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      lowerCamelCase    (SCREAMING_SNAKE_CASE_    ):
  """simple docstring"""







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







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				Optional[Any]      )     ->		Dict:
        return (x_in - self.means[key]) / self.stds[key]







  def       __A   (							self       :				int     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Union[str, Any]      )     ->		str:
        return x_in * self.stds[key] + self.means[key]







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				List[Any]      )     ->		List[Any]:
        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       :				Union[str, Any]     ,   __magic_name__       :				Any     ,   __magic_name__       :				str     ,   __magic_name__       :				Optional[Any]      )     ->		List[str]:
        for key, val in cond.items():
              SCREAMING_SNAKE_CASE_      =     val.clone()
        return x_in







  def       __A   (							self       :				Any     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     x.shape[0]
        SCREAMING_SNAKE_CASE_      =     None
        for i in tqdm.tqdm(self.scheduler.timesteps      ):
              # create batch of timesteps to pass into model
              SCREAMING_SNAKE_CASE_      =     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
                          SCREAMING_SNAKE_CASE_      =     self.value_function(x.permute(0     ,   2     ,   1      )     ,   __UpperCAmelCase      ).sample
                          SCREAMING_SNAKE_CASE_      =     torch.autograd.grad([y.sum()]     ,   [x]      )[0]

                          SCREAMING_SNAKE_CASE_      =     self.scheduler._get_variance(__UpperCAmelCase      )
                          SCREAMING_SNAKE_CASE_      =     torch.exp(0.5 * posterior_variance      )
                          SCREAMING_SNAKE_CASE_      =     model_std * grad

                    SCREAMING_SNAKE_CASE_      =     0
                    SCREAMING_SNAKE_CASE_      =     x.detach()
                    SCREAMING_SNAKE_CASE_      =     x + scale * grad
                    SCREAMING_SNAKE_CASE_      =     self.reset_xa(__UpperCAmelCase     ,   __UpperCAmelCase     ,   self.action_dim      )

              SCREAMING_SNAKE_CASE_      =     self.unet(x.permute(0     ,   2     ,   1      )     ,   __UpperCAmelCase      ).sample.permute(0     ,   2     ,   1      )

              # TODO: verify deprecation of this kwarg
              SCREAMING_SNAKE_CASE_      =     self.scheduler.step(__UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   predict_epsilon=__UpperCAmelCase      )["""prev_sample"""]

              # apply conditions to the trajectory (set the initial state)
              SCREAMING_SNAKE_CASE_      =     self.reset_xa(__UpperCAmelCase     ,   __UpperCAmelCase     ,   self.action_dim      )
              SCREAMING_SNAKE_CASE_      =     self.to_torch(__UpperCAmelCase      )
        return x, y







  def __call__(							self       :				List[str]     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				Dict=64     ,   __magic_name__       :				Dict=32     ,   __magic_name__       :				Tuple=2     ,   __magic_name__       :				Optional[Any]=0.1      )     ->		Optional[int]:
        # normalize the observations and create  batch dimension
        SCREAMING_SNAKE_CASE_      =     self.normalize(__UpperCAmelCase     ,   "observations"      )
        SCREAMING_SNAKE_CASE_      =     obs[None].repeat(__UpperCAmelCase     ,   axis=0      )

        SCREAMING_SNAKE_CASE_      =     {0: self.to_torch(__UpperCAmelCase      )}
        SCREAMING_SNAKE_CASE_      =     (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)
        SCREAMING_SNAKE_CASE_      =     randn_tensor(__UpperCAmelCase     ,   device=self.unet.device      )
        SCREAMING_SNAKE_CASE_      =     self.reset_xa(__UpperCAmelCase     ,   __UpperCAmelCase     ,   self.action_dim      )
        SCREAMING_SNAKE_CASE_      =     self.to_torch(__UpperCAmelCase      )

        # run the diffusion process
        SCREAMING_SNAKE_CASE_      =     self.run_diffusion(__UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase     ,   __UpperCAmelCase      )

        # sort output trajectories by value
        SCREAMING_SNAKE_CASE_      =     y.argsort(0     ,   descending=__UpperCAmelCase      ).squeeze()
        SCREAMING_SNAKE_CASE_      =     x[sorted_idx]
        SCREAMING_SNAKE_CASE_      =     sorted_values[:, :, : self.action_dim]
        SCREAMING_SNAKE_CASE_      =     actions.detach().cpu().numpy()
        SCREAMING_SNAKE_CASE_      =     self.de_normalize(__UpperCAmelCase     ,   key="actions"      )

        # select the action with the highest value
        if y is not None:
              SCREAMING_SNAKE_CASE_      =     0
        else:
              # if we didn't run value guiding, select a random action
              SCREAMING_SNAKE_CASE_      =     np.random.randint(0     ,   __UpperCAmelCase      )

        SCREAMING_SNAKE_CASE_      =     denorm_actions[selected_index, 0]
        return denorm_actions







'''simple docstring'''





import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class        lowerCAmelCase_:
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[Any]:
      return None







class        lowerCAmelCase_:
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      return None







class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      [
      # (model_name, model_kwargs)
      ('''bert-base-cased''', {}),
      ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
  ]




