






'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch
class 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     List[Any]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Optional[Any]=14				,     __lowerCamelCase     :     int=7				,     __lowerCamelCase     :     Tuple=True				,     __lowerCamelCase     :     Union[str, Any]=True				,     __lowerCamelCase     :     Union[str, Any]=False				,     __lowerCamelCase     :     Optional[int]=True				,     __lowerCamelCase     :     Optional[int]=99				,     __lowerCamelCase     :     Dict=32				,     __lowerCamelCase     :     Optional[int]=4				,     __lowerCamelCase     :     Union[str, Any]=4				,     __lowerCamelCase     :     Optional[int]=4				,     __lowerCamelCase     :     Union[str, Any]=37				,     __lowerCamelCase     :     Tuple="gelu"				,     __lowerCamelCase     :     Optional[int]=0.1				,     __lowerCamelCase     :     Optional[Any]=0.1				,     __lowerCamelCase     :     Tuple=512				,     __lowerCamelCase     :     Optional[int]=0.0_2				,     )							->							List[Any]:

     '''simple docstring'''




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

    def    a__				(					self     :     str     )							->							Union[str, Any]:

     '''simple docstring'''




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

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

     lowerCamelCase__  =     GPTJConfig(
         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=__lowerCamelCase				,     bos_token_id=self.bos_token_id				,     eos_token_id=self.eos_token_id				,     pad_token_id=self.pad_token_id				,     rotary_dim=self.rotary_dim				,     )

     return (config, input_ids, input_mask)

    def    a__				(					self     :     Optional[int]     )							->							List[str]:

     '''simple docstring'''




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

    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     List[str]     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     20
     lowerCamelCase__  =     model_class_name(__lowerCamelCase     )

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

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

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

     lowerCamelCase__  =     model(__lowerCamelCase     )

     lowerCamelCase__  =     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    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     int     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     20
     lowerCamelCase__  =     model_class_name(__lowerCamelCase     )

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

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

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

     lowerCamelCase__  =     model(__lowerCamelCase				,     attention_mask=__lowerCamelCase     )

     lowerCamelCase__  =     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      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    lowerCAmelCase__      				=							(FlaxGPTJForCausalLM,) if is_flax_available() else ()

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

     '''simple docstring'''




     lowerCamelCase__  =     FlaxGPTJModelTester(self     )

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

     '''simple docstring'''




     for model_class_name in self.all_model_classes:
      lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )

    def    a__				(					self     :     List[Any]     )							->							str:

     '''simple docstring'''




     for model_class_name in self.all_model_classes:
      lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )

    @tooslow
    def    a__				(					self     :     List[str]     )							->							Optional[Any]:

     '''simple docstring'''




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

     lowerCamelCase__  =     FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"     )
     lowerCamelCase__  =     False
     lowerCamelCase__  =     model.config.eos_token_id

     lowerCamelCase__  =     jax.jit(model.generate     )

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

     lowerCamelCase__  =     tokenizer.batch_decode(__lowerCamelCase				,     skip_special_tokens=__lowerCamelCase     )

     lowerCamelCase__  =     [
         "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    a__				(					self     :     List[Any]     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     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__  =     self._prepare_for_class(__lowerCamelCase				,     __lowerCamelCase     )
       lowerCamelCase__  =     {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

       lowerCamelCase__  =     convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,     __lowerCamelCase     )
       lowerCamelCase__  =     fx_state

       with torch.no_grad():
        lowerCamelCase__  =     pt_model(**__lowerCamelCase     ).to_tuple()

       lowerCamelCase__  =     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     )
        lowerCamelCase__  =     model_class.from_pretrained(__lowerCamelCase				,     from_pt=__lowerCamelCase     )

       lowerCamelCase__  =     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    a__				(					self     :     Dict     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  =     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__  =     self._prepare_for_class(__lowerCamelCase				,     __lowerCamelCase     )
       lowerCamelCase__  =     {k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

       lowerCamelCase__  =     pt_model_class(__lowerCamelCase     ).eval()
       lowerCamelCase__  =     model_class(__lowerCamelCase				,     dtype=jnp.floataa     )

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

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

       with torch.no_grad():
        lowerCamelCase__  =     pt_model(**__lowerCamelCase     ).to_tuple()

       lowerCamelCase__  =     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     )
        lowerCamelCase__  =     pt_model_class.from_pretrained(__lowerCamelCase				,     from_flax=__lowerCamelCase     )

       with torch.no_grad():
        lowerCamelCase__  =     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     )




    @tooslow
    def    a__				(					self     :     Optional[Any]     )							->							Tuple:

     '''simple docstring'''




     for model_class_name in self.all_model_classes:
      lowerCamelCase__  =     model_class_name.from_pretrained("EleutherAI/gpt-j-6B"     )
      lowerCamelCase__  =     model(np.ones((1, 1)     )     )
      self.assertIsNotNone(__lowerCamelCase     )









'''simple docstring'''





from decimal import Decimal, getcontext
from math import ceil, factorial






def    lowerCamelCase_     (    lowercase__):

 if not isinstance(lowercase__				,     lowercase__):
  raise TypeError("Undefined for non-integers")
 elif precision < 1:
  raise ValueError("Undefined for non-natural numbers")

 lowerCamelCase__  =     precision
 lowerCamelCase__  =     ceil(precision / 14)
 lowerCamelCase__  =     426880 * Decimal(10005).sqrt()
 lowerCamelCase__  =     1
 lowerCamelCase__  =     13591409
 lowerCamelCase__  =     Decimal(lowercase__)
 for k in range(1				,     lowercase__):
  lowerCamelCase__  =     factorial(6 * k) // (factorial(3 * k) * factorial(lowercase__) ** 3)
  linear_term += 545140134
  exponential_term *= -262537412640768000
  partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
 return str(constant_term / partial_sum)[:-1]


if __name__ == "__main__":
 __A							:							Dict  							=		50
 print(F"""The first {n} digits of pi is: {pi(n)}""")

