




'''simple docstring'''


import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
					from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
					from tensorflow.keras.optimizers import Adam




class     a			(				tf.keras.optimizers.schedules.LearningRateSchedule       ):






						"""simple docstring"""




						def __init__(							self					:      int			,			snake_case_					:      float			,			snake_case_					:      Callable			,			snake_case_					:      int			,			snake_case_					:      float = 1.0			,			snake_case_					:      str = None			,			):



								'''simple docstring'''



								super().__init__()
								snake_case__						:       Any       						=  initial_learning_rate
								snake_case__						:       str       						=  warmup_steps
								snake_case__						:       Dict       						=  power
								snake_case__						:       Optional[int]       						=  decay_schedule_fn
								snake_case__						:       str       						=  name







						def __call__(							self					:      Any			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								with tf.name_scope(self.name or '''WarmUp'''  ) as name:
										# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
										# learning rate will be `global_step/num_warmup_steps * init_lr`.
										snake_case__						:       Tuple       						=  tf.cast(snake_case_			,			tf.floataa  )
										snake_case__						:       List[str]       						=  tf.cast(self.warmup_steps			,			tf.floataa  )
										snake_case__						:       int       						=  global_step_float / warmup_steps_float
										snake_case__						:       Dict       						=  self.initial_learning_rate * tf.math.pow(snake_case_			,			self.power  )
										return tf.cond(
										    global_step_float < warmup_steps_float			,			lambda: warmup_learning_rate			,			lambda: self.decay_schedule_fn(step - self.warmup_steps  )			,			name=snake_case_			,			)







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								return {
								    "initial_learning_rate": self.initial_learning_rate,
								    "decay_schedule_fn": self.decay_schedule_fn,
								    "warmup_steps": self.warmup_steps,
								    "power": self.power,
								    "name": self.name,
								}





def 	_a       (				__lowerCAmelCase		:      float   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      float = 0.0   ,							__lowerCAmelCase		:      float = 0.9   ,							__lowerCAmelCase		:      float = 0.999   ,							__lowerCAmelCase		:      float = 1e-8   ,							__lowerCAmelCase		:      Optional[float] = None   ,							__lowerCAmelCase		:      Optional[float] = None   ,							__lowerCAmelCase		:      float = 0.0   ,							__lowerCAmelCase		:      float = 1.0   ,							__lowerCAmelCase		:      Optional[List[str]] = None   ,							):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  tf.keras.optimizers.schedules.PolynomialDecay(
		    initial_learning_rate=__lowerCAmelCase   ,							decay_steps=num_train_steps - num_warmup_steps   ,							end_learning_rate=init_lr * min_lr_ratio   ,							power=__lowerCAmelCase   ,							)
		if num_warmup_steps:
				snake_case__						:       Optional[int]       						=  WarmUp(
				    initial_learning_rate=__lowerCAmelCase   ,							decay_schedule_fn=__lowerCAmelCase   ,							warmup_steps=__lowerCAmelCase   ,							)
		if weight_decay_rate > 0.0:
				snake_case__						:       str       						=  AdamWeightDecay(
				    learning_rate=__lowerCAmelCase   ,							weight_decay_rate=__lowerCAmelCase   ,							beta_a=__lowerCAmelCase   ,							beta_a=__lowerCAmelCase   ,							epsilon=__lowerCAmelCase   ,							clipnorm=__lowerCAmelCase   ,							global_clipnorm=__lowerCAmelCase   ,							exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']   ,							include_in_weight_decay=__lowerCAmelCase   ,							)
		else:
				snake_case__						:       List[str]       						=  tf.keras.optimizers.Adam(
				    learning_rate=__lowerCAmelCase   ,							beta_a=__lowerCAmelCase   ,							beta_a=__lowerCAmelCase   ,							epsilon=__lowerCAmelCase   ,							clipnorm=__lowerCAmelCase   ,							global_clipnorm=__lowerCAmelCase   ,							)
		# We return the optimizer and the LR scheduler in order to better track the
		# evolution of the LR independently of the optimizer.
		return optimizer, lr_schedule




