




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:





     """simple docstring"""



     def __init__(   self			:							str    ,					A			:							Tuple    ,					A			:							str=14    ,					A			:							Optional[int]=7    ,					A			:							Any=True    ,					A			:							str=True    ,					A			:							Optional[Any]=False    ,					A			:							int=True    ,					A			:							List[Any]=99    ,					A			:							List[Any]=32    ,					A			:							int=4    ,					A			:							str=4    ,					A			:							str=[[["global", "local"], 2]]    ,					A			:							Optional[Any]=37    ,					A			:							int="gelu"    ,					A			:							Dict=0.1    ,					A			:							Optional[Any]=0.1    ,					A			:							Optional[Any]=5_12    ,					A			:							str=7    ,					A			:							str=0.0_2    ,					)      ->      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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Tuple:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      Union[str, Any]:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Any    ,					A			:							Optional[Any]    ,					A			:							Optional[Any]    ,					A			:							Dict     )      ->      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 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple    ,					A			:							List[str]    ,					A			:							str    ,					A			:							Optional[int]    ,					A			:							Dict     )      ->      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(		lowercase__							,						lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Tuple							=     (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
     __UpperCAmelCase						:			Dict							=     (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      str:






         """simple docstring"""
         a__  			=							FlaxGPTNeoModelTester(self     )

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






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[Any]:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Any:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[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__			,		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 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]     )      ->      str:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      List[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     )












import math


def   A		(__A  :       int			)  -> float:







     """simple docstring"""


     UpperCAmelCase_		=				math.log(2  ,							10			) * number
     UpperCAmelCase_		=				round((log_number - int(__A			))  ,							15			)

     return difference


def   A		(__A  :       int = 678910			)  -> int:







     """simple docstring"""


     UpperCAmelCase_		=				90
     UpperCAmelCase_		=				0

     UpperCAmelCase_		=				math.log(1.23  ,							10			)
     UpperCAmelCase_		=				math.log(1.24  ,							10			)
     UpperCAmelCase_		=				0

     while position < number:
          UpperCAmelCase_		=				log_difference(__A			)

          if difference >= upper_limit:
               power_iterator += 93

          elif difference < lower_limit:
               power_iterator += 196

          else:
               UpperCAmelCase_		=				power_iterator
               power_iterator += 196
               position += 1

     return previous_power


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(f"{solution() = }")






