






'''simple docstring'''



import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		UNetaDModel     ,      UpperCAmelCase			:		UNetaDModel     ,      UpperCAmelCase			:		DDPMScheduler     ,      UpperCAmelCase			:		Optional[int]     ,      )  ->  str:


       '''simple docstring'''

       super().__init__()
       lowercase					:   str				  =value_function
       lowercase					:   Union[str, Any]				  =unet
       lowercase					:   List[str]				  =scheduler
       lowercase					:   Optional[Any]				  =env
       lowercase					:   Any				  =env.get_dataset()
       lowercase					:   Tuple				  ={}
       for key in self.data.keys():
        try:
         lowercase					:   Any				  =self.data[key].mean()
        except:  # noqa: E722
         pass
       lowercase					:   Tuple				  ={}
       for key in self.data.keys():
        try:
         lowercase					:   Tuple				  =self.data[key].std()
        except:  # noqa: E722
         pass
       lowercase					:   Optional[int]				  =env.observation_space.shape[0]
       lowercase					:   Dict				  =env.action_space.shape[0]






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any		)  ->  Dict:


       '''simple docstring'''

       return (x_in - self.means[key]) / self.stds[key]






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Tuple		)  ->  List[Any]:


       '''simple docstring'''

       return x_in * self.stds[key] + self.means[key]






      def 				A__      ( self			:		str     ,      UpperCAmelCase			:		str		)  ->  Union[str, Any]:


       '''simple docstring'''

       if type(UpperCAmelCase		) is dict:
        return {k: self.to_torch(UpperCAmelCase		) for k, v in x_in.items()}
       elif torch.is_tensor(UpperCAmelCase		):
        return x_in.to(self.unet.device		)
       return torch.tensor(UpperCAmelCase     ,      device=self.unet.device		)






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Tuple		)  ->  str:


       '''simple docstring'''

       for key, val in cond.items():
        lowercase					:   Dict				  =val.clone()
       return x_in






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Tuple		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =x.shape[0]
       lowercase					:   str				  =None
       for i in tqdm.tqdm(self.scheduler.timesteps		):
        # create batch of timesteps to pass into model
        lowercase					:   List[Any]				  =torch.full((batch_size,)     ,      UpperCAmelCase     ,      device=self.unet.device     ,      dtype=torch.long		)
        for _ in range(UpperCAmelCase		):
         with torch.enable_grad():
          x.requires_grad_()

          # permute to match dimension for pre-trained models
          lowercase					:   Dict				  =self.value_function(x.permute(0     ,      2     ,      1		)     ,      UpperCAmelCase		).sample
          lowercase					:   List[Any]				  =torch.autograd.grad([y.sum()]     ,      [x]		)[0]

          lowercase					:   Union[str, Any]				  =self.scheduler._get_variance(UpperCAmelCase		)
          lowercase					:   Any				  =torch.exp(0.5 * posterior_variance		)
          lowercase					:   str				  =model_std * grad

         lowercase					:   Any				  =0
         lowercase					:   List[Any]				  =x.detach()
         lowercase					:   Optional[Any]				  =x + scale * grad
         lowercase					:   Any				  =self.reset_xa(UpperCAmelCase     ,      UpperCAmelCase     ,      self.action_dim		)

        lowercase					:   Dict				  =self.unet(x.permute(0     ,      2     ,      1		)     ,      UpperCAmelCase		).sample.permute(0     ,      2     ,      1		)

        # TODO: verify deprecation of this kwarg
        lowercase					:   Optional[int]				  =self.scheduler.step(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      predict_epsilon=UpperCAmelCase		)['''prev_sample''']

