

import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Optional[Any]      							=					CLIPTokenizer
			_UpperCAmelCase       :							Tuple      							=					CLIPTokenizerFast
			_UpperCAmelCase       :							Optional[Any]      							=					True
			_UpperCAmelCase       :							Tuple      							=					{}
			_UpperCAmelCase       :							Tuple      							=					False



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									super().setUp()

									# fmt: off
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
									# fmt: on
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	dict(zip(lowerCAmelCase__       ,						range(len(lowerCAmelCase__))))
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{"unk_token": "<unk>"}

									SCREAMING_SNAKE_CASE_:			Any  		=	os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_:			List[str]  		=	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  :					Dict       ,						**lowerCAmelCase__  :					List[str]):
									kwargs.update(self.special_tokens_map)
									return CLIPTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						**lowerCAmelCase__  :					Tuple):
									kwargs.update(self.special_tokens_map)
									return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	"lower newer"
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	"lower newer"
									return input_text, output_text



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Any  		=	CLIPTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	"lower newer"
									SCREAMING_SNAKE_CASE_:			int  		=	["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.tokenize(lowerCAmelCase__)
									self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokens + [tokenizer.unk_token]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[10, 2, 16, 9, 3, 2, 16, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__)       ,						lowerCAmelCase__)



			@require_ftfy
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.tokenizer_class.from_pretrained(lowerCAmelCase__       ,						**lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__       ,						**lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_:			str  		=	"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
																					SCREAMING_SNAKE_CASE_:			Dict  		=	tokenizer_s.tokenize(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer_r.tokenize(lowerCAmelCase__)

																					self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

																					# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																					# with Tilde) encoded in 2 different ways
																					SCREAMING_SNAKE_CASE_:			Any  		=	"xa\u0303y" + " " + "x\xe3y"
																					SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer_s.tokenize(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:			Any  		=	tokenizer_r.tokenize(lowerCAmelCase__)

																					self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

																					# Test that the tokenization is identical on unicode of space type
																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[
																					    "\u0009",  # (horizontal tab, '\t')
																					    "\u000B",  # (vertical tab)
																					    "\u000C",  # (form feed)
																					    "\u0020",  # (space, ' ')
																					    "\u200E",  # (left-to-right mark):w
																					    "\u200F",  # (right-to-left mark)
																					]
																					for unicode_seq in spaces_unicodes:
																											SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer_s.tokenize(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer_r.tokenize(lowerCAmelCase__)

																											self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)

																					# Test that the tokenization is identical on unicode of line break type
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	[
																					    "\u000A",  # (line feed, '\n')
																					    "\r\n",  # (carriage return and line feed, '\r\n')
																					    "\u000D",  # (carriage return, '\r')
																					    "\r",  # (carriage return, '\r')
																					    "\u000D",  # (carriage return, '\r')
																					    "\u2028",  # (line separator)
																					    "\u2029",  # (paragraph separator)
																					    # "\u0085", # (next line)
																					]

																					# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																					# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																					# space (and thus into an empty list).

																					for unicode_seq in line_break_unicodes:
																											SCREAMING_SNAKE_CASE_:			List[Any]  		=	tokenizer_s.tokenize(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer_r.tokenize(lowerCAmelCase__)

																											self.assertListEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
																					SCREAMING_SNAKE_CASE_:			str  		=	"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	F"{text_of_1_token} {text_of_1_token}"

																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.rust_tokenizer_class.from_pretrained(
																					    lowerCAmelCase__       ,						use_fast=lowerCAmelCase__       ,						)
																					SCREAMING_SNAKE_CASE_:			int  		=	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__))       ,						)

																					SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	F" {text}"

																					SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.rust_tokenizer_class.from_pretrained(
																					    lowerCAmelCase__       ,						use_fast=lowerCAmelCase__       ,						)
																					SCREAMING_SNAKE_CASE_:			int  		=	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__))       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									# Test related to the breaking change introduced in transformers v4.17.0
									# We need to check that an error in raised when the user try to load a previous version of the tokenizer.
									with self.assertRaises(lowerCAmelCase__) as context:
															self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer")

									self.assertTrue(
									    context.exception.args[0].startswith(
									        "The `backend_tokenizer` provided does not match the expected format."))



			@require_ftfy
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									super().test_tokenization_python_rust_equals()




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# CLIP always lower cases letters
									pass









import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowerCAmelCase							:  Tuple            =						{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
						lowerCAmelCase							:  Optional[int]            =						"""hopper-medium-v2"""
						lowerCAmelCase							:  Optional[int]            =						gym.make(env_name)

						lowerCAmelCase							:  Optional[int]            =						ValueGuidedRLPipeline.from_pretrained(
						    """bglick13/hopper-medium-v2-value-function-hor32""",
						    env=env,
						)

						env.seed(0)
						lowerCAmelCase							:  Optional[Any]            =						env.reset()
						lowerCAmelCase							:  Union[str, Any]            =						0
						lowerCAmelCase							:  Optional[int]            =						0
						lowerCAmelCase							:  Optional[int]            =						1000
						lowerCAmelCase							:  Dict            =						[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		lowerCAmelCase							:  Union[str, Any]            =						pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase     ,      lowerCAmelCase							:  Dict            =						env.step(denorm_actions)
																		lowerCAmelCase							:  Tuple            =						env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
																		    f''' {total_score}'''
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		lowerCAmelCase							:  Tuple            =						next_observation
						except KeyboardInterrupt:
												pass

						print(f'''Total reward: {total_reward}''')






