

"""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     _UpperCAmelCase			:
			def __init__(		self      ,		_A      ,		_A=14      ,		_A=7      ,		_A=True      ,		_A=True      ,		_A=False      ,		_A=True      ,		_A=99      ,		_A=32      ,		_A=4      ,		_A=4      ,		_A=[[["global", "local"], 2]]      ,		_A=37      ,		_A="gelu"      ,		_A=0.1      ,		_A=0.1      ,		_A=5_12      ,		_A=7      ,		_A=0.02      ,		)    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							str									=					parent
				_UpperCAmelCase    :							Dict									=					batch_size
				_UpperCAmelCase    :							Optional[Any]									=					seq_length
				_UpperCAmelCase    :							Tuple									=					is_training
				_UpperCAmelCase    :							Dict									=					use_input_mask
				_UpperCAmelCase    :							Dict									=					use_token_type_ids
				_UpperCAmelCase    :							Optional[int]									=					use_labels
				_UpperCAmelCase    :							List[Any]									=					vocab_size
				_UpperCAmelCase    :							Optional[int]									=					hidden_size
				_UpperCAmelCase    :							Union[str, Any]									=					num_hidden_layers
				_UpperCAmelCase    :							Tuple									=					num_attention_heads
				_UpperCAmelCase    :							Union[str, Any]									=					attention_types
				_UpperCAmelCase    :							List[Any]									=					intermediate_size
				_UpperCAmelCase    :							List[str]									=					hidden_act
				_UpperCAmelCase    :							Optional[int]									=					hidden_dropout_prob
				_UpperCAmelCase    :							Optional[Any]									=					attention_probs_dropout_prob
				_UpperCAmelCase    :							List[str]									=					max_position_embeddings
				_UpperCAmelCase    :							Union[str, Any]									=					window_size
				_UpperCAmelCase    :							Optional[Any]									=					initializer_range
				_UpperCAmelCase    :							Union[str, Any]									=					None
				_UpperCAmelCase    :							str									=					vocab_size - 1
				_UpperCAmelCase    :							Any									=					vocab_size - 1
				_UpperCAmelCase    :							List[str]									=					vocab_size - 1
			def 							__snake_case (		self     )    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							Any									=					ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size     )

				_UpperCAmelCase    :							List[str]									=					None
				if self.use_input_mask:
					_UpperCAmelCase    :							Optional[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=_A      ,		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     )    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					self.prepare_config_and_inputs()
				_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							Tuple									=					config_and_inputs
				_UpperCAmelCase    :							Any									=					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Dict									=					20
				_UpperCAmelCase    :							List[str]									=					model_class_name(_A     )

				_UpperCAmelCase    :							int									=					model.init_cache(input_ids.shape[0]      ,		_A     )
				_UpperCAmelCase    :							List[str]									=					jnp.ones((input_ids.shape[0], max_decoder_length)      ,		dtype="""i4"""     )

				_UpperCAmelCase    :							Union[str, Any]									=					jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1     )[None, :]      ,		(input_ids.shape[0], input_ids.shape[-1] - 1)     )
				_UpperCAmelCase    :							int									=					model(
				    input_ids[:, :-1]      ,		attention_mask=_A      ,		past_key_values=_A      ,		position_ids=_A      ,		)

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

				_UpperCAmelCase    :							Tuple									=					model(_A     )

				_UpperCAmelCase    :							int									=					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      ,		_A      ,		_A      ,		_A      ,		_A     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					20
				_UpperCAmelCase    :							List[Any]									=					model_class_name(_A     )

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

				_UpperCAmelCase    :							Dict									=					model.init_cache(input_ids.shape[0]      ,		_A     )
				_UpperCAmelCase    :							str									=					jnp.broadcast_to(
				    jnp.arange(input_ids.shape[-1] - 1     )[None, :]      ,		(input_ids.shape[0], input_ids.shape[-1] - 1)     )

				_UpperCAmelCase    :							Tuple									=					model(
				    input_ids[:, :-1]      ,		attention_mask=_A      ,		past_key_values=_A      ,		position_ids=_A      ,		)
				_UpperCAmelCase    :							Optional[int]									=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,		dtype="""i4"""     )
				_UpperCAmelCase    :							str									=					model(
				    input_ids[:, -1:]      ,		past_key_values=outputs_cache.past_key_values      ,		attention_mask=_A      ,		position_ids=_A      ,		)

