UpperCAmelCase	,					1 )
				lowerCamelCase		       =np.dot(_UpperCAmelCase	,					_UpperCAmelCase )

				# Numpy solution.
				lowerCamelCase		       =np.linalg.solve(_UpperCAmelCase	,					_UpperCAmelCase )

				# Our implementation.
				lowerCamelCase		       =conjugate_gradient(_UpperCAmelCase	,					_UpperCAmelCase )

				# Ensure both solutions are close to x_true (and therefore one another).
				assert np.linalg.norm(x_numpy - x_true ) <= 1e-6
				assert np.linalg.norm(x_conjugate_gradient - x_true ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_conjugate_gradient()











import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any]=14       ,				snake_case    :       Optional[Any]=7       ,				snake_case    :       Tuple=True       ,				snake_case    :       int=True       ,				snake_case    :       Dict=False       ,				snake_case    :       List[Any]=True       ,				snake_case    :       Optional[int]=99       ,				snake_case    :       Tuple=32       ,				snake_case    :       Any=4       ,				snake_case    :       List[Any]=4       ,				snake_case    :       int=[[["global", "local"], 2]]       ,				snake_case    :       Optional[Any]=37       ,				snake_case    :       Optional[Any]="gelu"       ,				snake_case    :       Dict=0.1       ,				snake_case    :       List[Any]=0.1       ,				snake_case    :       Union[str, Any]=512       ,				snake_case    :       int=7       ,				snake_case    :       Any=0.02       ,				):
          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  seq_length
          __UpperCamelCase     						=  is_training
          __UpperCamelCase     						=  use_input_mask
          __UpperCamelCase     						=  use_token_type_ids
          __UpperCamelCase     						=  use_labels
          __UpperCamelCase     						=  vocab_size
          __UpperCamelCase     						=  hidden_size
          __UpperCamelCase     						=  num_hidden_layers
          __UpperCamelCase     						=  num_attention_heads
          __UpperCamelCase     						=  attention_types
          __UpperCamelCase     						=  intermediate_size
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  hidden_dropout_prob
          __UpperCamelCase     						=  attention_probs_dropout_prob
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  window_size
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1







      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size )

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

          __UpperCamelCase     						=  GPTNeoConfig(
              vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_layers=self.num_hidden_layers       ,				num_heads=self.num_attention_heads       ,				max_position_embeddings=self.max_position_embeddings       ,				use_cache=snake_case       ,				bos_token_id=self.bos_token_id       ,				eos_token_id=self.eos_token_id       ,				pad_token_id=self.pad_token_id       ,				window_size=self.window_size       ,				attention_types=self.attention_types       ,				)

          return (config, input_ids, input_mask)







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  config_and_inputs
          __UpperCamelCase     						=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int] ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

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

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

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

          __UpperCamelCase     						=  model(snake_case )

          __UpperCamelCase     						=  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 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       Dict       ,				snake_case    :       Dict       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Tuple ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

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

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

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

          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case )

          __UpperCamelCase     						=  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  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       Dict			=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__	:       Any			=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  FlaxGPTNeoModelTester(self )







      def 					snake_case		(   self    :       int ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      @slow
      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  GPTaTokenizer.from_pretrained('''gpt2'''       ,				pad_token='''<|endoftext|>'''       ,				padding_side='''left''' )
          __UpperCamelCase     						=  tokenizer(['''Hello this is a long string''', '''Hey''']       ,				return_tensors='''np'''       ,				padding=snake_case       ,				truncation=snake_case )

          __UpperCamelCase     						=  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
          __UpperCamelCase     						=  False
          __UpperCamelCase     						=  model.config.eos_token_id

          __UpperCamelCase     						=  jax.jit(model.generate )

          __UpperCamelCase     						=  jit_generate(
              inputs['''input_ids''']       ,				attention_mask=inputs['''attention_mask''']       ,				pad_token_id=tokenizer.pad_token_id ).sequences

          __UpperCamelCase     						=  tokenizer.batch_decode(snake_case       ,				skip_special_tokens=snake_case )

          __UpperCamelCase     						=  [
              '''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(snake_case       ,				snake_case )







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

                  __UpperCamelCase     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,				snake_case )
                  __UpperCamelCase     						=  fx_state

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

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

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

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







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

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

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

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model(**snake_case ).to_tuple()

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

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

                  with torch.no_grad():
                      __UpperCamelCase     						=  pt_model_loaded(**snake_case ).to_tuple()

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







      @slow
      def 					snake_case		(   self    :       Tuple ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase     						=  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
              __UpperCamelCase     						=  model(np.ones((1, 1) ) )
              self.assertIsNotNone(snake_case )