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Tuple			,			snake_case_					:      Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1			,			snake_case_					:      float = 0.9			,			snake_case_					:      float = 0.9_9_9			,			snake_case_					:      float = 1e-7			,			snake_case_					:      bool = False			,			snake_case_					:      float = 0.0			,			snake_case_					:      Optional[List[str]] = None			,			snake_case_					:      Optional[List[str]] = None			,			snake_case_					:      str = "AdamWeightDecay"			,			**snake_case_					:      Any			,			):



								'''simple docstring'''



								super().__init__(snake_case_			,			snake_case_			,			snake_case_			,			snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  )
								snake_case__						:       str       						=  weight_decay_rate
								snake_case__						:       str       						=  include_in_weight_decay
								snake_case__						:       Union[str, Any]       						=  exclude_from_weight_decay







						@classmethod
						def       __magic_name__							(							cls					:      Dict			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  {'''WarmUp''': WarmUp}
								return super(snake_case_			,			cls  ).from_config(snake_case_			,			custom_objects=snake_case_  )







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      Tuple			,			snake_case_					:      Dict			,			snake_case_					:      Any  ):



								'''simple docstring'''



								super(snake_case_			,			self  )._prepare_local(snake_case_			,			snake_case_			,			snake_case_  )
								snake_case__						:       Tuple       						=  tf.constant(
								    self.weight_decay_rate			,			name='''adam_weight_decay_rate'''  )







						def       __magic_name__							(							self					:      int			,			snake_case_					:      int			,			snake_case_					:      str			,			snake_case_					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self._do_use_weight_decay(var.name  )
								if do_decay:
										return var.assign_sub(
										    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']			,			use_locking=self._use_locking			,			)
								return tf.no_op()







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      List[str]=None			,			**snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  list(zip(*snake_case_  )  )
								return super(snake_case_			,			self  ).apply_gradients(zip(snake_case_			,			snake_case_  )			,			name=snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      str			,			snake_case_					:      List[str]			,			snake_case_					:      List[str]			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								if apply_state is None:
										return self._decayed_lr_t[var_dtype], {}

								snake_case__						:       Optional[int]       						=  apply_state or {}
								snake_case__						:       Tuple       						=  apply_state.get((var_device, var_dtype)  )
								if coefficients is None:
										snake_case__						:       Optional[int]       						=  self._fallback_apply_state(snake_case_			,			snake_case_  )
										snake_case__						:       str       						=  coefficients

								return coefficients["lr_t"], {"apply_state": apply_state}







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      str			,			snake_case_					:      List[str]=None  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self._get_lr(var.device			,			var.dtype.base_dtype			,			snake_case_  )
								snake_case__						:       List[str]       						=  self._decay_weights_op(snake_case_			,			snake_case_			,			snake_case_  )
								with tf.control_dependencies([decay]  ):
										return super(snake_case_			,			self  )._resource_apply_dense(snake_case_			,			snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Any			,			snake_case_					:      List[Any]			,			snake_case_					:      Any=None  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self._get_lr(var.device			,			var.dtype.base_dtype			,			snake_case_  )
								snake_case__						:       Tuple       						=  self._decay_weights_op(snake_case_			,			snake_case_			,			snake_case_  )
								with tf.control_dependencies([decay]  ):
										return super(snake_case_			,			self  )._resource_apply_sparse(snake_case_			,			snake_case_			,			snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  super().get_config()
								config.update({'''weight_decay_rate''': self.weight_decay_rate}  )
								return config







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								if self.weight_decay_rate == 0:
										return False

								if self._include_in_weight_decay:
										for r in self._include_in_weight_decay:
												if re.search(snake_case_			,			snake_case_  ) is not None:
														return True

								if self._exclude_from_weight_decay:
										for r in self._exclude_from_weight_decay:
												if re.search(snake_case_			,			snake_case_  ) is not None:
														return False
								return True





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       str       						=  []
								snake_case__						:       List[Any]       						=  None







						@property
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								if self._accum_steps is None:
										snake_case__						:       Any       						=  tf.Variable(
										    tf.constant(0			,			dtype=tf.intaa  )			,			trainable=snake_case_			,			synchronization=tf.VariableSynchronization.ON_READ			,			aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,			)

								return self._accum_steps.value()







