


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:				Optional[Any],A_:				List[str],A_:				Dict=14,A_:				Tuple=7,A_:				str=True,A_:				Optional[Any]=True,A_:				List[Any]=False,A_:				List[str]=True,A_:				List[str]=99,A_:				Optional[Any]=32,A_:				List[Any]=5,A_:				str=4,A_:				Union[str, Any]=37,A_:				Optional[int]="gelu",A_:				Union[str, Any]=0.1,A_:				List[str]=0.1,A_:				str=512,A_:				Dict=0.0_2,):






            '''simple docstring'''




            __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				=	intermediate_size
            __UpperCamelCase				=	hidden_act
            __UpperCamelCase				=	hidden_dropout_prob
            __UpperCamelCase				=	attention_probs_dropout_prob
            __UpperCamelCase				=	max_position_embeddings
            __UpperCamelCase				=	initializer_range
            __UpperCamelCase				=	None
            __UpperCamelCase				=	vocab_size - 1
            __UpperCamelCase				=	vocab_size - 1
            __UpperCamelCase				=	vocab_size - 1







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __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				=	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=A_,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 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




            __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:				Any			):






            '''simple docstring'''




            __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase				=	self.prepare_config_and_inputs()

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

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







       def 						snake_case_					(	self:				Dict,A_:				List[Any],A_:				Dict,A_:				List[str],A_:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	20
            __UpperCamelCase				=	model_class_name(A_			)

            __UpperCamelCase				=	model.init_cache(input_ids.shape[0],A_			)
            __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=A_,past_key_values=A_,position_ids=A_,)

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

            __UpperCamelCase				=	model(A_			)

            __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:				Any,A_:				Optional[Any],A_:				Optional[Any],A_:				int,A_:				Tuple			):






            '''simple docstring'''




            __UpperCamelCase				=	20
            __UpperCamelCase				=	model_class_name(A_			)

            __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],A_			)
            __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=A_,past_key_values=A_,position_ids=A_,)
            __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=A_,position_ids=A_,)

            __UpperCamelCase				=	model(A_,attention_mask=A_			)

            __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	(_a							,     _a							,     unittest.TestCase		):
       _lowercase    							=      (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
       _lowercase    							=      (FlaxGPTaLMHeadModel,) if is_flax_available() else ()







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            __UpperCamelCase				=	FlaxGPTaModelTester(self			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            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(A_,A_,A_,A_			)







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






            '''simple docstring'''




            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(
                     A_,A_,A_,A_			)







       @slow
       def 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	GPTaTokenizer.from_pretrained('gpt2',pad_token='</s>',padding_side='left'			)
            __UpperCamelCase				=	tokenizer(['Hello this is a long string', 'Hey'],return_tensors='np',padding=A_,truncation=A_			)

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

            __UpperCamelCase				=	jax.jit(model.generate			)

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

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

            __UpperCamelCase				=	[
                '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(A_,A_			)







       @is_pt_flax_cross_test
       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            __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(A_,A_			)
                      __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(A_,A_			)

                      __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(A_			):
                           __UpperCamelCase				=	0
                           __UpperCamelCase				=	1
                           __UpperCamelCase				=	0
                           __UpperCamelCase				=	1
                      __UpperCamelCase				=	pt_model_class(A_			).eval()
                      __UpperCamelCase				=	model_class(A_,dtype=jnp.floataa			)

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

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

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

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

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







       @is_pt_flax_cross_test
       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __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(A_,A_			)
                      __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(A_,A_			)

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

                      __UpperCamelCase				=	load_flax_weights_in_pytorch_model(A_,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(A_			):
                           __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(**A_			).to_tuple()

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

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

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

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







       @slow
       def 						snake_case_					(	self:				Union[str, Any]			):






            '''simple docstring'''




            for model_class_name in self.all_model_classes:
                 __UpperCamelCase				=	model_class_name.from_pretrained('gpt2',from_pt=A_			)
                 __UpperCamelCase				=	model(np.ones((1, 1)			)			)
                 self.assertIsNotNone(A_			)






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	len(_lowercase				)  # Number of training samples
     __UpperCamelCase				=	np.eye(_lowercase				)  # Initialize weights as identity matrix
     for j in range(_lowercase				):
          __UpperCamelCase				=	point - x_train[j]
          __UpperCamelCase				=	np.exp(diff @ diff.T / (-2.0 * tau**2)				)

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

     # pairing elements of one and x_data
     __UpperCamelCase				=	np.column_stack((one, x_data)				)

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       color='blue'				)
     plt.plot(
         x_train_sorted[:, 1]			,       preds[x_train[:, 1].argsort(0				)]			,       color='yellow'			,       linewidth=5			,       )
     plt.title('Local Weighted Regression'				)
     plt.xlabel(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


