





"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    lowerCamelCase_       =		"""pt"""
elif is_tf_available():
    lowerCamelCase_       =		"""tf"""
else:
    lowerCamelCase_       =		"""jax"""






class  _SCREAMING_SNAKE_CASE(       lowerCAmelCase__   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				str     						=  ByTaTokenizer
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  False




       def    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             super().setUp()
             __SCREAMING_SNAKE_CASE       :Dict		    =  ByTaTokenizer()
             tokenizer.save_pretrained(self.tmpdirname							)




       @cached_property
       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             return ByTaTokenizer.from_pretrained('''google/byt5-small'''							)




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				Optional[Any]:





             """simple docstring"""






             return self.tokenizer_class.from_pretrained(self.tmpdirname		,**a__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=20		,SCREAMING_SNAKE_CASE__=5							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  []
             for i in range(len(a__							)							):
                   try:
                         __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer.decode([i]		,clean_up_tokenization_spaces=a__							)
                   except UnicodeDecodeError:
                         pass
                   toks.append((i, tok)							)

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  list(filter(lambda SCREAMING_SNAKE_CASE__							: re.match(R'''^[ a-zA-Z]+$'''		,t[1]							)		,a__							)							)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  list(filter(lambda SCREAMING_SNAKE_CASE__							: [t[0]] == tokenizer.encode(t[1]		,add_special_tokens=a__							)		,a__							)							)
             if max_length is not None and len(a__							) > max_length:
                   __SCREAMING_SNAKE_CASE       :str		    =  toks[:max_length]
             if min_length is not None and len(a__							) < min_length and len(a__							) > 0:
                   while len(a__							) < min_length:
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  toks + toks
        # toks_str = [t[1] for t in toks]
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  [t[0] for t in toks]

             # Ensure consistency
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer.decode(a__		,clean_up_tokenization_spaces=a__							)
             if " " not in output_txt and len(a__							) > 1:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  (
                       tokenizer.decode([toks_ids[0]]		,clean_up_tokenization_spaces=a__							)
                       + ''' '''
                       + tokenizer.decode(toks_ids[1:]		,clean_up_tokenization_spaces=a__							)
                   )
             if with_prefix_space:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ''' ''' + output_txt
             __SCREAMING_SNAKE_CASE       :int		    =  tokenizer.encode(a__		,add_special_tokens=a__							)
             return output_txt, output_ids




       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.ta_base_tokenizer
             __SCREAMING_SNAKE_CASE       :str		    =  tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>''']							)
             __SCREAMING_SNAKE_CASE       :str		    =  tokenizer(['''hi''', '''I went to the gym''', '''''']							)
             self.assertListEqual(batch_with_eos_added['''input_ids''']		,batch_without_eos_added['''input_ids''']							)




       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.ta_base_tokenizer
             __SCREAMING_SNAKE_CASE       :List[str]		    =  '''Unicode €.'''
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  tokenizer(a__							)
             __SCREAMING_SNAKE_CASE       :str		    =  [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
             self.assertEqual(encoded['''input_ids''']		,a__							)

             # decoding
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  tokenizer.decode(a__							)
             self.assertEqual(a__		,'''Unicode €.</s>'''							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  tokenizer('''e è é ê ë'''							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
             self.assertEqual(encoded['''input_ids''']		,a__							)
             # decoding
             __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer.decode(a__							)
             self.assertEqual(a__		,'''e è é ê ë</s>'''							)

             # encode/decode, but with `encode` instead of `__call__`
             self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''							)							)		,'''e è é ê ë</s>'''							)




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.ta_base_tokenizer
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             # fmt: off
             __SCREAMING_SNAKE_CASE       :Tuple		    =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
             # fmt: on
             __SCREAMING_SNAKE_CASE       :Any		    =  tokenizer(a__		,padding=a__		,return_tensors=a__							)
             self.assertIsInstance(a__		,a__							)

             if FRAMEWORK != "jax":
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  list(batch.input_ids.numpy()[0]							)
             else:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  list(batch.input_ids.tolist()[0]							)

             self.assertListEqual(a__		,a__							)

             self.assertEqual((2, 37)		,batch.input_ids.shape							)
             self.assertEqual((2, 37)		,batch.attention_mask.shape							)




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.ta_base_tokenizer
             __SCREAMING_SNAKE_CASE       :Tuple		    =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  tokenizer(a__		,padding=a__		,return_tensors=a__							)
             # check if input_ids are returned and no decoder_input_ids
             self.assertIn('''input_ids'''		,a__							)
             self.assertIn('''attention_mask'''		,a__							)
             self.assertNotIn('''decoder_input_ids'''		,a__							)
             self.assertNotIn('''decoder_attention_mask'''		,a__							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.ta_base_tokenizer
             __SCREAMING_SNAKE_CASE       :List[str]		    =  [
                 '''Summary of the text.''',
                 '''Another summary.''',
             ]
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  tokenizer(
                 text_target=a__		,max_length=32		,padding='''max_length'''		,truncation=a__		,return_tensors=a__							)
             self.assertEqual(32		,targets['''input_ids'''].shape[1]							)




