
import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class   UpperCamelCase_      (      __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Union[str, Any]				   =					LongformerTokenizer
						UpperCamelCase      :  int				   =					True
						UpperCamelCase      :  Tuple				   =					LongformerTokenizerFast
						UpperCamelCase      :  List[str]				   =					True





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Optional[Any]:
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowercase         =      [
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "<unk>",
										]
										lowercase         =      dict(zip(lowerCAmelCase__		,    range(len(lowerCAmelCase__     )     )     )     )
										lowercase         =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										lowercase         =      {"unk_token": "<unk>"}

										lowercase         =      os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES["vocab_file"]     )
										lowercase         =      os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES["merges_file"]     )
										with open(self.vocab_file		,    "w"		,    encoding="utf-8"     ) as fp:
														fp.write(json.dumps(lowerCAmelCase__     ) + "\n"     )
										with open(self.merges_file		,    "w"		,    encoding="utf-8"     ) as fp:
														fp.write("\n".join(lowerCAmelCase__     )     )





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    **lowerCAmelCase__							:Dict     )  ->Optional[Any]:
										kwargs.update(self.special_tokens_map     )
										return self.tokenizer_class.from_pretrained(self.tmpdirname		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    **lowerCAmelCase__							:List[Any]     )  ->int:
										kwargs.update(self.special_tokens_map     )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,    **lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Dict     )  ->List[str]:
										lowercase         =      "lower newer"
										lowercase         =      "lower newer"
										return input_text, output_text





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										lowercase         =      self.tokenizer_class(self.vocab_file		,    self.merges_file		,    **self.special_tokens_map     )
										lowercase         =      "lower newer"
										lowercase         =      ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
										lowercase         =      tokenizer.tokenize(lowerCAmelCase__     )  # , add_prefix_space=True)
										self.assertListEqual(lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      tokens + [tokenizer.unk_token]
										lowercase         =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Optional[int]:
										lowercase         =      self.get_tokenizer()

										self.assertListEqual(tokenizer.encode("Hello world!"		,    add_special_tokens=lowerCAmelCase__     )		,    [0, 31414, 232, 328, 2]     )
										self.assertListEqual(
										    tokenizer.encode("Hello world! cécé herlolip 418"		,    add_special_tokens=lowerCAmelCase__     )		,    [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,    )





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										lowercase         =      self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"     )

										lowercase         =      tokenizer.encode("sequence builders"		,    add_special_tokens=lowerCAmelCase__     )
										lowercase         =      tokenizer.encode("multi-sequence build"		,    add_special_tokens=lowerCAmelCase__     )

										lowercase         =      tokenizer.encode(
										    "sequence builders"		,    add_special_tokens=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__     )
										lowercase         =      tokenizer.encode(
										    "sequence builders"		,    "multi-sequence build"		,    add_special_tokens=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__     )

										lowercase         =      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__     )
										lowercase         =      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__		,    lowerCAmelCase__     )

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->List[str]:
										lowercase         =      self.get_tokenizer()

										lowercase         =      "Encode this sequence."
										lowercase         =      tokenizer.byte_encoder[" ".encode("utf-8"     )[0]]

										# Testing encoder arguments
										lowercase         =      tokenizer.encode(lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__     )
										lowercase         =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
										self.assertNotEqual(lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      tokenizer.encode(lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__     )
										lowercase         =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
										self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )

										tokenizer.add_special_tokens({"bos_token": "<s>"}     )
										lowercase         =      tokenizer.encode(lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
										lowercase         =      tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
										self.assertNotEqual(lowerCAmelCase__		,    lowerCAmelCase__     )

										# Testing spaces after special tokens
										lowercase         =      "<mask>"
										tokenizer.add_special_tokens(
										    {"mask_token": AddedToken(lowerCAmelCase__		,    lstrip=lowerCAmelCase__		,    rstrip=lowerCAmelCase__     )}     )  # mask token has a left space
										lowercase         =      tokenizer.convert_tokens_to_ids(lowerCAmelCase__     )

										lowercase         =      "Encode <mask> sequence"
										lowercase         =      "Encode <mask>sequence"

										lowercase         =      tokenizer.encode(lowerCAmelCase__     )
										lowercase         =      encoded.index(lowerCAmelCase__     )
										lowercase         =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
										self.assertEqual(lowerCAmelCase__		,    lowerCAmelCase__     )

										lowercase         =      tokenizer.encode(lowerCAmelCase__     )
										lowercase         =      encoded.index(lowerCAmelCase__     )
										lowercase         =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
										self.assertNotEqual(lowerCAmelCase__		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Optional[Any]:
										pass





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Tuple:
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																		lowercase         =      self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__		,    **lowerCAmelCase__     )
																		lowercase         =      self.tokenizer_class.from_pretrained(lowerCAmelCase__		,    **lowerCAmelCase__     )
																		lowercase         =      "A, <mask> AllenNLP sentence."
																		lowercase         =      tokenizer_r.encode_plus(lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__		,    return_token_type_ids=lowerCAmelCase__     )
																		lowercase         =      tokenizer_p.encode_plus(lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__		,    return_token_type_ids=lowerCAmelCase__     )

																		# token_type_ids should put 0 everywhere
																		self.assertEqual(sum(tokens_r["token_type_ids"]     )		,    sum(tokens_p["token_type_ids"]     )     )

