'''simple docstring'''

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 				lowercase    :


   def __init__( self					:						Union[str, Any] ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						Tuple=14 ,		__lowerCAmelCase					:						List[Any]=7 ,		__lowerCAmelCase					:						Any=True ,		__lowerCAmelCase					:						Any=True ,		__lowerCAmelCase					:						Dict=False ,		__lowerCAmelCase					:						Optional[Any]=True ,		__lowerCAmelCase					:						Union[str, Any]=99 ,		__lowerCAmelCase					:						Tuple=32 ,		__lowerCAmelCase					:						Optional[int]=4 ,		__lowerCAmelCase					:						str=4 ,		__lowerCAmelCase					:						Optional[int]=[[["global", "local"], 2]] ,		__lowerCAmelCase					:						List[str]=37 ,		__lowerCAmelCase					:						Dict="gelu" ,		__lowerCAmelCase					:						int=0.1 ,		__lowerCAmelCase					:						Dict=0.1 ,		__lowerCAmelCase					:						Tuple=512 ,		__lowerCAmelCase					:						List[Any]=7 ,		__lowerCAmelCase					:						Optional[int]=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_     			=   attention_types
         lowercase_     			=   intermediate_size
         lowercase_     			=   hidden_act
         lowercase_     			=   hidden_dropout_prob
         lowercase_     			=   attention_probs_dropout_prob
         lowercase_     			=   max_position_embeddings
         lowercase_     			=   window_size
         lowercase_     			=   initializer_range
         lowercase_     			=   None
         lowercase_     			=   vocab_size - 1
         lowercase_     			=   vocab_size - 1
         lowercase_     			=   vocab_size - 1


   def __UpperCAmelCase							( self					:						Optional[int])   ->       Tuple:
         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_     			=   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=__lowerCAmelCase ,		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 __UpperCAmelCase							( self					:						Any)   ->       str:
         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 __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Tuple)   ->       int:
         lowercase_     			=   20
         lowercase_     			=   model_class_name(__lowerCAmelCase)

         lowercase_     			=   model.init_cache(input_ids.shape[0] ,		__lowerCAmelCase)
         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=__lowerCAmelCase ,		past_key_values=__lowerCAmelCase ,		position_ids=__lowerCAmelCase ,		)

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

         lowercase_     			=   model(__lowerCAmelCase)

         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 __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						Tuple)   ->       List[str]:
         lowercase_     			=   20
         lowercase_     			=   model_class_name(__lowerCAmelCase)

         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] ,		__lowerCAmelCase)
         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=__lowerCAmelCase ,		past_key_values=__lowerCAmelCase ,		position_ids=__lowerCAmelCase ,		)
         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=__lowerCAmelCase ,		position_ids=__lowerCAmelCase ,		)

         lowercase_     			=   model(__lowerCAmelCase ,		attention_mask=__lowerCAmelCase)

         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 				lowercase    (    __lowerCamelCase			,					__lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   lowerCamelCase_            =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


   def __UpperCAmelCase							( self					:						List[str])   ->       Tuple:
         lowercase_     			=   FlaxGPTNeoModelTester(self)


   def __UpperCAmelCase							( self					:						Any)   ->       Optional[Any]:
         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(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Tuple)   ->       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_with_attn_mask(
                   __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase)


   @slow
   def __UpperCAmelCase							( self					:						str)   ->       Optional[int]:
         lowercase_     			=   GPTaTokenizer.from_pretrained("gpt2" ,		pad_token="<|endoftext|>" ,		padding_side="left")
         lowercase_     			=   tokenizer(["Hello this is a long string", "Hey"] ,		return_tensors="np" ,		padding=__lowerCAmelCase ,		truncation=__lowerCAmelCase)

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

         lowercase_     			=   jax.jit(model.generate)

         lowercase_     			=   jit_generate(
             inputs["input_ids"] ,		attention_mask=inputs["attention_mask"] ,		pad_token_id=tokenizer.pad_token_id).sequences

         lowercase_     			=   tokenizer.batch_decode(__lowerCAmelCase ,		skip_special_tokens=__lowerCAmelCase)

         lowercase_     			=   [
             "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(__lowerCAmelCase ,		__lowerCAmelCase)


   @is_pt_flax_cross_test
   def __UpperCAmelCase							( self					:						Any)   ->       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(__lowerCAmelCase ,		__lowerCAmelCase)
                     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(__lowerCAmelCase ,		__lowerCAmelCase)

