
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       ):







							def __init__(    self	:	int   ,					snake_case__	:	Optional[Any]   ,					snake_case__	:	Any   ,					snake_case__	:	Any   ,					snake_case__	:	str = 1.0   ,					snake_case__	:	str = None   ,					):
								"""simple docstring"""







								super().__init__()
								__lowerCAmelCase    =				initial_learning_rate
								__lowerCAmelCase    =				warmup_steps
								__lowerCAmelCase    =				power
								__lowerCAmelCase    =				decay_schedule_fn
								__lowerCAmelCase    =				name







							def __call__(    self	:	Union[str, Any]   ,					snake_case__	:	Union[str, 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`.
									__lowerCAmelCase    =				tf.cast(lowerCAmelCase__   ,					tf.floataa			)
									__lowerCAmelCase    =				tf.cast(self.warmup_steps   ,					tf.floataa			)
									__lowerCAmelCase    =				global_step_float / warmup_steps_float
									__lowerCAmelCase    =				self.initial_learning_rate * tf.math.pow(lowerCAmelCase__   ,					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=lowerCAmelCase__   ,					)







							def    UpperCAmelCase__       (    self	:	List[Any]			):
								"""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 		_UpperCAmelCase (						UpperCamelCase:  float      ,				UpperCamelCase:  int      ,				UpperCamelCase:  int      ,				UpperCamelCase:  float = 0.0      ,				UpperCamelCase:  float = 0.9      ,				UpperCamelCase:  float = 0.999      ,				UpperCamelCase:  float = 1e-8      ,				UpperCamelCase:  Optional[float] = None      ,				UpperCamelCase:  Optional[float] = None      ,				UpperCamelCase:  float = 0.0      ,				UpperCamelCase:  float = 1.0      ,				UpperCamelCase:  Optional[List[str]] = None      ,				):






	"""simple docstring"""


	__lowerCAmelCase    =				tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=_lowercase      ,				decay_steps=num_train_steps - num_warmup_steps      ,				end_learning_rate=init_lr * min_lr_ratio      ,				power=_lowercase      ,				)
	if num_warmup_steps:
		__lowerCAmelCase    =				WarmUp(
		    initial_learning_rate=_lowercase      ,				decay_schedule_fn=_lowercase      ,				warmup_steps=_lowercase      ,				)
	if weight_decay_rate > 0.0:
		__lowerCAmelCase    =				AdamWeightDecay(
		    learning_rate=_lowercase      ,				weight_decay_rate=_lowercase      ,				beta_a=_lowercase      ,				beta_a=_lowercase      ,				epsilon=_lowercase      ,				clipnorm=_lowercase      ,				global_clipnorm=_lowercase      ,				exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]      ,				include_in_weight_decay=_lowercase      ,				)
	else:
		__lowerCAmelCase    =				tf.keras.optimizers.Adam(
		    learning_rate=_lowercase      ,				beta_a=_lowercase      ,				beta_a=_lowercase      ,				epsilon=_lowercase      ,				clipnorm=_lowercase      ,				global_clipnorm=_lowercase      ,				)
	# 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		( a__       ):







							def __init__(    self	:	Dict   ,					snake_case__	:	str = 0.0_0_1   ,					snake_case__	:	Union[str, Any] = 0.9   ,					snake_case__	:	int = 0.9_9_9   ,					snake_case__	:	List[str] = 1E-7   ,					snake_case__	:	str = False   ,					snake_case__	:	List[str] = 0.0   ,					snake_case__	:	List[str] = None   ,					snake_case__	:	int = None   ,					snake_case__	:	int = "AdamWeightDecay"   ,					**snake_case__	:	Any   ,					):
								"""simple docstring"""







								super().__init__(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					**lowerCAmelCase__			)
								__lowerCAmelCase    =				weight_decay_rate
								__lowerCAmelCase    =				include_in_weight_decay
								__lowerCAmelCase    =				exclude_from_weight_decay







							@classmethod
							def    UpperCAmelCase__       (    cls	:	Optional[Any]   ,					snake_case__	:	str			):
								"""simple docstring"""







								__lowerCAmelCase    =				{"WarmUp": WarmUp}
								return super(lowerCAmelCase__   ,					cls			).from_config(lowerCAmelCase__   ,					custom_objects=lowerCAmelCase__			)







							def    UpperCAmelCase__       (    self	:	Dict   ,					snake_case__	:	List[Any]   ,					snake_case__	:	Union[str, Any]   ,					snake_case__	:	int			):
								"""simple docstring"""







								super(lowerCAmelCase__   ,					self			)._prepare_local(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__			)
								__lowerCAmelCase    =				tf.constant(
								    self.weight_decay_rate   ,					name="adam_weight_decay_rate"			)







							def    UpperCAmelCase__       (    self	:	Tuple   ,					snake_case__	:	Union[str, Any]   ,					snake_case__	:	Dict   ,					snake_case__	:	Optional[int]			):
								"""simple docstring"""