				_UpperCAmelCase    :							Optional[Any]									=					model(_A      ,		attention_mask=_A     )

				_UpperCAmelCase    :							int									=					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			(		__a    ,     __a    ,     unittest.TestCase):
			__a :       Union[str, Any]									=							(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
			__a :       Dict									=							(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()
			def 							__snake_case (		self     )    ->    Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					FlaxGPTNeoModelTester(self     )
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
					_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							List[Any]									=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward(_A      ,		_A      ,		_A      ,		_A     )
			def 							__snake_case (		self     )    ->    Union[str, Any]:






				'''simple docstring'''







				for model_class_name in self.all_model_classes:
					_UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase    :							Optional[Any]									=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward_with_attn_mask(
					    _A      ,		_A      ,		_A      ,		_A     )
			@slow
			def 							__snake_case (		self     )    ->    List[Any]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[Any]									=					GPTaTokenizer.from_pretrained("""gpt2"""      ,		pad_token="""<|endoftext|>"""      ,		padding_side="""left"""     )
				_UpperCAmelCase    :							Tuple									=					tokenizer(["""Hello this is a long string""", """Hey"""]      ,		return_tensors="""np"""      ,		padding=_A      ,		truncation=_A     )

				_UpperCAmelCase    :							List[Any]									=					FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""     )
				_UpperCAmelCase    :							List[str]									=					False
				_UpperCAmelCase    :							Optional[Any]									=					model.config.eos_token_id

				_UpperCAmelCase    :							Optional[Any]									=					jax.jit(model.generate     )

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

				_UpperCAmelCase    :							str									=					tokenizer.batch_decode(_A      ,		skip_special_tokens=_A     )

				_UpperCAmelCase    :							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(_A      ,		_A     )
			@is_pt_flax_cross_test
			def 							__snake_case (		self     )    ->    Optional[Any]:






				'''simple docstring'''







				_UpperCAmelCase						,   _UpperCAmelCase    :							List[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    :							List[str]									=					self._prepare_for_class(_A      ,		_A     )
						_UpperCAmelCase    :							List[Any]									=					{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    :							str									=					getattr(_A      ,		_A     )

						_UpperCAmelCase						,   _UpperCAmelCase    :							Tuple									=					pt_inputs["""input_ids"""].shape
						_UpperCAmelCase    :							int									=					np.random.randint(0      ,		seq_length - 1      ,		size=(batch_size,)     )
						for batch_idx, start_index in enumerate(_A     ):
							_UpperCAmelCase    :							Any									=					0
							_UpperCAmelCase    :							List[str]									=					1
							_UpperCAmelCase    :							int									=					0
							_UpperCAmelCase    :							Union[str, Any]									=					1
						_UpperCAmelCase    :							List[Any]									=					pt_model_class(_A     ).eval()
						_UpperCAmelCase    :							int									=					model_class(_A      ,		dtype=jnp.floataa     )

						_UpperCAmelCase    :							Any									=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,		_A     )
						_UpperCAmelCase    :							List[Any]									=					fx_state

						with torch.no_grad():
							_UpperCAmelCase    :							int									=					pt_model(**_A     ).to_tuple()

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

						with tempfile.TemporaryDirectory() as tmpdirname:
							pt_model.save_pretrained(_A     )
							_UpperCAmelCase    :							List[Any]									=					model_class.from_pretrained(_A      ,		from_pt=_A     )

						_UpperCAmelCase    :							Union[str, Any]									=					fx_model_loaded(**_A     ).to_tuple()
						self.assertEqual(
						    len(_A     )      ,		len(_A     )      ,		"""Output lengths differ between Flax and PyTorch"""     )
						for fx_output_loaded, pt_output in zip(_A      ,		_A     ):
							self.assert_almost_equals(fx_output_loaded[:, -1]      ,		pt_output[:, -1].numpy()      ,		4e-2     )
			@is_pt_flax_cross_test
			def 							__snake_case (		self     )    ->    Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase						,   _UpperCAmelCase    :							List[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    :							Union[str, Any]									=					self._prepare_for_class(_A      ,		_A     )
						_UpperCAmelCase    :							List[str]									=					{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