																		# attention_mask should put 1 everywhere, so sum over length should be 1
																		self.assertEqual(
																		    sum(tokens_r["attention_mask"]     ) / len(tokens_r["attention_mask"]     )		,    sum(tokens_p["attention_mask"]     ) / len(tokens_p["attention_mask"]     )		,    )

																		lowercase         =      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
																		lowercase         =      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]     )

																		# Rust correctly handles the space before the mask while python doesnt
																		self.assertSequenceEqual(tokens_p["input_ids"]		,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]     )
																		self.assertSequenceEqual(tokens_r["input_ids"]		,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]     )

																		self.assertSequenceEqual(
																		    lowerCAmelCase__		,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )
																		self.assertSequenceEqual(
																		    lowerCAmelCase__		,    ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]     )





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Optional[Any]:
										for trim_offsets, add_prefix_space in itertools.product([True, False]		,    repeat=2     ):
														lowercase         =      self.rust_tokenizer_class.from_pretrained(
														    self.tmpdirname		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )

														lowercase         =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
														lowercase         =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

														self.assertEqual(pre_tokenizer_state["add_prefix_space"]		,    lowerCAmelCase__     )

														self.assertEqual(post_processor_state["add_prefix_space"]		,    lowerCAmelCase__     )
														self.assertEqual(post_processor_state["trim_offsets"]		,    lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Any:
										# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
										# `trim_offsets`
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
																		lowercase         =      "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																		lowercase         =      F'''{text_of_1_token} {text_of_1_token}'''

																		lowercase         =      self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase__		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )
																		lowercase         =      tokenizer_r(lowerCAmelCase__		,    return_offsets_mapping=lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
																		self.assertEqual(encoding.offset_mapping[0]		,    (0, len(lowerCAmelCase__     ))     )
																		self.assertEqual(
																		    encoding.offset_mapping[1]		,    (len(lowerCAmelCase__     ) + 1, len(lowerCAmelCase__     ) + 1 + len(lowerCAmelCase__     ))		,    )

																		lowercase         =      self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase__		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )
																		lowercase         =      tokenizer_r(lowerCAmelCase__		,    return_offsets_mapping=lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
																		self.assertEqual(encoding.offset_mapping[0]		,    (0, len(lowerCAmelCase__     ))     )
																		self.assertEqual(
																		    encoding.offset_mapping[1]		,    (len(lowerCAmelCase__     ) + 1, len(lowerCAmelCase__     ) + 1 + len(lowerCAmelCase__     ))		,    )

																		lowercase         =      self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase__		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )
																		lowercase         =      tokenizer_r(lowerCAmelCase__		,    return_offsets_mapping=lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
																		self.assertEqual(encoding.offset_mapping[0]		,    (0, len(lowerCAmelCase__     ))     )
																		self.assertEqual(
																		    encoding.offset_mapping[1]		,    (len(lowerCAmelCase__     ), len(lowerCAmelCase__     ) + 1 + len(lowerCAmelCase__     ))		,    )

																		lowercase         =      self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase__		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )
																		lowercase         =      tokenizer_r(lowerCAmelCase__		,    return_offsets_mapping=lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
																		self.assertEqual(encoding.offset_mapping[0]		,    (0, len(lowerCAmelCase__     ))     )
																		self.assertEqual(
																		    encoding.offset_mapping[1]		,    (len(lowerCAmelCase__     ), len(lowerCAmelCase__     ) + 1 + len(lowerCAmelCase__     ))		,    )

																		lowercase         =      F''' {text}'''

																		# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																		#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																		# )
																		# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																		# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																		# self.assertEqual(
																		#     encoding.offset_mapping[1],
																		#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																		# )

																		lowercase         =      self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase__		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )
																		lowercase         =      tokenizer_r(lowerCAmelCase__		,    return_offsets_mapping=lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
																		self.assertEqual(encoding.offset_mapping[0]		,    (1, 1 + len(lowerCAmelCase__     ))     )
																		self.assertEqual(
																		    encoding.offset_mapping[1]		,    (1 + len(lowerCAmelCase__     ) + 1, 1 + len(lowerCAmelCase__     ) + 1 + len(lowerCAmelCase__     ))		,    )

																		lowercase         =      self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase__		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )
																		lowercase         =      tokenizer_r(lowerCAmelCase__		,    return_offsets_mapping=lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
																		self.assertEqual(encoding.offset_mapping[0]		,    (0, 1 + len(lowerCAmelCase__     ))     )
																		self.assertEqual(
																		    encoding.offset_mapping[1]		,    (1 + len(lowerCAmelCase__     ), 1 + len(lowerCAmelCase__     ) + 1 + len(lowerCAmelCase__     ))		,    )

																		lowercase         =      self.rust_tokenizer_class.from_pretrained(
																		    lowerCAmelCase__		,    use_fast=lowerCAmelCase__		,    add_prefix_space=lowerCAmelCase__		,    trim_offsets=lowerCAmelCase__     )
																		lowercase         =      tokenizer_r(lowerCAmelCase__		,    return_offsets_mapping=lowerCAmelCase__		,    add_special_tokens=lowerCAmelCase__     )
																		self.assertEqual(encoding.offset_mapping[0]		,    (0, 1 + len(lowerCAmelCase__     ))     )
																		self.assertEqual(
																		    encoding.offset_mapping[1]		,    (1 + len(lowerCAmelCase__     ), 1 + len(lowerCAmelCase__     ) + 1 + len(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