								__lowerCAmelCase    =				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    UpperCAmelCase__       (    self	:	int   ,					snake_case__	:	Union[str, Any]   ,					snake_case__	:	List[Any]=None   ,					**snake_case__	:	Optional[Any]			):
								"""simple docstring"""







								__lowerCAmelCase    =				list(zip(*lowerCAmelCase__			)			)
								return super(lowerCAmelCase__   ,					self			).apply_gradients(zip(lowerCAmelCase__   ,					lowerCAmelCase__			)   ,					name=lowerCAmelCase__   ,					**lowerCAmelCase__			)







							def    UpperCAmelCase__       (    self	:	int   ,					snake_case__	:	Dict   ,					snake_case__	:	List[Any]   ,					snake_case__	:	Tuple			):
								"""simple docstring"""







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

								__lowerCAmelCase    =				apply_state or {}
								__lowerCAmelCase    =				apply_state.get((var_device, var_dtype)			)
								if coefficients is None:
									__lowerCAmelCase    =				self._fallback_apply_state(lowerCAmelCase__   ,					lowerCAmelCase__			)
									__lowerCAmelCase    =				coefficients

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







							def    UpperCAmelCase__       (    self	:	str   ,					snake_case__	:	Any   ,					snake_case__	:	Optional[int]   ,					snake_case__	:	Tuple=None			):
								"""simple docstring"""







								__lowerCAmelCase    =				self._get_lr(var.device   ,					var.dtype.base_dtype   ,					lowerCAmelCase__			)
								__lowerCAmelCase    =				self._decay_weights_op(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__			)
								with tf.control_dependencies([decay]			):
									return super(lowerCAmelCase__   ,					self			)._resource_apply_dense(lowerCAmelCase__   ,					lowerCAmelCase__   ,					**lowerCAmelCase__			)







							def    UpperCAmelCase__       (    self	:	Tuple   ,					snake_case__	:	Any   ,					snake_case__	:	Optional[int]   ,					snake_case__	:	str   ,					snake_case__	:	List[str]=None			):
								"""simple docstring"""







								__lowerCAmelCase    =				self._get_lr(var.device   ,					var.dtype.base_dtype   ,					lowerCAmelCase__			)
								__lowerCAmelCase    =				self._decay_weights_op(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__			)
								with tf.control_dependencies([decay]			):
									return super(lowerCAmelCase__   ,					self			)._resource_apply_sparse(lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					**lowerCAmelCase__			)







							def    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								__lowerCAmelCase    =				super().get_config()
								config.update({"weight_decay_rate": self.weight_decay_rate}			)
								return config







							def    UpperCAmelCase__       (    self	:	str   ,					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(lowerCAmelCase__   ,					lowerCAmelCase__			) is not None:
											return True

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

class    a		( a__       ):







							def __init__(    self	:	Dict			):
								"""simple docstring"""







								__lowerCAmelCase    =				[]
								__lowerCAmelCase    =				None







							@property
							def    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







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

								return self._accum_steps.value()







							@property
							def    UpperCAmelCase__       (    self	:	Optional[Any]			):
								"""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	:	str   ,					snake_case__	:	List[Any]			):
								"""simple docstring"""







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

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

								self._accum_steps.assign_add(1			)







							def    UpperCAmelCase__       (    self	:	List[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(lowerCAmelCase__			)			)













"""simple docstring"""




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
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 (
		    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CTRLForSequenceClassification,
		    CTRLLMHeadModel,
		    CTRLModel,
		)


class 			__UpperCamelCase							:






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=14 ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=99 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=5 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=37 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=None ,   )      ->					Optional[int]:
								a :						Optional[int]      =							parent
								a :						Optional[int]      =							batch_size
								a :						str      =							seq_length
								a :						int      =							is_training
								a :						Union[str, Any]      =							use_token_type_ids
								a :						Tuple      =							use_input_mask
								a :						Optional[Any]      =							use_labels
								a :						str      =							use_mc_token_ids
								a :						str      =							vocab_size
								a :						str      =							hidden_size
								a :						List[Any]      =							num_hidden_layers
								a :						str      =							num_attention_heads
								a :						Tuple      =							intermediate_size
								a :						List[Any]      =							hidden_act
								a :						List[Any]      =							hidden_dropout_prob
								a :						List[str]      =							attention_probs_dropout_prob
								a :						Dict      =							max_position_embeddings
								a :						int      =							type_vocab_size
								a :						Any      =							type_sequence_label_size
								a :						List[Any]      =							initializer_range
								a :						List[str]      =							num_labels
								a :						Optional[int]      =							num_choices
								a :						int      =							scope
								a :						Tuple      =							self.vocab_size - 1