        # apply conditions to the trajectory (set the initial state)
        lowercase					:   Tuple				  =self.reset_xa(UpperCAmelCase     ,      UpperCAmelCase     ,      self.action_dim		)
        lowercase					:   Any				  =self.to_torch(UpperCAmelCase		)
       return x, y






      def __call__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]=64     ,      UpperCAmelCase			:		Dict=32     ,      UpperCAmelCase			:		List[Any]=2     ,      UpperCAmelCase			:		List[str]=0.1		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Tuple				  =self.normalize(UpperCAmelCase     ,      '''observations'''		)
       lowercase					:   Dict				  =obs[None].repeat(UpperCAmelCase     ,      axis=0		)

       lowercase					:   str				  ={0: self.to_torch(UpperCAmelCase		)}
       lowercase					:   Any				  =(batch_size, planning_horizon, self.state_dim + self.action_dim)

       # generate initial noise and apply our conditions (to make the trajectories start at current state)
       lowercase					:   Tuple				  =randn_tensor(UpperCAmelCase     ,      device=self.unet.device		)
       lowercase					:   Optional[Any]				  =self.reset_xa(UpperCAmelCase     ,      UpperCAmelCase     ,      self.action_dim		)
       lowercase					:   Dict				  =self.to_torch(UpperCAmelCase		)

       # run the diffusion process
       lowercase					:   Tuple				  =self.run_diffusion(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

       # sort output trajectories by value
       lowercase					:   int				  =y.argsort(0     ,      descending=UpperCAmelCase		).squeeze()
       lowercase					:   Union[str, Any]				  =x[sorted_idx]
       lowercase					:   Optional[int]				  =sorted_values[:, :, : self.action_dim]
       lowercase					:   str				  =actions.detach().cpu().numpy()
       lowercase					:   List[Any]				  =self.de_normalize(UpperCAmelCase     ,      key='''actions'''		)

       # select the action with the highest value
       if y is not None:
        lowercase					:   List[str]				  =0
       else:
        # if we didn't run value guiding, select a random action
        lowercase					:   List[str]				  =np.random.randint(0     ,      UpperCAmelCase		)

       lowercase					:   List[Any]				  =denorm_actions[selected_index, 0]
       return denorm_actions











'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.distilbert.modeling_tf_distilbert import (
          TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDistilBertForMaskedLM,
          TFDistilBertForMultipleChoice,
          TFDistilBertForQuestionAnswering,
          TFDistilBertForSequenceClassification,
          TFDistilBertForTokenClassification,
          TFDistilBertModel,
      )
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		List[str]     ,      )  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   int				  =parent
       lowercase					:   Any				  =13
       lowercase					:   Any				  =7
       lowercase					:   Optional[int]				  =True
       lowercase					:   Optional[int]				  =True
       lowercase					:   Tuple				  =False
       lowercase					:   Optional[Any]				  =True
       lowercase					:   Dict				  =99
       lowercase					:   Union[str, Any]				  =32
       lowercase					:   Union[str, Any]				  =2
       lowercase					:   Union[str, Any]				  =4
       lowercase					:   List[str]				  =37
       lowercase					:   str				  ='''gelu'''
       lowercase					:   Dict				  =0.1
       lowercase					:   List[Any]				  =0.1
       lowercase					:   List[str]				  =512
       lowercase					:   Optional[int]				  =16
       lowercase					:   Optional[Any]				  =2
       lowercase					:   List[str]				  =0.0_2
       lowercase					:   Any				  =3
       lowercase					:   Optional[Any]				  =4
       lowercase					:   int				  =None






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Tuple				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size		)

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

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

       lowercase					:   Dict				  =DistilBertConfig(
           vocab_size=self.vocab_size     ,      dim=self.hidden_size     ,      n_layers=self.num_hidden_layers     ,      n_heads=self.num_attention_heads     ,      hidden_dim=self.intermediate_size     ,      hidden_act=self.hidden_act     ,      dropout=self.hidden_dropout_prob     ,      attention_dropout=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      initializer_range=self.initializer_range     ,      )