						_UpperCAmelCase    :							Any									=					pt_model_class(_A     ).eval()
						_UpperCAmelCase    :							Optional[Any]									=					model_class(_A      ,		dtype=jnp.floataa     )

						_UpperCAmelCase    :							Union[str, Any]									=					load_flax_weights_in_pytorch_model(_A      ,		fx_model.params     )
						_UpperCAmelCase						,   _UpperCAmelCase    :							Optional[Any]									=					pt_inputs["""input_ids"""].shape
						_UpperCAmelCase    :							Optional[Any]									=					np.random.randint(0      ,		seq_length - 1      ,		size=(batch_size,)     )
						for batch_idx, start_index in enumerate(_A     ):
							_UpperCAmelCase    :							Dict									=					0
							_UpperCAmelCase    :							Optional[Any]									=					1
							_UpperCAmelCase    :							Tuple									=					0
							_UpperCAmelCase    :							Tuple									=					1

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

						with torch.no_grad():
							_UpperCAmelCase    :							Union[str, Any]									=					pt_model(**_A     ).to_tuple()

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

						with tempfile.TemporaryDirectory() as tmpdirname:
							fx_model.save_pretrained(_A     )
							_UpperCAmelCase    :							Optional[Any]									=					pt_model_class.from_pretrained(_A      ,		from_flax=_A     )

						with torch.no_grad():
							_UpperCAmelCase    :							Tuple									=					pt_model_loaded(**_A     ).to_tuple()

						self.assertEqual(
						    len(_A     )      ,		len(_A     )      ,		"""Output lengths differ between Flax and PyTorch"""     )
						for fx_output, pt_output in zip(_A      ,		_A     ):
							self.assert_almost_equals(fx_output[:, -1]      ,		pt_output[:, -1].numpy()      ,		4e-2     )
			@slow
			def 							__snake_case (		self     )    ->    int:






				'''simple docstring'''







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








"""simple docstring"""






from __future__ import annotations
def       UpperCamelCase					(			_lowerCAmelCase     :						list,  _lowerCAmelCase     :						list )				->			list:
	_UpperCAmelCase    :							List[Any]									=					[None] * (len(_lowerCAmelCase ) + len(_lowerCAmelCase ))

	_UpperCAmelCase    :							Union[str, Any]									=					0  # pointer to current index for left Half
	_UpperCAmelCase    :							str									=					0  # pointer to current index for the right Half
	_UpperCAmelCase    :							Optional[int]									=					0  # pointer to current index for the sorted array Half

	while pointera < len(_lowerCAmelCase ) and pointera < len(_lowerCAmelCase ):
		if left_half[pointera] < right_half[pointera]:
			_UpperCAmelCase    :							Tuple									=					left_half[pointera]
			pointera += 1
			index += 1
		else:
			_UpperCAmelCase    :							int									=					right_half[pointera]
			pointera += 1
			index += 1
	while pointera < len(_lowerCAmelCase ):
		_UpperCAmelCase    :							Tuple									=					left_half[pointera]
		pointera += 1
		index += 1

	while pointera < len(_lowerCAmelCase ):
		_UpperCAmelCase    :							Union[str, Any]									=					right_half[pointera]
		pointera += 1
		index += 1

	return sorted_array
def       UpperCamelCase					(			_lowerCAmelCase     :						list )				->			list:
	if len(_lowerCAmelCase ) <= 1:
		return array
	# the actual formula to calculate the middle element = left + (right - left) // 2
	# this avoids integer overflow in case of large N
	_UpperCAmelCase    :							Any									=					0 + (len(_lowerCAmelCase ) - 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
	_UpperCAmelCase    :							Optional[Any]									=					array[:middle]
	_UpperCAmelCase    :							Optional[int]									=					array[middle:]

	return merge(merge_sort(_lowerCAmelCase ),  merge_sort(_lowerCAmelCase ) )


if __name__ == "__main__":
						import doctest

						doctest.testmod()