							def        __a  (  self )      ->					int:
								a :						Optional[Any]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

								a :						str      =							None
								if self.use_input_mask:
									a :						Dict      =							random_attention_mask([self.batch_size, self.seq_length] )

								a :						Tuple      =							None
								if self.use_token_type_ids:
									a :						int      =							ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size )

								a :						int      =							None
								if self.use_mc_token_ids:
									a :						Dict      =							ids_tensor([self.batch_size, self.num_choices] ,   self.seq_length )

								a :						List[Any]      =							None
								a :						Optional[int]      =							None
								a :						str      =							None
								if self.use_labels:
									a :						List[str]      =							ids_tensor([self.batch_size] ,   self.type_sequence_label_size )
									a :						List[str]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels )
									a :						Any      =							ids_tensor([self.batch_size] ,   self.num_choices )

								a :						Tuple      =							self.get_config()

								a :						int      =							ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,   2 )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)






							def        __a  (  self )      ->					Tuple:
								return CTRLConfig(
								    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 ,   pad_token_id=self.pad_token_id ,   )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					str:
								a :						Union[str, Any]      =							CTRLModel(config=lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()

								model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ ,   head_mask=lowerCAmelCase__ )
								model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ )
								a :						List[Any]      =							model(lowerCAmelCase__ )
								self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size) )
								self.parent.assertEqual(len(result.past_key_values ) ,   config.n_layer )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					Optional[int]:
								a :						Tuple      =							CTRLLMHeadModel(lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()

								a :						List[str]      =							model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ ,   labels=lowerCAmelCase__ )
								self.parent.assertEqual(result.loss.shape ,   () )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )






							def        __a  (  self )      ->					Optional[int]:
								a :						Tuple      =							self.prepare_config_and_inputs()

								(
								    (
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			
								) :						Optional[Any]      =							config_and_inputs

								a :						List[Any]      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

								return config, inputs_dict






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					Optional[Any]:
								a :						List[str]      =							self.num_labels
								a :						Union[str, Any]      =							CTRLForSequenceClassification(lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()
								a :						Optional[int]      =							ids_tensor([self.batch_size] ,   self.type_sequence_label_size )
								a :						Dict      =							model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ ,   labels=lowerCAmelCase__ )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels) )


@require_torch
class 			__UpperCamelCase							(		a__						,			a__						,			a__						,			unittest.TestCase		):
							lowerCamelCase  :   Tuple    =(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							lowerCamelCase  :   List[Any]    =(CTRLLMHeadModel,) if is_torch_available() else ()
							lowerCamelCase  :   List[Any]    =(
							    {
							        """feature-extraction""": CTRLModel,
							        """text-classification""": CTRLForSequenceClassification,
							        """text-generation""": CTRLLMHeadModel,
							        """zero-shot""": CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase  :   Optional[int]    =True
							lowerCamelCase  :   List[str]    =False
							lowerCamelCase  :   Optional[Any]    =False






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[int]:
								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
									# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
									# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
									# config could not be created.
									return True

								return False






							def        __a  (  self )      ->					str:
								a :						str      =							CTRLModelTester(self )
								a :						Dict      =							ConfigTester(self ,   config_class=lowerCAmelCase__ ,   n_embd=37 )






							def        __a  (  self )      ->					Tuple:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()






							def        __a  (  self )      ->					Optional[Any]:
								self.config_tester.run_common_tests()






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[int]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								a :						List[str]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__ )






							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
							def        __a  (  self )      ->					Tuple:
								pass






							@slow
							def        __a  (  self )      ->					Optional[Any]:
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						Union[str, Any]      =							CTRLModel.from_pretrained(lowerCAmelCase__ )
									self.assertIsNotNone(lowerCAmelCase__ )






							@unittest.skip("The model doesn't support left padding" )  # and it's not used enough to be worth fixing :)
							def        __a  (  self )      ->					Tuple:
								pass


@require_torch
class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					int:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()






							@slow
							def        __a  (  self )      ->					Dict:
								a :						Dict      =							CTRLLMHeadModel.from_pretrained("ctrl" )
								model.to(lowerCAmelCase__ )
								a :						List[str]      =							torch.tensor(
								    [[1_1859, 0, 1611, 8]] ,   dtype=torch.long ,   device=lowerCAmelCase__ )  # Legal the president is
								a :						Any      =							[
								    1_1859,
								    0,
								    1611,
								    8,
								    5,
								    150,
								    2_6449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2595,
								    48,
								    2_0740,
								    24_6533,
								    24_6533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								a :						str      =							model.generate(lowerCAmelCase__ ,   do_sample=lowerCAmelCase__ )
								self.assertListEqual(output_ids[0].tolist() ,   lowerCAmelCase__ )