  @require_tf
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(__UpperCAmelCase  ,"""tf"""  ,12  ,**__UpperCAmelCase      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,**__UpperCAmelCase      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      from transformers import BertModel

      lowerCAmelCase__ :  Optional[int]							      =					["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
      with NamedTemporaryFile(mode="""w+t"""      ) as vocab_file:
          vocab_file.write("""\n""".join(__UpperCAmelCase      )      )
          vocab_file.flush()
          lowerCAmelCase__ :  Dict							      =					BertTokenizerFast(vocab_file.name      )

      with TemporaryDirectory() as bert_save_dir:
          lowerCAmelCase__ :  Tuple							      =					BertModel(BertConfig(vocab_size=len(__UpperCAmelCase      )      )      )
          model.save_pretrained(__UpperCAmelCase      )
          self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,__UpperCAmelCase      )




  @require_tf
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          lowerCAmelCase__ :  Dict							      =					self._test_export(__UpperCAmelCase  ,"""tf"""  ,12  ,**__UpperCAmelCase      )
          lowerCAmelCase__ :  List[str]							      =					quantize(Path(__UpperCAmelCase      )      )

          # Ensure the actual quantized model is not bigger than the original one
          if quantized_path.stat().st_size >= Path(__UpperCAmelCase      ).stat().st_size:
              self.fail("""Quantized model is bigger than initial ONNX model"""      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          lowerCAmelCase__ :  Any							      =					self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,**__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					quantize(__UpperCAmelCase      )

          # Ensure the actual quantized model is not bigger than the original one
          if quantized_path.stat().st_size >= Path(__UpperCAmelCase      ).stat().st_size:
              self.fail("""Quantized model is bigger than initial ONNX model"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Optional[Any]:
      try:
          # Compute path
          with TemporaryDirectory() as tempdir:
              lowerCAmelCase__ :  Optional[int]							      =					Path(__UpperCAmelCase      ).joinpath("""model.onnx"""      )

          # Remove folder if exists
          if path.parent.exists():
              path.parent.rmdir()

          # Export
          convert(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase      )

          return path
      except Exception as e:
          self.fail(__UpperCAmelCase      )




  @require_torch
  @require_tokenizers
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      from transformers import BertModel

      lowerCAmelCase__ :  List[Any]							      =					BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""      )      )
      lowerCAmelCase__ :  Union[str, Any]							      =					BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""      )
      self._test_infer_dynamic_axis(__UpperCAmelCase  ,__UpperCAmelCase  ,"""pt"""      )




  @require_tf
  @require_tokenizers
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      from transformers import TFBertModel

      lowerCAmelCase__ :  int							      =					TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""      )      )
      lowerCAmelCase__ :  Optional[int]							      =					BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""      )
      self._test_infer_dynamic_axis(__UpperCAmelCase  ,__UpperCAmelCase  ,"""tf"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Any							      =					FeatureExtractionPipeline(__UpperCAmelCase  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					infer_shapes(__UpperCAmelCase  ,__UpperCAmelCase      )

      # Assert all variables are present
      self.assertEqual(len(__UpperCAmelCase      )  ,len(__UpperCAmelCase      )      )
      self.assertTrue(all(var_name in shapes for var_name in variable_names      )      )
      self.assertSequenceEqual(variable_names[:3]  ,__UpperCAmelCase      )
      self.assertSequenceEqual(variable_names[3:]  ,__UpperCAmelCase      )

      # Assert inputs are {0: batch, 1: sequence}
      for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
          self.assertDictEqual(shapes[var_name]  ,{0: """batch""", 1: """sequence"""}      )

      # Assert outputs are {0: batch, 1: sequence} and {0: batch}
      self.assertDictEqual(shapes["""output_0"""]  ,{0: """batch""", 1: """sequence"""}      )
      self.assertDictEqual(shapes["""output_1"""]  ,{0: """batch"""}      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[str]							      =					["""input_ids""", """attention_mask""", """token_type_ids"""]
      lowerCAmelCase__ :  Union[str, Any]							      =					{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					ensure_valid_input(FuncContiguousArgs()  ,__UpperCAmelCase  ,__UpperCAmelCase      )

      # Should have exactly the same number of args (all are valid)
      self.assertEqual(len(__UpperCAmelCase      )  ,3      )

      # Should have exactly the same input names
      self.assertEqual(set(__UpperCAmelCase      )  ,set(__UpperCAmelCase      )      )

      # Parameter should be reordered according to their respective place in the function:
      # (input_ids, token_type_ids, attention_mask)
      self.assertEqual(__UpperCAmelCase  ,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])      )

      # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
      lowerCAmelCase__      ,   lowerCAmelCase__ :  int							      =					ensure_valid_input(FuncNonContiguousArgs()  ,__UpperCAmelCase  ,__UpperCAmelCase      )

      # Should have exactly the one arg (all before the one not provided "some_other_args")
      self.assertEqual(len(__UpperCAmelCase      )  ,1      )
      self.assertEqual(len(__UpperCAmelCase      )  ,1      )

      # Should have only "input_ids"
      self.assertEqual(inputs_args[0]  ,tokens["""input_ids"""]      )
      self.assertEqual(ordered_input_names[0]  ,"""input_ids"""      )






  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  Dict							      =					generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""      )  ,"""-test"""      )
      self.assertEqual("""/home/something/my_fake_model-test.onnx"""  ,generated.as_posix()      )


