

'''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   _SCREAMING_SNAKE_CASE   :



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



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

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

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

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

      __magic_name__						       =    None
      __magic_name__						       =    None
      __magic_name__						       =    None
      if self.use_labels:
           __magic_name__						       =    ids_tensor([self.batch_size]			,				self.type_sequence_label_size      )
           __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels      )
           __magic_name__						       =    ids_tensor([self.batch_size]			,				self.num_choices      )

      __magic_name__						       =    self.get_config()

      __magic_name__						       =    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 			snake_case__  (   self		:   str      ):
      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 			snake_case__  (   self		:   Dict			,				a__		:   str			,				a__		:   Dict			,				a__		:   Any			,				a__		:   str			,				a__		:   Dict			,				*a__		:   Union[str, Any]      ):
      __magic_name__						       =    CTRLModel(config=a__      )
      model.to(a__      )
      model.eval()

      model(a__			,				token_type_ids=a__			,				head_mask=a__      )
      model(a__			,				token_type_ids=a__      )
      __magic_name__						       =    model(a__      )
      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 			snake_case__  (   self		:   List[str]			,				a__		:   Dict			,				a__		:   Union[str, Any]			,				a__		:   List[str]			,				a__		:   List[str]			,				a__		:   Optional[Any]			,				*a__		:   int      ):
      __magic_name__						       =    CTRLLMHeadModel(a__      )
      model.to(a__      )
      model.eval()

      __magic_name__						       =    model(a__			,				token_type_ids=a__			,				labels=a__      )
      self.parent.assertEqual(result.loss.shape			,				()      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.prepare_config_and_inputs()

      (
          __magic_name__  
      )						       =    config_and_inputs

      __magic_name__						       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

      return config, inputs_dict



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   Optional[Any]			,				a__		:   str			,				a__		:   Tuple			,				a__		:   int			,				*a__		:   List[Any]      ):
      __magic_name__						       =    self.num_labels
      __magic_name__						       =    CTRLForSequenceClassification(a__      )
      model.to(a__      )
      model.eval()
      __magic_name__						       =    ids_tensor([self.batch_size]			,				self.type_sequence_label_size      )
      __magic_name__						       =    model(a__			,				token_type_ids=a__			,				labels=a__      )
      self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)      )






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



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   List[str]			,				a__		:   Any			,				a__		:   Optional[int]			,				a__		:   List[str]      ):




      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 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    CTRLModelTester(self      )
      __magic_name__						       =    ConfigTester(self			,				config_class=a__			,				n_embd=37      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()



 def 			snake_case__  (   self		:   int      ):
      self.config_tester.run_common_tests()



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_ctrl_model(*a__      )



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head_model(*a__      )



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



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




      for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __magic_name__						       =    CTRLModel.from_pretrained(a__      )
           self.assertIsNotNone(a__      )



 @unittest.skip('''The model doesn\'t support left padding'''      )  # and it's not used enough to be worth fixing :)
 def 			snake_case__  (   self		:   str      ):
      pass






@require_torch
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def 			snake_case__  (   self		:   List[Any]      ):
      super().tearDown()
      # clean-up as much as possible GPU memory occupied by PyTorch
      gc.collect()
      torch.cuda.empty_cache()



 @slow
 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    CTRLLMHeadModel.from_pretrained('''ctrl'''      )
      model.to(a__      )
      __magic_name__						       =    torch.tensor(
          [[1_1859, 0, 1611, 8]]			,				dtype=torch.long			,				device=a__      )  # Legal the president is
      __magic_name__						       =    [
          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

      __magic_name__						       =    model.generate(a__			,				do_sample=a__      )
      self.assertListEqual(output_ids[0].tolist()			,				a__      )









'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

      if self.config.trained_betas is not None:
           __magic_name__						       =    torch.tensor(self.config.trained_betas			,				dtype=torch.floataa      )
      else:
           __magic_name__						       =    torch.sin(steps * math.pi / 2      ) ** 2

      __magic_name__						       =    (1.0 - self.betas**2) ** 0.5

      __magic_name__						       =    (torch.atana(self.betas			,				self.alphas      ) / math.pi * 2)[:-1]
      __magic_name__						       =    timesteps.to(a__      )

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      if self.num_inference_steps is None:
           raise ValueError(
               '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''      )

      __magic_name__						       =    (self.timesteps == timestep).nonzero().item()
      __magic_name__						       =    timestep_index + 1

      __magic_name__						       =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(a__      )

      if len(self.ets      ) == 1:
           __magic_name__						       =    self.ets[-1]
      elif len(self.ets      ) == 2:
           __magic_name__						       =    (3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets      ) == 3:
           __magic_name__						       =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           __magic_name__						       =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

      __magic_name__						       =    self.alphas[prev_timestep_index]
      __magic_name__						       =    self.betas[prev_timestep_index]

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