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.ta_base_tokenizer
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  ['''A long paragraph for summarization. </s>''']
             __SCREAMING_SNAKE_CASE       :Any		    =  ['''Summary of the text. </s>''']
             # fmt: off
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
             # fmt: on

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer(a__		,text_target=a__							)

             self.assertEqual(a__		,batch['''input_ids'''][0]							)
             self.assertEqual(a__		,batch['''labels'''][0]							)




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.get_tokenizers()
             for tokenizer in tokenizers:
                   with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                         self.assertNotEqual(tokenizer.model_max_length		,42							)

        # Now let's start the test
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.get_tokenizers()
             for tokenizer in tokenizers:
                   with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                         # Isolate this from the other tests because we save additional tokens/etc
                         __SCREAMING_SNAKE_CASE       :List[str]		    =  tempfile.mkdtemp()

                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  ''' He is very happy, UNwant\u00E9d,running'''
                         __SCREAMING_SNAKE_CASE       :Any		    =  tokenizer.encode(a__		,add_special_tokens=a__							)
                         tokenizer.save_pretrained(a__							)

                         __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer.__class__.from_pretrained(a__							)
                         __SCREAMING_SNAKE_CASE       :Optional[int]		    =  after_tokenizer.encode(a__		,add_special_tokens=a__							)
                         self.assertListEqual(a__		,a__							)

                         shutil.rmtree(a__							)

             __SCREAMING_SNAKE_CASE       :int		    =  self.get_tokenizers(model_max_length=42							)
             for tokenizer in tokenizers:
                   with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                         # Isolate this from the other tests because we save additional tokens/etc
                         __SCREAMING_SNAKE_CASE       :Optional[int]		    =  tempfile.mkdtemp()

                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  ''' He is very happy, UNwant\u00E9d,running'''
                         tokenizer.add_tokens(['''bim''', '''bambam''']							)
                         __SCREAMING_SNAKE_CASE       :Optional[int]		    =  tokenizer.additional_special_tokens
                         additional_special_tokens.append('''new_additional_special_token'''							)
                         tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}							)
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  tokenizer.encode(a__		,add_special_tokens=a__							)
                         tokenizer.save_pretrained(a__							)

                         __SCREAMING_SNAKE_CASE       :str		    =  tokenizer.__class__.from_pretrained(a__							)
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  after_tokenizer.encode(a__		,add_special_tokens=a__							)
                         self.assertListEqual(a__		,a__							)
                         self.assertIn('''new_additional_special_token'''		,after_tokenizer.additional_special_tokens							)
                         self.assertEqual(after_tokenizer.model_max_length		,42							)

                         __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer.__class__.from_pretrained(a__		,model_max_length=43							)
                         self.assertEqual(tokenizer.model_max_length		,43							)

                         shutil.rmtree(a__							)




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  []
             if self.test_slow_tokenizer:
                   tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

             if self.test_rust_tokenizer:
                   tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

             for tokenizer_class, tokenizer_utils in tokenizer_list:
                   with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(a__							)

                         with open(os.path.join(a__		,'''special_tokens_map.json'''							)		,encoding='''utf-8'''							) as json_file:
                               __SCREAMING_SNAKE_CASE       :Optional[int]		    =  json.load(a__							)

                         with open(os.path.join(a__		,'''tokenizer_config.json'''							)		,encoding='''utf-8'''							) as json_file:
                               __SCREAMING_SNAKE_CASE       :Any		    =  json.load(a__							)

                         __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [f'''<extra_id_{i}>''' for i in range(1_25							)]

                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  added_tokens_extra_ids + [
                             '''an_additional_special_token'''
                         ]
                         __SCREAMING_SNAKE_CASE       :str		    =  added_tokens_extra_ids + [
                             '''an_additional_special_token'''
                         ]

                         with open(os.path.join(a__		,'''special_tokens_map.json'''							)		,'''w'''		,encoding='''utf-8'''							) as outfile:
                               json.dump(a__		,a__							)
                         with open(os.path.join(a__		,'''tokenizer_config.json'''							)		,'''w'''		,encoding='''utf-8'''							) as outfile:
                               json.dump(a__		,a__							)

                         # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                         # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                         # "special_tokens_map.json" files
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  tokenizer_class.from_pretrained(
                             a__		,)
                         self.assertIn(
                             '''an_additional_special_token'''		,tokenizer_without_change_in_init.additional_special_tokens							)
                         # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                         self.assertEqual(
                             ['''an_additional_special_token''']		,tokenizer_without_change_in_init.convert_ids_to_tokens(
                                 tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']							)							)		,)

                         # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                         __SCREAMING_SNAKE_CASE       :Any		    =  added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''		,lstrip=a__							)]
                         __SCREAMING_SNAKE_CASE       :Dict		    =  tokenizer_class.from_pretrained(
                             a__		,additional_special_tokens=a__		,)