       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   int				  =TFDistilBertModel(config=UpperCAmelCase		)
       lowercase					:   int				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}

       lowercase					:   List[str]				  =model(UpperCAmelCase		)

       lowercase					:   str				  =[input_ids, input_mask]

       lowercase					:   Tuple				  =model(UpperCAmelCase		)

       self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[str]				  =TFDistilBertForMaskedLM(config=UpperCAmelCase		)
       lowercase					:   int				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   Union[str, Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Optional[int]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  =TFDistilBertForQuestionAnswering(config=UpperCAmelCase		)
       lowercase					:   int				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
       }
       lowercase					:   List[str]				  =model(UpperCAmelCase		)
       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 				A__      ( self			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Dict				  =self.num_labels
       lowercase					:   Optional[Any]				  =TFDistilBertForSequenceClassification(UpperCAmelCase		)
       lowercase					:   str				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   Union[str, Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.num_choices
       lowercase					:   Optional[int]				  =TFDistilBertForMultipleChoice(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =tf.tile(tf.expand_dims(UpperCAmelCase     ,      1		)     ,      (1, self.num_choices, 1)		)
       lowercase					:   List[Any]				  =tf.tile(tf.expand_dims(UpperCAmelCase     ,      1		)     ,      (1, self.num_choices, 1)		)
       lowercase					:   Tuple				  ={
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
       }
       lowercase					:   Tuple				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Dict				  =self.num_labels
       lowercase					:   Tuple				  =TFDistilBertForTokenClassification(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       lowercase					:   str				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)		)






      def 				A__      ( self			:		List[str]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   int				  =self.prepare_config_and_inputs()
       ((lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase)  ,		(lowercase))					:   Union[str, Any]				  =config_and_inputs
       lowercase					:   Tuple				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict





@require_tf
class UpperCAmelCase_					(    __A				,       __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			(
          (
              TFDistilBertModel,
              TFDistilBertForMaskedLM,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertForMultipleChoice,
          )
          if is_tf_available()
          else None
      )
      UpperCamelCase_								=			(
          {
              '''feature-extraction''': TFDistilBertModel,
              '''fill-mask''': TFDistilBertForMaskedLM,
              '''question-answering''': TFDistilBertForQuestionAnswering,
              '''text-classification''': TFDistilBertForSequenceClassification,
              '''token-classification''': TFDistilBertForTokenClassification,
              '''zero-shot''': TFDistilBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase_								=			False
      UpperCamelCase_								=			False






      def 				A__      ( self			:		Dict		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =TFDistilBertModelTester(self		)
       lowercase					:   int				  =ConfigTester(self     ,      config_class=UpperCAmelCase     ,      dim=37		)






      def 				A__      ( self			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 				A__      ( self			:		Optional[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_model(*UpperCAmelCase		)






      def 				A__      ( self			:		Tuple		)  ->  Any:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCAmelCase		)






      def 				A__      ( self			:		Optional[int]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCAmelCase		)






      def 				A__      ( self			:		Any		)  ->  str:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCAmelCase		)






      def 				A__      ( self			:		Optional[Any]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCAmelCase		)






      def 				A__      ( self			:		str		)  ->  Union[str, Any]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCAmelCase		)






      @slow
      def 				A__      ( self			:		List[Any]		)  ->  Dict:


       '''simple docstring'''

       for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]		):
        lowercase					:   Union[str, Any]				  =TFDistilBertModel.from_pretrained(UpperCAmelCase		)
        self.assertIsNotNone(UpperCAmelCase		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      @slow
      def 				A__      ( self			:		List[str]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''		)
       lowercase					:   Tuple				  =tf.constant([[0, 1, 2, 3, 4, 5]]		)
       lowercase					:   List[Any]				  =model(UpperCAmelCase		)[0]

       lowercase					:   str				  =[1, 6, 768]
       self.assertEqual(output.shape     ,      UpperCAmelCase		)

       lowercase					:   Optional[int]				  =tf.constant(
           [
               [
                   [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                   [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                   [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
               ]
           ]		)
       tf.debugging.assert_near(output[:, :3, :3]     ,      UpperCAmelCase     ,      atol=1e-4		)



