






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
      from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__		:							Dict 				=				get_tests_dir("""fixtures/test_sentencepiece.model""")

SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				{"target_lang": "fi", "source_lang": "en"}
SCREAMING_SNAKE_CASE__		:							Any 				=				">>zh<<"
SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				"Helsinki-NLP/"

if is_torch_available():
      SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				"pt"
elif is_tf_available():
      SCREAMING_SNAKE_CASE__		:							Any 				=				"tf"
else:
      SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				"jax"







@require_sentencepiece
class 				UpperCAmelCase_  (		__a							,      unittest.TestCase		):
     __lowerCamelCase      				=  MarianTokenizer
     __lowerCamelCase      				=  False
     __lowerCamelCase      				=  True




     def   __UpperCAmelCase  (				self		):
          super().setUp()
          UpperCAmelCase__      :  Tuple				      =			["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
          UpperCAmelCase__      :  Optional[Any]				      =			dict(zip(lowerCAmelCase__	,						range(len(lowerCAmelCase__		)		)		)		)
          UpperCAmelCase__      :  Tuple				      =			Path(self.tmpdirname		)
          save_json(lowerCAmelCase__	,						save_dir / VOCAB_FILES_NAMES["""vocab"""]		)
          save_json(lowerCAmelCase__	,						save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]		)
          if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
               copyfile(lowerCAmelCase__	,						save_dir / VOCAB_FILES_NAMES["""source_spm"""]		)
               copyfile(lowerCAmelCase__	,						save_dir / VOCAB_FILES_NAMES["""target_spm"""]		)

          UpperCAmelCase__      :  str				      =			MarianTokenizer.from_pretrained(self.tmpdirname		)
          tokenizer.save_pretrained(self.tmpdirname		)




     def   __UpperCAmelCase  (				self	,						**_lowerCAmelCase		):
          return MarianTokenizer.from_pretrained(self.tmpdirname	,						**lowerCAmelCase__		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          return (
              "This is a test",
              "This is a test",
          )




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			"""</s>"""
          UpperCAmelCase__      :  List[str]				      =			0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__		)	,						lowerCAmelCase__		)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__		)	,						lowerCAmelCase__		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			list(self.get_tokenizer().get_vocab().keys()		)

          self.assertEqual(vocab_keys[0]	,						"""</s>"""		)
          self.assertEqual(vocab_keys[1]	,						"""<unk>"""		)
          self.assertEqual(vocab_keys[-1]	,						"""<pad>"""		)
          self.assertEqual(len(lowerCAmelCase__		)	,						9		)




     def   __UpperCAmelCase  (				self		):
          self.assertEqual(self.get_tokenizer().vocab_size	,						9		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de"		)
          UpperCAmelCase__      :  List[str]				      =			en_de_tokenizer(["""I am a small frog"""]	,						return_tensors=lowerCAmelCase__		)
          self.assertIsInstance(lowerCAmelCase__	,						lowerCAmelCase__		)
          UpperCAmelCase__      :  Dict				      =			[38, 121, 14, 697, 38848, 0]
          self.assertListEqual(lowerCAmelCase__	,						batch.input_ids[0]		)

          UpperCAmelCase__      :  List[str]				      =			tempfile.mkdtemp()
          en_de_tokenizer.save_pretrained(lowerCAmelCase__		)
          UpperCAmelCase__      :  Dict				      =			[x.name for x in Path(lowerCAmelCase__		).glob("""*"""		)]
          self.assertIn("""source.spm"""	,						lowerCAmelCase__		)
          MarianTokenizer.from_pretrained(lowerCAmelCase__		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[str]				      =			self.get_tokenizer()

          UpperCAmelCase__      :  Any				      =			tok(
              ["""I am a small frog""" * 1000, """I am a small frog"""]	,						padding=lowerCAmelCase__	,						truncation=lowerCAmelCase__	,						return_tensors=lowerCAmelCase__		)
          self.assertIsInstance(lowerCAmelCase__	,						lowerCAmelCase__		)
          self.assertEqual(batch.input_ids.shape	,						(2, 512)		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  int				      =			self.get_tokenizer()
          UpperCAmelCase__      :  Tuple				      =			tok(["""I am a tiny frog""", """I am a small frog"""]	,						padding=lowerCAmelCase__	,						return_tensors=lowerCAmelCase__		)
          self.assertIsInstance(lowerCAmelCase__	,						lowerCAmelCase__		)
          self.assertEqual(batch_smaller.input_ids.shape	,						(2, 10)		)




     @slow
     def   __UpperCAmelCase  (				self		):
          # fmt: off
          UpperCAmelCase__      :  Union[str, Any]				      =			{"""input_ids""": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=lowerCAmelCase__	,						model_name="""Helsinki-NLP/opus-mt-en-de"""	,						revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""	,						decode_kwargs={"""use_source_tokenizer""": True}	,						)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""		)

          UpperCAmelCase__      :  Any				      =			"""Tämä on testi"""
          UpperCAmelCase__      :  str				      =			"""This is a test"""

          UpperCAmelCase__      :  Union[str, Any]				      =			[76, 7, 2047, 2]
          UpperCAmelCase__      :  Optional[Any]				      =			[69, 12, 11, 940, 2]

          UpperCAmelCase__      :  Optional[int]				      =			tokenizer(lowerCAmelCase__		).input_ids
          self.assertListEqual(lowerCAmelCase__	,						lowerCAmelCase__		)

          UpperCAmelCase__      :  int				      =			tokenizer(text_target=lowerCAmelCase__		).input_ids
          self.assertListEqual(lowerCAmelCase__	,						lowerCAmelCase__		)

          UpperCAmelCase__      :  List[str]				      =			tokenizer.decode(lowerCAmelCase__	,						skip_special_tokens=lowerCAmelCase__		)
          self.assertEqual(lowerCAmelCase__	,						lowerCAmelCase__		)








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





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str     )  ->Any:
										return (x_in - self.means[key]) / self.stds[key]





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:List[Any]     )  ->Optional[Any]:
										return x_in * self.stds[key] + self.means[key]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Tuple:
										if type(lowerCAmelCase__     ) is dict:
														return {k: self.to_torch(lowerCAmelCase__     ) for k, v in x_in.items()}
										elif torch.is_tensor(lowerCAmelCase__     ):
														return x_in.to(self.unet.device     )
										return torch.tensor(lowerCAmelCase__		,    device=self.unet.device     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										for key, val in cond.items():
														lowercase         =      val.clone()
										return x_in





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

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

																						lowercase         =      self.scheduler._get_variance(lowerCAmelCase__     )
																						lowercase         =      torch.exp(0.5 * posterior_variance     )
																						lowercase         =      model_std * grad

																		lowercase         =      0
																		lowercase         =      x.detach()
																		lowercase         =      x + scale * grad
																		lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )

														lowercase         =      self.unet(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample.permute(0		,    2		,    1     )

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

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





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

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

										# run the diffusion process
										lowercase     ,	lowercase         =      self.run_diffusion(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

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

										lowercase         =      denorm_actions[selected_index, 0]
										return denorm_actions
