






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 					snake_case_							:







   """simple docstring"""



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


       """simple docstring"""



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

   def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		List[Any]:


       """simple docstring"""



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

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

       _lowercase     =	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:	str	)  ->		Dict:


       """simple docstring"""



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

   def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       snake_case__:	List[Any]   ,       snake_case__:	List[str]   ,       snake_case__:	Tuple   ,       snake_case__:	Optional[int]	)  ->		Optional[int]:


       """simple docstring"""



       _lowercase     =	20
       _lowercase     =	model_class_name(snake_case__	)

       _lowercase     =	model.init_cache(input_ids.shape[0]   ,       snake_case__	)
       _lowercase     =	jnp.ones((input_ids.shape[0], max_decoder_length)   ,       dtype='i4'	)

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

       _lowercase     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,       dtype='i4'	)
       _lowercase     =	model(
           input_ids[:, -1:]   ,       attention_mask=snake_case__   ,       past_key_values=outputs_cache.past_key_values   ,       position_ids=snake_case__   ,       )

       _lowercase     =	model(snake_case__	)

       _lowercase     =	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   ,       snake_case__:	List[Any]   ,       snake_case__:	int   ,       snake_case__:	Tuple   ,       snake_case__:	Tuple	)  ->		int:


       """simple docstring"""



       _lowercase     =	20
       _lowercase     =	model_class_name(snake_case__	)

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

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

       _lowercase     =	model(
           input_ids[:, :-1]   ,       attention_mask=snake_case__   ,       past_key_values=snake_case__   ,       position_ids=snake_case__   ,       )
       _lowercase     =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,       dtype='i4'	)
       _lowercase     =	model(
           input_ids[:, -1:]   ,       past_key_values=outputs_cache.past_key_values   ,       attention_mask=snake_case__   ,       position_ids=snake_case__   ,       )

       _lowercase     =	model(snake_case__   ,       attention_mask=snake_case__	)

       _lowercase     =	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 					snake_case_							(       lowercase_      ,			lowercase_      ,			unittest.TestCase      ):







   """simple docstring"""



   __lowerCAmelCase      =						(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   __lowerCAmelCase      =						(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

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


       """simple docstring"""



       _lowercase     =	FlaxGPTNeoModelTester(self	)

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


       """simple docstring"""



       for model_class_name in self.all_model_classes:
           _lowercase ,    _lowercase ,    _lowercase     =	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:	Tuple	)  ->		Any:


       """simple docstring"""



       for model_class_name in self.all_model_classes:
           _lowercase ,    _lowercase ,    _lowercase     =	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:	str	)  ->		List[str]:


       """simple docstring"""



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

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

       _lowercase     =	jax.jit(model.generate	)

       _lowercase     =	jit_generate(
           inputs['input_ids']   ,       attention_mask=inputs['attention_mask']   ,       pad_token_id=tokenizer.pad_token_id	).sequences

       _lowercase     =	tokenizer.batch_decode(snake_case__   ,       skip_special_tokens=snake_case__	)

       _lowercase     =	[
           '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:	Any	)  ->		Union[str, Any]:


       """simple docstring"""



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

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

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

               _lowercase     =	convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,       snake_case__	)
               _lowercase     =	fx_state

               with torch.no_grad():
                   _lowercase     =	pt_model(**snake_case__	).to_tuple()

               _lowercase     =	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__	)
                   _lowercase     =	model_class.from_pretrained(snake_case__   ,       from_pt=snake_case__	)

               _lowercase     =	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]	)  ->		Optional[int]:


       """simple docstring"""



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

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

               _lowercase     =	pt_model_class(snake_case__	).eval()
               _lowercase     =	model_class(snake_case__   ,       dtype=jnp.floataa	)

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

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

               with torch.no_grad():
                   _lowercase     =	pt_model(**snake_case__	).to_tuple()

               _lowercase     =	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__	)
                   _lowercase     =	pt_model_class.from_pretrained(snake_case__   ,       from_flax=snake_case__	)

               with torch.no_grad():
                   _lowercase     =	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:	Optional[Any]	)  ->		Dict:


       """simple docstring"""



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








from __future__ import annotations
def        UpperCAmelCase      (   _lowercase,      _lowercase      ):


    '''simple docstring'''



    _lowercase     =	[None] * (len(_lowercase      ) + len(_lowercase      ))

    _lowercase     =	0  # pointer to current index for left Half
    _lowercase     =	0  # pointer to current index for the right Half
    _lowercase     =	0  # pointer to current index for the sorted array Half

    while pointera < len(_lowercase      ) and pointera < len(_lowercase      ):
        if left_half[pointera] < right_half[pointera]:
            _lowercase     =	left_half[pointera]
            pointera += 1
            index += 1
        else:
            _lowercase     =	right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(_lowercase      ):
        _lowercase     =	left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(_lowercase      ):
        _lowercase     =	right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array
def        UpperCAmelCase      (   _lowercase      ):


    '''simple docstring'''



    if len(_lowercase      ) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    _lowercase     =	0 + (len(_lowercase      ) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    _lowercase     =	array[:middle]
    _lowercase     =	array[middle:]

    return merge(merge_sort(_lowercase      ),      merge_sort(_lowercase      )      )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
