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  :





       '''simple docstring'''



       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=0.0_2     ,       __lowerCAmelCase=3     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=None     ,       )  ->  Optional[int]:
        lowercase__    :   List[Any]						= parent
        lowercase__    :   Union[str, Any]						= batch_size
        lowercase__    :   List[Any]						= seq_length
        lowercase__    :   Optional[Any]						= is_training
        lowercase__    :   Union[str, Any]						= use_token_type_ids
        lowercase__    :   str						= use_input_mask
        lowercase__    :   Any						= use_labels
        lowercase__    :   Optional[Any]						= use_mc_token_ids
        lowercase__    :   List[Any]						= vocab_size
        lowercase__    :   Tuple						= hidden_size
        lowercase__    :   Any						= num_hidden_layers
        lowercase__    :   Optional[Any]						= num_attention_heads
        lowercase__    :   str						= intermediate_size
        lowercase__    :   Optional[int]						= hidden_act
        lowercase__    :   Optional[Any]						= hidden_dropout_prob
        lowercase__    :   Union[str, Any]						= attention_probs_dropout_prob
        lowercase__    :   Tuple						= max_position_embeddings
        lowercase__    :   List[str]						= type_vocab_size
        lowercase__    :   Dict						= type_sequence_label_size
        lowercase__    :   List[Any]						= initializer_range
        lowercase__    :   Any						= num_labels
        lowercase__    :   Optional[Any]						= num_choices
        lowercase__    :   Tuple						= scope
        lowercase__    :   Dict						= self.vocab_size - 1

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Dict						= ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size			)

        lowercase__    :   List[str]						= None
        if self.use_input_mask:
         lowercase__    :   Union[str, Any]						= random_attention_mask([self.batch_size, self.seq_length]			)

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

        lowercase__    :   Union[str, Any]						= None
        if self.use_mc_token_ids:
         lowercase__    :   List[str]						= ids_tensor([self.batch_size, self.num_choices]     ,       self.seq_length			)

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

        lowercase__    :   List[str]						= self.get_config()

        lowercase__    :   List[str]						= 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 _lowerCAmelCase(						self			)  ->  List[Any]:
        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       *__lowerCAmelCase			)  ->  Tuple:
        lowercase__    :   List[str]						= CTRLModel(config=__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()

        model(__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase     ,       head_mask=__lowerCAmelCase			)
        model(__lowerCAmelCase     ,       token_type_ids=__lowerCAmelCase			)
        lowercase__    :   str						= 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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       *__lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Union[str, Any]						= CTRLLMHeadModel(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()

        lowercase__    :   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 _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   Any						= self.prepare_config_and_inputs()

        (
            lowercase__							
        )    :   Dict						= config_and_inputs

        lowercase__    :   str						= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

        return config, inputs_dict


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       *__lowerCAmelCase			)  ->  Dict:
        lowercase__    :   str						= self.num_labels
        lowercase__    :   str						= CTRLForSequenceClassification(__lowerCAmelCase			)
        model.to(__lowerCAmelCase			)
        model.eval()
        lowercase__    :   Optional[int]						= ids_tensor([self.batch_size]     ,       self.type_sequence_label_size			)
        lowercase__    :   Tuple						= 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 ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE 		=   (CTRLLMHeadModel,) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE 		=   (
           {
               "feature-extraction": CTRLModel,
               "text-classification": CTRLForSequenceClassification,
               "text-generation": CTRLLMHeadModel,
               "zero-shot": CTRLForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   False
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  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 _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   int						= CTRLModelTester(self			)
        lowercase__    :   List[str]						= ConfigTester(self     ,       config_class=__lowerCAmelCase     ,       n_embd=37			)

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

       def _lowerCAmelCase(						self			)  ->  Tuple:
        self.config_tester.run_common_tests()

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Dict						= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_ctrl_model(*__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   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 _lowerCAmelCase(						self			)  ->  Tuple:
        pass

       @slow
       def _lowerCAmelCase(						self			)  ->  Any:
        for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowercase__    :   Tuple						= 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 _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        pass



@require_torch
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



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


       @slow
       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   List[Any]						= CTRLLMHeadModel.from_pretrained('''ctrl'''			)
        model.to(__lowerCAmelCase			)
        lowercase__    :   int						= torch.tensor(
            [[11859, 0, 1611, 8]]     ,       dtype=torch.long     ,       device=__lowerCAmelCase			)  # Legal the president is
        lowercase__    :   Dict						= [
            11859,
            0,
            1611,
            8,
            5,
            150,
            26449,
            2,
            19,
            348,
            469,
            3,
            2595,
            48,
            20740,
            246533,
            246533,
            19,
            30,
            5,
        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

        lowercase__    :   int						= model.generate(__lowerCAmelCase     ,       do_sample=__lowerCAmelCase			)
        self.assertListEqual(output_ids[0].tolist()     ,       __lowerCAmelCase			)


'''simple docstring'''

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   None


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=0.9_9_9		,					UpperCAmelCase="cosine"		,					):
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCAmelCase					):
   return math.exp(t * -1_2.0					)

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 lowercase__    :   str						= []
 for i in range(UpperCAmelCase					):
  lowercase__    :   int						= i / num_diffusion_timesteps
  lowercase__    :   Tuple						= (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(UpperCAmelCase					) / alpha_bar_fn(UpperCAmelCase					)		,					UpperCAmelCase					)					)
 return torch.tensor(UpperCAmelCase		,					dtype=torch.floataa					)





class       UpperCAmelCase  (						a__   ,						a__ ):





       '''simple docstring'''



