




"""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       UpperCAmelCase_    :




    def __init__(       self		:			List[str]      ,					snake_case_		:			List[Any]      ,					snake_case_		:			Tuple=14      ,					snake_case_		:			Optional[Any]=7      ,					snake_case_		:			Any=True      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			List[str]=False      ,					snake_case_		:			Dict=True      ,					snake_case_		:			Union[str, Any]=99      ,					snake_case_		:			List[str]=32      ,					snake_case_		:			str=4      ,					snake_case_		:			int=4      ,					snake_case_		:			List[str]=[[["global", "local"], 2]]      ,					snake_case_		:			int=37      ,					snake_case_		:			str="gelu"      ,					snake_case_		:			Union[str, Any]=0.1      ,					snake_case_		:			str=0.1      ,					snake_case_		:			Optional[int]=512      ,					snake_case_		:			List[str]=7      ,					snake_case_		:			Tuple=0.02      ,					) ->				List[str]:




     '''simple docstring'''

     A__			=							parent
     A__			=							batch_size
     A__			=							seq_length
     A__			=							is_training
     A__			=							use_input_mask
     A__			=							use_token_type_ids
     A__			=							use_labels
     A__			=							vocab_size
     A__			=							hidden_size
     A__			=							num_hidden_layers
     A__			=							num_attention_heads
     A__			=							attention_types
     A__			=							intermediate_size
     A__			=							hidden_act
     A__			=							hidden_dropout_prob
     A__			=							attention_probs_dropout_prob
     A__			=							max_position_embeddings
     A__			=							window_size
     A__			=							initializer_range
     A__			=							None
     A__			=							vocab_size - 1
     A__			=							vocab_size - 1
     A__			=							vocab_size - 1




    def 				__magic_name__				(       self		:			Optional[int] ) ->				Any:




     '''simple docstring'''

     A__			=							ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size )

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

     A__			=							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 				__magic_name__				(       self		:			Optional[Any] ) ->				List[str]:




     '''simple docstring'''

     A__			=							self.prepare_config_and_inputs()
     A__, A__, A__			=							config_and_inputs
     A__			=							{"input_ids": input_ids, "attention_mask": attention_mask}
     return config, inputs_dict




    def 				__magic_name__				(       self		:			Dict      ,					snake_case_		:			Any      ,					snake_case_		:			List[Any]      ,					snake_case_		:			Any      ,					snake_case_		:			Optional[Any] ) ->				int:




     '''simple docstring'''

     A__			=							20
     A__			=							model_class_name(snake_case_ )

     A__			=							model.init_cache(input_ids.shape[0]      ,					snake_case_ )
     A__			=							jnp.ones((input_ids.shape[0], max_decoder_length)      ,					dtype="i4" )

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

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

     A__			=							model(snake_case_ )

     A__			=							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 				__magic_name__				(       self		:			Any      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			int      ,					snake_case_		:			int      ,					snake_case_		:			List[Any] ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							20
     A__			=							model_class_name(snake_case_ )

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

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

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

     A__			=							model(snake_case_      ,					attention_mask=snake_case_ )

     A__			=							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_    (		A_,  A_,  unittest.TestCase				):
    lowercase__		   = (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
    lowercase__		   = (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							FlaxGPTNeoModelTester(self )




    def 				__magic_name__				(       self		:			Tuple ) ->				Dict:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__, A__, A__			=							self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(snake_case_      ,					snake_case_      ,					snake_case_      ,					snake_case_ )




    def 				__magic_name__				(       self		:			str ) ->				str:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__, A__, A__			=							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 				__magic_name__				(       self		:			List[str] ) ->				Tuple:




     '''simple docstring'''

     A__			=							GPTaTokenizer.from_pretrained("gpt2"      ,					pad_token="<|endoftext|>"      ,					padding_side="left" )
     A__			=							tokenizer(["Hello this is a long string", "Hey"]      ,					return_tensors="np"      ,					padding=snake_case_      ,					truncation=snake_case_ )

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

     A__			=							jax.jit(model.generate )

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

     A__			=							tokenizer.batch_decode(snake_case_      ,					skip_special_tokens=snake_case_ )

     A__			=							[
         "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 				__magic_name__				(       self		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

     A__, A__			=							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
       A__			=							self._prepare_for_class(snake_case_      ,					snake_case_ )
       A__			=							{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

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

       A__			=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,					snake_case_ )
       A__			=							fx_state

       with torch.no_grad():
        A__			=							pt_model(**snake_case_ ).to_tuple()

       A__			=							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_ )
        A__			=							model_class.from_pretrained(snake_case_      ,					from_pt=snake_case_ )

       A__			=							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 				__magic_name__				(       self		:			Union[str, Any] ) ->				List[Any]:




     '''simple docstring'''

     A__, A__			=							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
       A__			=							self._prepare_for_class(snake_case_      ,					snake_case_ )
       A__			=							{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

       A__			=							pt_model_class(snake_case_ ).eval()
       A__			=							model_class(snake_case_      ,					dtype=jnp.floataa )

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

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

       with torch.no_grad():
        A__			=							pt_model(**snake_case_ ).to_tuple()

       A__			=							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_ )
        A__			=							pt_model_class.from_pretrained(snake_case_      ,					from_flax=snake_case_ )

       with torch.no_grad():
        A__			=							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 				__magic_name__				(       self		:			List[str] ) ->				Optional[Any]:




     '''simple docstring'''

     for model_class_name in self.all_model_classes:
      A__			=							model_class_name.from_pretrained("EleutherAI/gpt-neo-125M" )
      A__			=							model(np.ones((1, 1) ) )
      self.assertIsNotNone(snake_case_ )











"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = 10_00		)		->						int:
 A__			=							0
 A__			=							0
 while 1:
  num += 3
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
 return total


if __name__ == "__main__":
      print(f'{solution() = }')