						@property
						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								if not self._gradients:
										raise ValueError('''The accumulator should be called first to initialize the gradients'''  )
								return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]







						def __call__(							self					:      List[Any]			,			snake_case_					:      int  ):



								'''simple docstring'''



								if not self._gradients:
										snake_case__						:       Optional[Any]       						=  self.step  # Create the step variable.
										self._gradients.extend(
										    [
										        tf.Variable(
										            tf.zeros_like(snake_case_  )			,			trainable=snake_case_			,			synchronization=tf.VariableSynchronization.ON_READ			,			aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,			)
										        if gradient is not None
										        else gradient
										        for gradient in gradients
										    ]  )
								if len(snake_case_  ) != len(self._gradients  ):
										raise ValueError(F"""Expected {len(self._gradients  )} gradients, but got {len(snake_case_  )}"""  )

								for accum_gradient, gradient in zip(self._gradients			,			snake_case_  ):
										if accum_gradient is not None and gradient is not None:
												accum_gradient.assign_add(snake_case_  )

								self._accum_steps.assign_add(1  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								if not self._gradients:
										return
								self._accum_steps.assign(0  )
								for gradient in self._gradients:
										if gradient is not None:
												gradient.assign(tf.zeros_like(snake_case_  )  )






'''simple docstring'''


import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    GPTNeoXForCausalLM,
					    GPTNeoXForQuestionAnswering,
					    GPTNeoXForSequenceClassification,
					    GPTNeoXForTokenClassification,
					    GPTNeoXModel,
					)




class     a			:






						"""simple docstring"""




						def __init__(							self					:      Any			,			snake_case_					:      str			,			snake_case_					:      Optional[Any]=1_3			,			snake_case_					:      int=7			,			snake_case_					:      int=True			,			snake_case_					:      Optional[Any]=True			,			snake_case_					:      Dict=True			,			snake_case_					:      int=True			,			snake_case_					:      Optional[Any]=9_9			,			snake_case_					:      int=6_4			,			snake_case_					:      Dict=5			,			snake_case_					:      List[Any]=4			,			snake_case_					:      Union[str, Any]=3_7			,			snake_case_					:      Dict="gelu"			,			snake_case_					:      Union[str, Any]=0.1			,			snake_case_					:      Dict=0.1			,			snake_case_					:      Any=5_1_2			,			snake_case_					:      Any=1_6			,			snake_case_					:      Any=2			,			snake_case_					:      Dict=0.0_2			,			snake_case_					:      List[str]=3			,			snake_case_					:      Optional[int]=4			,			snake_case_					:      str=None			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  parent
								snake_case__						:       int       						=  batch_size
								snake_case__						:       Dict       						=  seq_length
								snake_case__						:       int       						=  is_training
								snake_case__						:       Optional[Any]       						=  use_input_mask
								snake_case__						:       Optional[Any]       						=  use_token_type_ids
								snake_case__						:       Dict       						=  use_labels
								snake_case__						:       int       						=  vocab_size
								snake_case__						:       Any       						=  hidden_size
								snake_case__						:       int       						=  num_hidden_layers
								snake_case__						:       Union[str, Any]       						=  num_attention_heads
								snake_case__						:       List[Any]       						=  intermediate_size
								snake_case__						:       int       						=  hidden_act
								snake_case__						:       int       						=  hidden_dropout_prob
								snake_case__						:       List[Any]       						=  attention_probs_dropout_prob
								snake_case__						:       Optional[int]       						=  max_position_embeddings
								snake_case__						:       Optional[int]       						=  type_vocab_size
								snake_case__						:       Any       						=  type_sequence_label_size
								snake_case__						:       str       						=  initializer_range
								snake_case__						:       List[str]       						=  num_labels
								snake_case__						:       Dict       						=  num_choices
								snake_case__						:       Union[str, Any]       						=  scope
								snake_case__						:       List[Any]       						=  vocab_size - 1







