





'''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 				lowerCAmelCase_						:




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=14     ,       _lowerCAmelCase=7     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=99     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=4     ,       _lowerCAmelCase=4     ,       _lowerCAmelCase=[[["global", "local"], 2]]     ,       _lowerCAmelCase=37     ,       _lowerCAmelCase="gelu"     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=512     ,       _lowerCAmelCase=7     ,       _lowerCAmelCase=0.02     ,       )      ->							Dict:
     _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		     = num_hidden_layers
     _lowerCAmelCase		     = num_attention_heads
     _lowerCAmelCase		     = attention_types
     _lowerCAmelCase		     = intermediate_size
     _lowerCAmelCase		     = hidden_act
     _lowerCAmelCase		     = hidden_dropout_prob
     _lowerCAmelCase		     = attention_probs_dropout_prob
     _lowerCAmelCase		     = max_position_embeddings
     _lowerCAmelCase		     = window_size
     _lowerCAmelCase		     = initializer_range
     _lowerCAmelCase		     = None
     _lowerCAmelCase		     = vocab_size - 1
     _lowerCAmelCase		     = vocab_size - 1
     _lowerCAmelCase		     = vocab_size - 1




 def 							_snake_case				(		self						)      ->							str:
     _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		     = 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 							_snake_case				(		self						)      ->							Any:
     _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 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Dict:
     _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 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Union[str, Any]:
     _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 				lowerCAmelCase_						(						__magic_name__  ,__magic_name__  ,unittest.TestCase						):
 __lowerCamelCase  :							Any    					=   (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
 __lowerCamelCase  :							List[str]    					=   (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




 def 							_snake_case				(		self						)      ->							Optional[int]:
     _lowerCAmelCase		     = FlaxGPTNeoModelTester(self						)




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




 @slow
 def 							_snake_case				(		self						)      ->							int:
     _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		     = FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"						)
     _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 							_snake_case				(		self						)      ->							int:
     _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 							_snake_case				(		self						)      ->							Optional[Any]:
     _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						)





 @slow
 def 							_snake_case				(		self						)      ->							Optional[int]:
     for model_class_name in self.all_model_classes:
         _lowerCAmelCase		     = model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"						)
         _lowerCAmelCase		     = model(np.ones((1, 1)						)						)
         self.assertIsNotNone(_lowerCAmelCase						)









'''simple docstring'''


import math


def 		__a(SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    if number == 0:
        _lowerCAmelCase		     = "Zero"
        return words
    else:
        _lowerCAmelCase		     = math.logaa(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = digits + 1
        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "One"
        _lowerCAmelCase		     = "Two"
        _lowerCAmelCase		     = "Three"
        _lowerCAmelCase		     = "Four"
        _lowerCAmelCase		     = "Five"
        _lowerCAmelCase		     = "Six"
        _lowerCAmelCase		     = "Seven"
        _lowerCAmelCase		     = "Eight"
        _lowerCAmelCase		     = "Nine"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "Twenty"
        _lowerCAmelCase		     = "Thirty"
        _lowerCAmelCase		     = "Forty"
        _lowerCAmelCase		     = "Fifty"
        _lowerCAmelCase		     = "Sixty"
        _lowerCAmelCase		     = "Seventy"
        _lowerCAmelCase		     = "Eighty"
        _lowerCAmelCase		     = "Ninety"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Ten"
        _lowerCAmelCase		     = "Eleven"
        _lowerCAmelCase		     = "Twelve"
        _lowerCAmelCase		     = "Thirteen"
        _lowerCAmelCase		     = "Fourteen"
        _lowerCAmelCase		     = "Fifteen"
        _lowerCAmelCase		     = "Sixteen"
        _lowerCAmelCase		     = "Seventeen"
        _lowerCAmelCase		     = "Eighteen"
        _lowerCAmelCase		     = "Nineteen"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Hundred,"
        _lowerCAmelCase		     = "Thousand,"
        _lowerCAmelCase		     = "Lakh,"
        _lowerCAmelCase		     = "Crore,"

        _lowerCAmelCase		     = number
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = 0
        _lowerCAmelCase		     = int(SCREAMING_SNAKE_CASE_      )
        while counter < digits:
            _lowerCAmelCase		     = temp_num % 10
            if counter % 2 == 0:
                _lowerCAmelCase		     = ""
                if counter in placevalue and current != 0:
                    _lowerCAmelCase		     = placevalue[counter]
                if counter == 2:
                    _lowerCAmelCase		     = singles[current] + addition + words
                elif counter == 0:
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words

                else:
                    _lowerCAmelCase		     = doubles[current] + addition + words

            else:
                if counter == 1:
                    if current == 1:
                        _lowerCAmelCase		     = teens[number % 10] + words
                    else:
                        _lowerCAmelCase		     = ""
                        if counter in placevalue:
                            _lowerCAmelCase		     = placevalue[counter]
                        _lowerCAmelCase		     = doubles[current] + addition + words
                else:
                    _lowerCAmelCase		     = ""
                    if counter in placevalue:
                        if current == 0 and ((temp_num % 100) // 10) == 0:
                            _lowerCAmelCase		     = ""
                        else:
                            _lowerCAmelCase		     = placevalue[counter]
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words
            counter += 1
            _lowerCAmelCase		     = temp_num // 10
    return words


if __name__ == "__main__":
       import doctest

       doctest.testmod()