       @register_to_config
       def __init__(						self     ,       __lowerCAmelCase = 1000     ,       __lowerCAmelCase = "fixed_small_log"     ,       __lowerCAmelCase = True     ,       __lowerCAmelCase = 1.0     ,       __lowerCAmelCase = "epsilon"     ,       __lowerCAmelCase = "squaredcos_cap_v2"     ,       )  ->  Optional[int]:
        if beta_schedule != "squaredcos_cap_v2":
         raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''			)

        lowercase__    :   Union[str, Any]						= betas_for_alpha_bar(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= 1.0 - self.betas
        lowercase__    :   int						= torch.cumprod(self.alphas     ,       dim=0			)
        lowercase__    :   str						= torch.tensor(1.0			)

        # standard deviation of the initial noise distribution
        lowercase__    :   Optional[Any]						= 1.0

        # setable values
        lowercase__    :   Optional[Any]						= None
        lowercase__    :   List[Any]						= torch.from_numpy(np.arange(0     ,       __lowerCAmelCase			)[::-1].copy()			)

        lowercase__    :   Tuple						= variance_type

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  torch.FloatTensor:
        return sample

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Optional[int]:
        lowercase__    :   List[str]						= num_inference_steps
        lowercase__    :   List[str]						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowercase__    :   List[str]						= (np.arange(0     ,       __lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
        lowercase__    :   str						= torch.from_numpy(__lowerCAmelCase			).to(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Tuple:
        if prev_timestep is None:
         lowercase__    :   Any						= t - 1

        lowercase__    :   Any						= self.alphas_cumprod[t]
        lowercase__    :   List[Any]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   str						= 1 - alpha_prod_t
        lowercase__    :   int						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Tuple						= self.betas[t]
        else:
         lowercase__    :   Dict						= 1 - alpha_prod_t / alpha_prod_t_prev

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        lowercase__    :   Any						= beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
         lowercase__    :   Union[str, Any]						= self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small_log":
         lowercase__    :   int						= torch.log(torch.clamp(__lowerCAmelCase     ,       min=1E-20			)			)
         lowercase__    :   Dict						= torch.exp(0.5 * variance			)
        elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         lowercase__    :   Union[str, Any]						= variance.log()
         lowercase__    :   Optional[int]						= beta.log()

         lowercase__    :   Tuple						= (predicted_variance + 1) / 2
         lowercase__    :   Dict						= frac * max_log + (1 - frac) * min_log

        return variance

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase = True     ,       )  ->  Union[UnCLIPSchedulerOutput, Tuple]:
        lowercase__    :   Tuple						= timestep

        if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         lowercase__			,							lowercase__    :   str						= torch.split(__lowerCAmelCase     ,       sample.shape[1]     ,       dim=1			)
        else:
         lowercase__    :   Dict						= None

        # 1. compute alphas, betas
        if prev_timestep is None:
         lowercase__    :   int						= t - 1

        lowercase__    :   Optional[int]						= self.alphas_cumprod[t]
        lowercase__    :   Optional[int]						= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowercase__    :   Optional[int]						= 1 - alpha_prod_t
        lowercase__    :   List[Any]						= 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
         lowercase__    :   Optional[int]						= self.betas[t]
         lowercase__    :   Optional[Any]						= self.alphas[t]
        else:
         lowercase__    :   Any						= 1 - alpha_prod_t / alpha_prod_t_prev
         lowercase__    :   Optional[int]						= 1 - beta

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
         lowercase__    :   int						= (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
         lowercase__    :   Dict						= model_output
        else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             ''' for the UnCLIPScheduler.'''			)

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         lowercase__    :   List[Any]						= torch.clamp(
             __lowerCAmelCase     ,       -self.config.clip_sample_range     ,       self.config.clip_sample_range			)

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   Optional[Any]						= (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowercase__    :   Tuple						= alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        lowercase__    :   List[Any]						= pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowercase__    :   List[Any]						= 0
        if t > 0:
         lowercase__    :   Dict						= randn_tensor(
             model_output.shape     ,       dtype=model_output.dtype     ,       generator=__lowerCAmelCase     ,       device=model_output.device			)

         lowercase__    :   Union[str, Any]						= self._get_variance(
             __lowerCAmelCase     ,       predicted_variance=__lowerCAmelCase     ,       prev_timestep=__lowerCAmelCase     ,       )

         if self.variance_type == "fixed_small_log":
          lowercase__    :   List[Any]						= variance
         elif self.variance_type == "learned_range":
          lowercase__    :   int						= (0.5 * variance).exp()
         else:
          raise ValueError(
              F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
              ''' for the UnCLIPScheduler.'''			)

         lowercase__    :   List[str]						= variance * variance_noise

        lowercase__    :   Union[str, Any]						= pred_prev_sample + variance

        if not return_dict:
         return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase     ,       pred_original_sample=__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  torch.FloatTensor:
        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
        lowercase__    :   Tuple						= self.alphas_cumprod.to(device=original_samples.device     ,       dtype=original_samples.dtype			)
        lowercase__    :   str						= timesteps.to(original_samples.device			)

        lowercase__    :   Union[str, Any]						= alphas_cumprod[timesteps] ** 0.5
        lowercase__    :   List[str]						= sqrt_alpha_prod.flatten()
        while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   List[str]						= sqrt_alpha_prod.unsqueeze(-1			)

        lowercase__    :   int						= (1 - alphas_cumprod[timesteps]) ** 0.5
        lowercase__    :   List[Any]						= sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
         lowercase__    :   int						= sqrt_one_minus_alpha_prod.unsqueeze(-1			)

        lowercase__    :   Optional[int]						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