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size  )

								snake_case__						:       Optional[int]       						=  None
								if self.use_input_mask:
										snake_case__						:       int       						=  random_attention_mask([self.batch_size, self.seq_length]  )

								snake_case__						:       Union[str, Any]       						=  None
								if self.use_labels:
										snake_case__						:       Optional[Any]       						=  ids_tensor([self.batch_size, self.seq_length]			,			self.num_labels  )

								snake_case__						:       Dict       						=  self.get_config()

								return config, input_ids, input_mask, token_labels







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								return GPTNeoXConfig(
								    vocab_size=self.vocab_size			,			hidden_size=self.hidden_size			,			num_hidden_layers=self.num_hidden_layers			,			num_attention_heads=self.num_attention_heads			,			intermediate_size=self.intermediate_size			,			hidden_act=self.hidden_act			,			hidden_dropout_prob=self.hidden_dropout_prob			,			attention_probs_dropout_prob=self.attention_probs_dropout_prob			,			max_position_embeddings=self.max_position_embeddings			,			type_vocab_size=self.type_vocab_size			,			is_decoder=snake_case_			,			initializer_range=self.initializer_range			,			pad_token_id=self.pad_token_id			,			)







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       str       						=  self.prepare_config_and_inputs()

								snake_case__						:       List[str]       						=  True

								return config, input_ids, input_mask, token_labels







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Any			,			snake_case_					:      str			,			snake_case_					:      str  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  GPTNeoXModel(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Union[str, Any]       						=  model(snake_case_			,			attention_mask=snake_case_  )
								snake_case__						:       int       						=  model(snake_case_  )
								self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Any			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  True
								snake_case__						:       Tuple       						=  GPTNeoXModel(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Dict       						=  model(snake_case_			,			attention_mask=snake_case_  )
								self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)  )







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      str			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  GPTNeoXForCausalLM(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Tuple       						=  model(snake_case_			,			attention_mask=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      str			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.num_labels
								snake_case__						:       List[Any]       						=  GPTNeoXForQuestionAnswering(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Any       						=  model(snake_case_			,			attention_mask=snake_case_  )
								self.parent.assertEqual(result.start_logits.shape			,			(self.batch_size, self.seq_length)  )
								self.parent.assertEqual(result.end_logits.shape			,			(self.batch_size, self.seq_length)  )







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.num_labels
								snake_case__						:       List[str]       						=  GPTNeoXForSequenceClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Tuple       						=  ids_tensor([self.batch_size]			,			self.type_sequence_label_size  )
								snake_case__						:       str       						=  model(snake_case_			,			attention_mask=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      Dict			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.num_labels
								snake_case__						:       Any       						=  GPTNeoXForTokenClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Optional[Any]       						=  model(snake_case_			,			attention_mask=snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.num_labels)  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      int			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  True
								snake_case__						:       Union[str, Any]       						=  GPTNeoXForCausalLM(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()

								# first forward pass
								snake_case__						:       int       						=  model(snake_case_			,			attention_mask=snake_case_			,			use_cache=snake_case_  )
								snake_case__						:       Dict       						=  outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								snake_case__						:       Tuple       						=  ids_tensor((self.batch_size, 3)			,			config.vocab_size  )
								snake_case__						:       Union[str, Any]       						=  ids_tensor((self.batch_size, 3)			,			vocab_size=2  )

								# append to next input_ids and
								snake_case__						:       Dict       						=  torch.cat([input_ids, next_tokens]			,			dim=-1  )
								snake_case__						:       Optional[int]       						=  torch.cat([input_mask, next_mask]			,			dim=-1  )

								snake_case__						:       Optional[Any]       						=  model(snake_case_			,			attention_mask=snake_case_			,			output_hidden_states=snake_case_  )
								snake_case__						:       Union[str, Any]       						=  output_from_no_past['''hidden_states'''][0]
								snake_case__						:       str       						=  model(
								    snake_case_			,			attention_mask=snake_case_			,			past_key_values=snake_case_			,			output_hidden_states=snake_case_			,			)['''hidden_states'''][0]

								# select random slice
								snake_case__						:       str       						=  ids_tensor((1,)			,			output_from_past.shape[-1]  ).item()
								snake_case__						:       str       						=  output_from_no_past[:, -3:, random_slice_idx].detach()
								snake_case__						:       Union[str, Any]       						=  output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(snake_case_			,			snake_case_			,			atol=1e-3  )  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.prepare_config_and_inputs()
								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Tuple       						=  config_and_inputs
								snake_case__						:       str       						=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict





@require_torch
class     a			(				SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    (
						    (
						        GPTNeoXModel,
						        GPTNeoXForCausalLM,
						        GPTNeoXForQuestionAnswering,
						        GPTNeoXForSequenceClassification,
						        GPTNeoXForTokenClassification,
						    )
						    if is_torch_available()
						    else ()
						)
						__UpperCAmelCase			     =    (GPTNeoXForCausalLM,) if is_torch_available() else ()
						__UpperCAmelCase			     =    (
						    {
						        """feature-extraction""": GPTNeoXModel,
						        """question-answering""": GPTNeoXForQuestionAnswering,
						        """text-classification""": GPTNeoXForSequenceClassification,
						        """text-generation""": GPTNeoXForCausalLM,
						        """token-classification""": GPTNeoXForTokenClassification,
						        """zero-shot""": GPTNeoXForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  GPTNeoXModelTester(self  )
								snake_case__						:       List[str]       						=  ConfigTester(self			,			config_class=snake_case_			,			hidden_size=6_4			,			num_attention_heads=8  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								self.config_tester.run_common_tests()







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       List[Any]       						=  self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Any       						=  self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case__						:       Optional[int]       						=  None

								self.model_tester.create_and_check_model_as_decoder(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       Dict       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*snake_case_  )







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*snake_case_  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*snake_case_  )







						@unittest.skip(reason='''Feed forward chunking is not implemented'''  )
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								pass







						@parameterized.expand([('''linear''',), ('''dynamic''',)]  )
						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       List[Any]       						=  self.model_tester.prepare_config_and_inputs_for_common()
								snake_case__						:       Dict       						=  ids_tensor([1, 1_0]			,			config.vocab_size  )
								snake_case__						:       List[Any]       						=  ids_tensor([1, int(config.max_position_embeddings * 1.5  )]			,			config.vocab_size  )

								set_seed(4_2  )  # Fixed seed at init time so the two models get the same random weights
								snake_case__						:       Tuple       						=  GPTNeoXModel(snake_case_  )
								original_model.to(snake_case_  )
								original_model.eval()
								snake_case__						:       Any       						=  original_model(snake_case_  ).last_hidden_state
								snake_case__						:       List[str]       						=  original_model(snake_case_  ).last_hidden_state

								set_seed(4_2  )  # Fixed seed at init time so the two models get the same random weights
								snake_case__						:       Optional[Any]       						=  {'''type''': scaling_type, '''factor''': 1_0.0}
								snake_case__						:       Optional[Any]       						=  GPTNeoXModel(snake_case_  )
								scaled_model.to(snake_case_  )
								scaled_model.eval()
								snake_case__						:       Optional[int]       						=  scaled_model(snake_case_  ).last_hidden_state
								snake_case__						:       List[str]       						=  scaled_model(snake_case_  ).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
										self.assertTrue(torch.allclose(snake_case_			,			snake_case_			,			atol=1e-5  )  )
								else:
										self.assertFalse(torch.allclose(snake_case_			,			snake_case_			,			atol=1e-5  )  )

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(snake_case_			,			snake_case_			,			atol=1e-5  )  )





@require_torch
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@slow
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )
								for checkpointing in [True, False]:
										snake_case__						:       str       						=  GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''  )

										if checkpointing:
												model.gradient_checkpointing_enable()
										else:
												model.gradient_checkpointing_disable()
										model.to(snake_case_  )

										snake_case__						:       Tuple       						=  tokenizer('''My favorite food is'''			,			return_tensors='''pt'''  ).to(snake_case_  )
										# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
										# See: https://github.com/huggingface/transformers/pull/24193
										snake_case__						:       List[str]       						=  '''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''

										snake_case__						:       Optional[int]       						=  model.generate(**snake_case_			,			do_sample=snake_case_			,			max_new_tokens=2_0  )
										snake_case__						:       Tuple       						=  tokenizer.batch_decode(snake_case_  )[0]

										self.assertEqual(snake_case_			,			snake_case_  )