                         self.assertIn('''a_new_additional_special_token'''		,tokenizer.additional_special_tokens							)
                         self.assertEqual(
                             ['''a_new_additional_special_token''']		,tokenizer.convert_ids_to_tokens(
                                 tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']							)							)		,)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :str		    =  []
             if self.test_slow_tokenizer:
                   tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

             if self.test_rust_tokenizer:
                   tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

             for tokenizer_class, tokenizer_utils in tokenizer_list:
                   with tempfile.TemporaryDirectory() as tmp_dir:
                         tokenizer_utils.save_pretrained(a__							)

                         __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer_class.from_pretrained(a__							)

                         self.assertTrue(tokenizer.decode([2_55]							) == ''''''							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             pass




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             pass




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             pass




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             pass




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_tokenizers(fast=a__		,do_lower_case=a__							)
             for tokenizer in tokenizers:
                   with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                         __SCREAMING_SNAKE_CASE       :Dict		    =  ['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                         __SCREAMING_SNAKE_CASE       :Dict		    =  tokenizer.convert_tokens_to_string(a__							)

                         self.assertIsInstance(a__		,a__							)




       def    _UpperCamelCase					(						self							)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.get_tokenizers()
             for tokenizer in tokenizers:
                   with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  [
                             '''bos_token''',
                             '''eos_token''',
                             '''unk_token''',
                             '''sep_token''',
                             '''pad_token''',
                             '''cls_token''',
                             '''mask_token''',
                         ]

                         __SCREAMING_SNAKE_CASE       :Any		    =  0
                         __SCREAMING_SNAKE_CASE       :List[str]		    =  tokenizer.convert_ids_to_tokens(
                             a__		,skip_special_tokens=a__							)

                         for attr in attributes_list:
                               setattr(a__		,attr + '''_id'''		,a__							)
                               self.assertEqual(getattr(a__		,a__							)		,a__							)
                               self.assertEqual(getattr(a__		,attr + '''_id'''							)		,a__							)

                               setattr(a__		,attr + '''_id'''		,a__							)
                               self.assertEqual(getattr(a__		,a__							)		,a__							)
                               self.assertEqual(getattr(a__		,attr + '''_id'''							)		,a__							)

                         setattr(a__		,'''additional_special_tokens_ids'''		,[]							)
                         self.assertListEqual(getattr(a__		,'''additional_special_tokens'''							)		,[]							)
                         self.assertListEqual(getattr(a__		,'''additional_special_tokens_ids'''							)		,[]							)

                         setattr(a__		,'''additional_special_tokens_ids'''		,[token_id_to_test_setters]							)
                         self.assertListEqual(getattr(a__		,'''additional_special_tokens'''							)		,[token_to_test_setters]							)
                         self.assertListEqual(getattr(a__		,'''additional_special_tokens_ids'''							)		,[token_id_to_test_setters]							)






"""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  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,)      ->				Optional[int]:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				str:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






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




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





             """simple docstring"""






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




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





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






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

                               # permute to match dimension for pre-trained models
                               __SCREAMING_SNAKE_CASE       :str		    =  self.value_function(x.permute(0		,2		,1							)		,SCREAMING_SNAKE_CASE__							).sample
                               __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.autograd.grad([y.sum()]		,[x]							)[0]

                               __SCREAMING_SNAKE_CASE       :int		    =  self.scheduler._get_variance(SCREAMING_SNAKE_CASE__							)
                               __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.exp(0.5 * posterior_variance							)
                               __SCREAMING_SNAKE_CASE       :List[str]		    =  model_std * grad

                         __SCREAMING_SNAKE_CASE       :Dict		    =  0
                         __SCREAMING_SNAKE_CASE       :List[Any]		    =  x.detach()
                         __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  x + scale * grad
                         __SCREAMING_SNAKE_CASE       :Any		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.unet(x.permute(0		,2		,1							)		,SCREAMING_SNAKE_CASE__							).sample.permute(0		,2		,1							)

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

                   # apply conditions to the trajectory (set the initial state)
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)
                   __SCREAMING_SNAKE_CASE       :Dict		    =  self.to_torch(SCREAMING_SNAKE_CASE__							)
             return x, y




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=64		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.1							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.normalize(SCREAMING_SNAKE_CASE__		,'''observations'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  obs[None].repeat(SCREAMING_SNAKE_CASE__		,axis=0							)

             __SCREAMING_SNAKE_CASE       :str		    =  {0: self.to_torch(SCREAMING_SNAKE_CASE__							)}
             __SCREAMING_SNAKE_CASE       :Optional[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)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  randn_tensor(SCREAMING_SNAKE_CASE__		,device=self.unet.device							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.reset_xa(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,self.action_dim							)
             __SCREAMING_SNAKE_CASE       :Any		    =  self.to_torch(SCREAMING_SNAKE_CASE__							)

             # run the diffusion process
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Tuple		    =  self.run_diffusion(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

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

             # select the action with the highest value
             if y is not None:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  0
             else:
                   # if we didn't run value guiding, select a random action
                   __SCREAMING_SNAKE_CASE       :Any		    =  np.random.randint(0		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  denorm_actions[selected_index, 0]
             return denorm_actions