






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    a   :



      """simple docstring"""


      def __init__(				self :      Tuple		,				__lowercase :      Tuple		,				__lowercase :      Tuple=14		,				__lowercase :      int=7		,				__lowercase :      Optional[Any]=True		,				__lowercase :      List[str]=True		,				__lowercase :      int=False		,				__lowercase :      List[str]=True		,				__lowercase :      List[str]=99		,				__lowercase :      Any=32		,				__lowercase :      List[str]=4		,				__lowercase :      Any=4		,				__lowercase :      int=[[["global", "local"], 2]]		,				__lowercase :      List[Any]=37		,				__lowercase :      List[str]="gelu"		,				__lowercase :      Any=0.1		,				__lowercase :      Tuple=0.1		,				__lowercase :      Tuple=512		,				__lowercase :      Tuple=7		,				__lowercase :      Any=0.02		,				)							->				Dict:
        __UpperCAmelCase							:     Union[str, Any]									=  parent
        __UpperCAmelCase							:     Dict									=  batch_size
        __UpperCAmelCase							:     Optional[int]									=  seq_length
        __UpperCAmelCase							:     Optional[int]									=  is_training
        __UpperCAmelCase							:     Optional[int]									=  use_input_mask
        __UpperCAmelCase							:     int									=  use_token_type_ids
        __UpperCAmelCase							:     Optional[int]									=  use_labels
        __UpperCAmelCase							:     Dict									=  vocab_size
        __UpperCAmelCase							:     Any									=  hidden_size
        __UpperCAmelCase							:     Optional[Any]									=  num_hidden_layers
        __UpperCAmelCase							:     List[str]									=  num_attention_heads
        __UpperCAmelCase							:     Optional[int]									=  attention_types
        __UpperCAmelCase							:     List[str]									=  intermediate_size
        __UpperCAmelCase							:     Optional[int]									=  hidden_act
        __UpperCAmelCase							:     Optional[int]									=  hidden_dropout_prob
        __UpperCAmelCase							:     List[Any]									=  attention_probs_dropout_prob
        __UpperCAmelCase							:     List[Any]									=  max_position_embeddings
        __UpperCAmelCase							:     Dict									=  window_size
        __UpperCAmelCase							:     Dict									=  initializer_range
        __UpperCAmelCase							:     List[str]									=  None
        __UpperCAmelCase							:     List[str]									=  vocab_size - 1
        __UpperCAmelCase							:     List[str]									=  vocab_size - 1
        __UpperCAmelCase							:     List[str]									=  vocab_size - 1
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				Any:
        __UpperCAmelCase							:     Tuple									=  ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size )

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

        __UpperCAmelCase							:     Union[str, Any]									=  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=lowercase_		,				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 :      Dict )							->				Union[str, Any]:
        __UpperCAmelCase							:     Union[str, Any]									=  self.prepare_config_and_inputs()
        __UpperCAmelCase							:     int									=  config_and_inputs
        __UpperCAmelCase							:     List[str]									=  {'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict
      def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      Any		,				__lowercase :      Optional[int]		,				__lowercase :      List[str]		,				__lowercase :      Optional[Any] )							->				Optional[int]:
        __UpperCAmelCase							:     List[Any]									=  20
        __UpperCAmelCase							:     int									=  model_class_name(lowercase_ )

        __UpperCAmelCase							:     Dict									=  model.init_cache(input_ids.shape[0]		,				lowercase_ )
        __UpperCAmelCase							:     str									=  jnp.ones((input_ids.shape[0], max_decoder_length)		,				dtype="""i4""" )

        __UpperCAmelCase							:     int									=  jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,				(input_ids.shape[0], input_ids.shape[-1] - 1) )
        __UpperCAmelCase							:     Any									=  model(
            input_ids[:, :-1]		,				attention_mask=lowercase_		,				past_key_values=lowercase_		,				position_ids=lowercase_		,				)

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

        __UpperCAmelCase							:     List[Any]									=  model(lowercase_ )

        __UpperCAmelCase							:     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}""" )




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

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

        __UpperCAmelCase							:     List[Any]									=  model.init_cache(input_ids.shape[0]		,				lowercase_ )
        __UpperCAmelCase							:     Tuple									=  jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1 )[None, :]		,				(input_ids.shape[0], input_ids.shape[-1] - 1) )

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

        __UpperCAmelCase							:     str									=  model(lowercase_		,				attention_mask=lowercase_ )

        __UpperCAmelCase							:     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}""" )


@require_flax
class    a   (			UpperCamelCase_							,	UpperCamelCase_							,	unittest.TestCase       ):