                     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(__lowerCAmelCase):
                           lowercase_     			=   0
                           lowercase_     			=   1
                           lowercase_     			=   0
                           lowercase_     			=   1
                     lowercase_     			=   pt_model_class(__lowerCAmelCase).eval()
                     lowercase_     			=   model_class(__lowerCAmelCase ,		dtype=jnp.floataa)

                     lowercase_     			=   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,		__lowerCAmelCase)
                     lowercase_     			=   fx_state

                     with torch.no_grad():
                           lowercase_     			=   pt_model(**__lowerCAmelCase).to_tuple()

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

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

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


   @is_pt_flax_cross_test
   def __UpperCAmelCase							( self					:						Optional[int])   ->       str:
         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(__lowerCAmelCase ,		__lowerCAmelCase)
                     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(__lowerCAmelCase ,		__lowerCAmelCase)

                     lowercase_     			=   pt_model_class(__lowerCAmelCase).eval()
                     lowercase_     			=   model_class(__lowerCAmelCase ,		dtype=jnp.floataa)

                     lowercase_     			=   load_flax_weights_in_pytorch_model(__lowerCAmelCase ,		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(__lowerCAmelCase):
                           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(**__lowerCAmelCase).to_tuple()

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

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

                     with torch.no_grad():
                           lowercase_     			=   pt_model_loaded(**__lowerCAmelCase).to_tuple()

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


   @slow
   def __UpperCAmelCase							( self					:						int)   ->       Tuple:
         for model_class_name in self.all_model_classes:
               lowercase_     			=   model_class_name.from_pretrained("EleutherAI/gpt-neo-125M")
               lowercase_     			=   model(np.ones((1, 1)))
               self.assertIsNotNone(__lowerCAmelCase)







'''simple docstring'''

import timeit





class 				lowercase    :


   def __init__( self					:						Dict ,		__lowerCAmelCase					:						Any)   ->       Optional[int]:
         if isinstance(__lowerCAmelCase ,		__lowerCAmelCase):  # Initializes a matrix identical to the one provided.
               lowercase_     			=   arg
               lowercase_     			=   len(__lowerCAmelCase)
         else:  # Initializes a square matrix of the given size and set values to zero.
               lowercase_     			=   arg
               lowercase_     			=   [[0 for _ in range(self.n)] for _ in range(self.n)]


   def __mul__( self					:						int ,		__lowerCAmelCase					:						List[str])   ->       Union[str, Any]:
         lowercase_     			=   Matrix(self.n)
         for i in range(self.n):
               for j in range(self.n):
                     for k in range(self.n):
                           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
         return matrix




def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Dict					)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   Matrix([[1, 0], [0, 1]]					)
      while b > 0:
            if b & 1:
                  matrix *= a
            a *= a
            b >>= 1
      return matrix


def __a			(						__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    Tuple			,		__lowerCamelCase      :    List[Any]					)						->				Dict:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa
      lowercase_     			=   Matrix([[1, 1], [1, 0]]					)
      lowercase_     			=   modular_exponentiation(__lowerCamelCase			,		n - 2					)
      return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def __a			(						__lowerCamelCase      :    List[Any]			,		__lowerCamelCase      :    Union[str, Any]			,		__lowerCamelCase      :    List[str]					)						->				Optional[Any]:
      '''simple docstring'''


      if n == 1:
            return fa
      elif n == 2:
            return fa

      lowercase_     			=   fa
      lowercase_     			=   fa
      n -= 2

      while n > 0:
            lowercase_			,				lowercase_     			=   fn_a + fn_a, fn_a
            n -= 1

      return fn_a


def __a			(						)						->				str:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
      lowercase_     			=   "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print("With matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Optional[Any]:
      '''simple docstring'''


      lowercase_     			=   "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
      lowercase_     			=   "simple_fibonacci(randint(1,70000), 1, 1)"
      lowercase_     			=   timeit.timeit(setup=__lowerCamelCase			,		stmt=__lowerCamelCase			,		number=100					)
      print(
          "Without matrix exponentiation the average execution time is "			,		exec_time / 100					)
      return exec_time


def __a			(						)						->				Tuple:
      '''simple docstring'''


      matrix_exponentiation_time()
      simple_fibonacci_time()


if __name__ == "__main__":
       main()






