




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 		UpperCamelCase__     :

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__=14					,  UpperCamelCase__=7					,  UpperCamelCase__=True					,  UpperCamelCase__=True					,  UpperCamelCase__=False					,  UpperCamelCase__=True					,  UpperCamelCase__=99					,  UpperCamelCase__=32					,  UpperCamelCase__=5					,  UpperCamelCase__=4					,  UpperCamelCase__=37					,  UpperCamelCase__="gelu"					,  UpperCamelCase__=0.1					,  UpperCamelCase__=0.1					,  UpperCamelCase__=512					,  UpperCamelCase__=0.02					,  )					->				int:
           lowerCamelCase       :				Union[str, Any]      			= parent
           lowerCamelCase       :				Tuple      			= batch_size
           lowerCamelCase       :				str      			= seq_length
           lowerCamelCase       :				Any      			= is_training
           lowerCamelCase       :				str      			= use_input_mask
           lowerCamelCase       :				List[str]      			= use_token_type_ids
           lowerCamelCase       :				Optional[Any]      			= use_labels
           lowerCamelCase       :				Dict      			= vocab_size
           lowerCamelCase       :				Dict      			= hidden_size
           lowerCamelCase       :				List[str]      			= num_hidden_layers
           lowerCamelCase       :				int      			= num_attention_heads
           lowerCamelCase       :				Any      			= intermediate_size
           lowerCamelCase       :				int      			= hidden_act
           lowerCamelCase       :				List[str]      			= hidden_dropout_prob
           lowerCamelCase       :				str      			= attention_probs_dropout_prob
           lowerCamelCase       :				Tuple      			= max_position_embeddings
           lowerCamelCase       :				List[Any]      			= initializer_range
           lowerCamelCase       :				Optional[Any]      			= None
           lowerCamelCase       :				str      			= vocab_size - 1
           lowerCamelCase       :				Optional[int]      			= vocab_size - 1
           lowerCamelCase       :				str      			= vocab_size - 1







    def 	_lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				Dict      			= ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size					)

           lowerCamelCase       :				Union[str, Any]      			= None
           if self.use_input_mask:
                  lowerCamelCase       :				Any      			= random_attention_mask([self.batch_size, self.seq_length]					)

           lowerCamelCase       :				Union[str, Any]      			= 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 	_lowercase					(   self					)					->				List[str]:
           lowerCamelCase       :				Optional[Any]      			= self.prepare_config_and_inputs()
           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				int      			= config_and_inputs
           lowerCamelCase       :				str      			= {"input_ids": input_ids, "attention_mask": attention_mask}
           return config, inputs_dict







    def 	_lowercase					(   self					)					->				Dict:
           lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Optional[Any]      			= self.prepare_config_and_inputs()

           lowerCamelCase       :				int      			= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
           lowerCamelCase       :				Optional[int]      			= ids_tensor([self.batch_size, self.seq_length]					,  vocab_size=2					)

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







    def 	_lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Optional[Any]:
           lowerCamelCase       :				Optional[int]      			= 20
           lowerCamelCase       :				Union[str, Any]      			= model_class_name(__UpperCamelCase					)

           lowerCamelCase       :				List[Any]      			= model.init_cache(input_ids.shape[0]					,  __UpperCamelCase					)
           lowerCamelCase       :				str      			= jnp.ones((input_ids.shape[0], max_decoder_length)					,  dtype="i4"					)

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

           lowerCamelCase       :				List[Any]      			= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,  dtype="i4"					)
           lowerCamelCase       :				str      			= model(
               input_ids[:, -1:]					,  attention_mask=__UpperCamelCase					,  past_key_values=outputs_cache.past_key_values					,  position_ids=__UpperCamelCase					,  )

           lowerCamelCase       :				Any      			= model(__UpperCamelCase					)

           lowerCamelCase       :				Tuple      			= 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					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				str:
           lowerCamelCase       :				Union[str, Any]      			= 20
           lowerCamelCase       :				Any      			= model_class_name(__UpperCamelCase					)

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

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

           lowerCamelCase       :				Optional[int]      			= model(
               input_ids[:, :-1]					,  attention_mask=__UpperCamelCase					,  past_key_values=__UpperCamelCase					,  position_ids=__UpperCamelCase					,  )
           lowerCamelCase       :				Dict      			= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,  dtype="i4"					)
           lowerCamelCase       :				Union[str, Any]      			= model(
               input_ids[:, -1:]					,  past_key_values=outputs_cache.past_key_values					,  attention_mask=__UpperCamelCase					,  position_ids=__UpperCamelCase					,  )

           lowerCamelCase       :				Any      			= model(__UpperCamelCase					,  attention_mask=__UpperCamelCase					)

           lowerCamelCase       :				Optional[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 		UpperCamelCase__     (UpperCAmelCase_   ,    UpperCAmelCase_   ,    unittest.TestCase					):