      """simple docstring"""


      a  :			str								=     (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      a  :			Union[str, Any]								=     (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
      def      UpperCAmelCase				(				self :      Any )							->				Any:
        __UpperCAmelCase							:     Dict									=  FlaxGPTNeoModelTester(self )
      def      UpperCAmelCase				(				self :      List[str] )							->				int:




        for model_class_name in self.all_model_classes:
          __UpperCAmelCase							:     Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowercase_		,				lowercase_		,				lowercase_		,				lowercase_ )
      def      UpperCAmelCase				(				self :      int )							->				Dict:




        for model_class_name in self.all_model_classes:
          __UpperCAmelCase							:     Optional[int]									=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowercase_		,				lowercase_		,				lowercase_		,				lowercase_ )
      @slow
      def      UpperCAmelCase				(				self :      Tuple )							->				Any:
        __UpperCAmelCase							:     Dict									=  GPTaTokenizer.from_pretrained("""gpt2"""		,				pad_token="""<|endoftext|>"""		,				padding_side="""left""" )
        __UpperCAmelCase							:     Optional[int]									=  tokenizer(["""Hello this is a long string""", """Hey"""]		,				return_tensors="""np"""		,				padding=lowercase_		,				truncation=lowercase_ )

        __UpperCAmelCase							:     Tuple									=  FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M""" )
        __UpperCAmelCase							:     Optional[int]									=  False
        __UpperCAmelCase							:     str									=  model.config.eos_token_id

        __UpperCAmelCase							:     Union[str, Any]									=  jax.jit(model.generate )

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

        __UpperCAmelCase							:     Union[str, Any]									=  tokenizer.batch_decode(lowercase_		,				skip_special_tokens=lowercase_ )

        __UpperCAmelCase							:     List[str]									=  [
            '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(lowercase_		,				lowercase_ )
      @is_pt_flax_cross_test
      def      UpperCAmelCase				(				self :      Optional[int] )							->				List[Any]:
        __UpperCAmelCase							:     Dict									=  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							:     str									=  self._prepare_for_class(lowercase_		,				lowercase_ )
            __UpperCAmelCase							:     Union[str, Any]									=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

            # load corresponding PyTorch class
            __UpperCAmelCase							:     List[Any]									=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
            __UpperCAmelCase							:     Union[str, Any]									=  getattr(lowercase_		,				lowercase_ )

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

            __UpperCAmelCase							:     Optional[Any]									=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,				lowercase_ )
            __UpperCAmelCase							:     Tuple									=  fx_state

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

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

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

            __UpperCAmelCase							:     Dict									=  fx_model_loaded(**lowercase_ ).to_tuple()
            self.assertEqual(
                len(lowercase_ )		,				len(lowercase_ )		,				"""Output lengths differ between Flax and PyTorch""" )
            for fx_output_loaded, pt_output in zip(lowercase_		,				lowercase_ ):
              self.assert_almost_equals(fx_output_loaded[:, -1]		,				pt_output[:, -1].numpy()		,				4e-2 )
      @is_pt_flax_cross_test
      def      UpperCAmelCase				(				self :      List[str] )							->				Union[str, Any]:
        __UpperCAmelCase							:     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
            __UpperCAmelCase							:     Optional[int]									=  self._prepare_for_class(lowercase_		,				lowercase_ )
            __UpperCAmelCase							:     Tuple									=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

            # load corresponding PyTorch class
            __UpperCAmelCase							:     Dict									=  model_class.__name__[4:]  # Skip the "Flax" at the beginning
            __UpperCAmelCase							:     int									=  getattr(lowercase_		,				lowercase_ )

            __UpperCAmelCase							:     List[Any]									=  pt_model_class(lowercase_ ).eval()
            __UpperCAmelCase							:     List[str]									=  model_class(lowercase_		,				dtype=jnp.floataa )

            __UpperCAmelCase							:     List[str]									=  load_flax_weights_in_pytorch_model(lowercase_		,				fx_model.params )
            __UpperCAmelCase							:     Any									=  pt_inputs['input_ids'].shape
            __UpperCAmelCase							:     Dict									=  np.random.randint(0		,				seq_length - 1		,				size=(batch_size,) )
            for batch_idx, start_index in enumerate(lowercase_ ):
              __UpperCAmelCase							:     List[str]									=  0
              __UpperCAmelCase							:     str									=  1
              __UpperCAmelCase							:     List[Any]									=  0
              __UpperCAmelCase							:     Optional[Any]									=  1

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

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

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

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

            with torch.no_grad():
              __UpperCAmelCase							:     List[Any]									=  pt_model_loaded(**lowercase_ ).to_tuple()

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




      @slow
      def      UpperCAmelCase				(				self :      List[str] )							->				Any:




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










def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(a + 1  ,		400		):
      for c in range(b + 1  ,		500		):
        if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
          return a * b * c

  return -1






def     lowerCamelCase__				(   ):

  for a in range(300		):
    for b in range(400		):
      __UpperCAmelCase							:     Optional[Any]									=  1000 - a - b
      if a < b < c and (a**2) + (b**2) == (c**2):
        return a * b * c

  return -1






def     lowerCamelCase__				(   ):
  import timeit

  print(
      timeit.timeit("""solution()"""  ,		setup="""from __main__ import solution"""  ,		number=1000		)		)
  print(
      timeit.timeit(
          """solution_fast()"""  ,		setup="""from __main__ import solution_fast"""  ,		number=1000		)		)


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