    '''simple docstring'''



    lowerCamelCase_				:     Tuple     					=				(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    lowerCamelCase_				:     List[Any]     					=				(FlaxGPTaLMHeadModel,) if is_flax_available() else ()







    def 	_lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Dict      			= FlaxGPTaModelTester(self					)







    def 	_lowercase					(   self					)					->				Dict:
           for model_class_name in self.all_model_classes:
                  lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				List[Any]      			= self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(__UpperCamelCase					,  __UpperCamelCase					,  __UpperCamelCase					,  __UpperCamelCase					)







    def 	_lowercase					(   self					)					->				Dict:
           for model_class_name in self.all_model_classes:
                  lowerCamelCase			,				lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      __UpperCamelCase					,  __UpperCamelCase					,  __UpperCamelCase					,  __UpperCamelCase					)







    @slow
    def 	_lowercase					(   self					)					->				Union[str, Any]:
           lowerCamelCase       :				Optional[int]      			= GPTaTokenizer.from_pretrained("gpt2"					,  pad_token="</s>"					,  padding_side="left"					)
           lowerCamelCase       :				Any      			= tokenizer(["Hello this is a long string", "Hey"]					,  return_tensors="np"					,  padding=__UpperCamelCase					,  truncation=__UpperCamelCase					)

           lowerCamelCase       :				Dict      			= FlaxGPTaLMHeadModel.from_pretrained("gpt2"					)
           lowerCamelCase       :				int      			= False
           lowerCamelCase       :				Any      			= model.config.eos_token_id

           lowerCamelCase       :				Tuple      			= jax.jit(model.generate					)

           lowerCamelCase       :				Optional[int]      			= jit_generate(inputs["input_ids"]					,  attention_mask=inputs["attention_mask"]					).sequences

           lowerCamelCase       :				str      			= tokenizer.batch_decode(__UpperCamelCase					,  skip_special_tokens=__UpperCamelCase					)

           lowerCamelCase       :				Tuple      			= [
               "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 	_lowercase					(   self					)					->				Any:
           lowerCamelCase			,				lowerCamelCase       :				List[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
                         lowerCamelCase       :				Optional[Any]      			= self._prepare_for_class(__UpperCamelCase					,  __UpperCamelCase					)
                         lowerCamelCase       :				List[Any]      			= {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

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

                         lowerCamelCase       :				Any      			= convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,  __UpperCamelCase					)
                         lowerCamelCase       :				str      			= fx_state

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

                         lowerCamelCase       :				int      			= 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					)
                                lowerCamelCase       :				Dict      			= model_class.from_pretrained(__UpperCamelCase					,  from_pt=__UpperCamelCase					)

                         lowerCamelCase       :				str      			= 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 	_lowercase					(   self					)					->				Optional[int]:
           lowerCamelCase			,				lowerCamelCase       :				Union[str, Any]      			= 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
                         lowerCamelCase       :				Tuple      			= self._prepare_for_class(__UpperCamelCase					,  __UpperCamelCase					)
                         lowerCamelCase       :				List[Any]      			= {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

                         lowerCamelCase       :				Optional[int]      			= pt_model_class(__UpperCamelCase					).eval()
                         lowerCamelCase       :				List[str]      			= model_class(__UpperCamelCase					,  dtype=jnp.floataa					)

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

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

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

                         lowerCamelCase       :				Optional[int]      			= 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					)
                                lowerCamelCase       :				Dict      			= pt_model_class.from_pretrained(__UpperCamelCase					,  from_flax=__UpperCamelCase					)

                         with torch.no_grad():
                                lowerCamelCase       :				List[Any]      			= 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 	_lowercase					(   self					)					->				str:
           for model_class_name in self.all_model_classes:
                  lowerCamelCase       :				str      			= model_class_name.from_pretrained("gpt2"					,  from_pt=__UpperCamelCase					)
                  lowerCamelCase       :				Union[str, Any]      			= model(np.ones((1, 1)					)					)
                  self.assertIsNotNone(__UpperCamelCase					)












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

       for k in range(t - 1   ):
              # select a column of modified matrix A':
              lowerCamelCase       :				Optional[Any]      			= r[k:, [k]]
              # construct first basis vector
              lowerCamelCase       :				Union[str, Any]      			= np.zeros_like(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				int      			= 1.0
              # determine scaling factor
              lowerCamelCase       :				Any      			= np.linalg.norm(_SCREAMING_SNAKE_CASE   )
              # construct vector v for Householder reflection
              lowerCamelCase       :				List[str]      			= x + np.sign(x[0]   ) * alpha * ea
              v /= np.linalg.norm(_SCREAMING_SNAKE_CASE   )

              # construct the Householder matrix
              lowerCamelCase       :				List[Any]      			= np.eye(m - k   ) - 2.0 * v @ v.T
              # pad with ones and zeros as necessary
              lowerCamelCase       :				Union[str, Any]      			= np.block([[np.eye(_SCREAMING_SNAKE_CASE   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

              lowerCamelCase       :				Any      			= q @ q_k.T
              lowerCamelCase       :				List[Any]      			= q_k @ r

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






