
import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import DecisionTransformerModel
			from transformers.models.decision_transformer.modeling_decision_transformer import (
			    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=13				,				_UpperCamelCase=7				,				_UpperCamelCase=6				,				_UpperCamelCase=17				,				_UpperCamelCase=23				,				_UpperCamelCase=11				,				_UpperCamelCase=True				,				)				->      Optional[Any]:
							lowerCAmelCase_						=	parent
							lowerCAmelCase_						=	batch_size
							lowerCAmelCase_						=	seq_length
							lowerCAmelCase_						=	act_dim
							lowerCAmelCase_						=	state_dim
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	max_length
							lowerCAmelCase_						=	is_training




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	floats_tensor((self.batch_size, self.seq_length, self.state_dim)     )
							lowerCAmelCase_						=	floats_tensor((self.batch_size, self.seq_length, self.act_dim)     )
							lowerCAmelCase_						=	floats_tensor((self.batch_size, self.seq_length, 1)     )
							lowerCAmelCase_						=	floats_tensor((self.batch_size, self.seq_length, 1)     )
							lowerCAmelCase_						=	ids_tensor((self.batch_size, self.seq_length)				,				vocab_size=1_000     )
							lowerCAmelCase_						=	random_attention_mask((self.batch_size, self.seq_length)     )

							lowerCAmelCase_						=	self.get_config()

							return (
							    config,
							    states,
							    actions,
							    rewards,
							    returns_to_go,
							    timesteps,
							    attention_mask,
							)




				def      __a						(					self     )				->      int:
							return DecisionTransformerConfig(
							    batch_size=self.batch_size				,				seq_length=self.seq_length				,				act_dim=self.act_dim				,				state_dim=self.state_dim				,				hidden_size=self.hidden_size				,				max_length=self.max_length				,				)




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				)				->      Union[str, Any]:
							lowerCAmelCase_						=	DecisionTransformerModel(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )

							self.parent.assertEqual(result.state_preds.shape				,				states.shape     )
							self.parent.assertEqual(result.action_preds.shape				,				actions.shape     )
							self.parent.assertEqual(result.return_preds.shape				,				returns_to_go.shape     )
							self.parent.assertEqual(
							    result.last_hidden_state.shape				,				(self.batch_size, self.seq_length * 3, self.hidden_size)     )  # seq length *3 as there are 3 modelities: states, returns and actions




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  (
							    lowerCAmelCase_
							)	,  
							)						=	config_and_inputs
							lowerCAmelCase_						=	{
							    "states": states,
							    "actions": actions,
							    "rewards": rewards,
							    "returns_to_go": returns_to_go,
							    "timesteps": timesteps,
							    "attention_mask": attention_mask,
							}
							return config, inputs_dict





@require_torch
class 	_lowerCAmelCase					(	__a					,      __a					,      __a					,      unittest.TestCase       ):
				_lowercase					=(DecisionTransformerModel,) if is_torch_available() else ()
				_lowercase					=()
				_lowercase					={'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

				# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
				_lowercase					=False

				# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	DecisionTransformerModelTester(self     )
							lowerCAmelCase_						=	ConfigTester(self				,				config_class=_UpperCamelCase				,				hidden_size=37     )




				def      __a						(					self     )				->      Optional[int]:
							self.config_tester.run_common_tests()




				def      __a						(					self     )				->      int:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase     )




				@slow
				def      __a						(					self     )				->      Dict:
							for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_						=	DecisionTransformerModel.from_pretrained(_UpperCamelCase     )
										self.assertIsNotNone(_UpperCamelCase     )




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_	,  lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowerCAmelCase_						=	model_class(_UpperCamelCase     )
										lowerCAmelCase_						=	inspect.signature(model.forward     )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase_						=	[*signature.parameters.keys()]

										lowerCAmelCase_						=	[
										    "states",
										    "actions",
										    "rewards",
										    "returns_to_go",
										    "timesteps",
										    "attention_mask",
										]

										self.assertListEqual(arg_names[: len(_UpperCamelCase     )]				,				_UpperCamelCase     )





@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):




				@slow
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	2  # number of steps of autoregressive prediction we will perform
							lowerCAmelCase_						=	10  # defined by the RL environment, may be normalized
							lowerCAmelCase_						=	DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert"     )
							lowerCAmelCase_						=	model.to(_UpperCamelCase     )
							lowerCAmelCase_						=	model.config
							torch.manual_seed(0     )
							lowerCAmelCase_						=	torch.randn(1				,				1				,				config.state_dim     ).to(device=_UpperCamelCase				,				dtype=torch.floataa     )  # env.reset()

							lowerCAmelCase_						=	torch.tensor(
							    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]				,				device=_UpperCamelCase     )

							lowerCAmelCase_						=	torch.tensor(_UpperCamelCase				,				device=_UpperCamelCase				,				dtype=torch.floataa     ).reshape(1				,				1				,				1     )
							lowerCAmelCase_						=	state
							lowerCAmelCase_						=	torch.zeros(1				,				0				,				config.act_dim				,				device=_UpperCamelCase				,				dtype=torch.floataa     )
							lowerCAmelCase_						=	torch.zeros(1				,				0				,				device=_UpperCamelCase				,				dtype=torch.floataa     )
							lowerCAmelCase_						=	torch.tensor(0				,				device=_UpperCamelCase				,				dtype=torch.long     ).reshape(1				,				1     )

							for step in range(_UpperCamelCase     ):
										lowerCAmelCase_						=	torch.cat([actions, torch.zeros(1				,				1				,				config.act_dim				,				device=_UpperCamelCase     )]				,				dim=1     )
										lowerCAmelCase_						=	torch.cat([rewards, torch.zeros(1				,				1				,				device=_UpperCamelCase     )]				,				dim=1     )

										lowerCAmelCase_						=	torch.ones(1				,				states.shape[1]     ).to(dtype=torch.long				,				device=states.device     )

										with torch.no_grad():
													lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	model(
													    states=_UpperCamelCase				,				actions=_UpperCamelCase				,				rewards=_UpperCamelCase				,				returns_to_go=_UpperCamelCase				,				timesteps=_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				return_dict=_UpperCamelCase				,				)

										self.assertEqual(action_pred.shape				,				actions.shape     )
										self.assertTrue(torch.allclose(action_pred[0, -1]				,				expected_outputs[step]				,				atol=1e-4     )     )
										lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_	,  lowerCAmelCase_						=	(  # env.step(action)
										    torch.randn(1				,				1				,				config.state_dim     ).to(device=_UpperCamelCase				,				dtype=torch.floataa     ),
										    1.0,
										    False,
										    {},
										)

										lowerCAmelCase_						=	action_pred[0, -1]
										lowerCAmelCase_						=	torch.cat([states, state]				,				dim=1     )
										lowerCAmelCase_						=	returns_to_go[0, -1] - reward
										lowerCAmelCase_						=	torch.cat([returns_to_go, pred_return.reshape(1				,				1				,				1     )]				,				dim=1     )
										lowerCAmelCase_						=	torch.cat(
										    [timesteps, torch.ones((1, 1)				,				device=_UpperCamelCase				,				dtype=torch.long     ) * (step + 1)]				,				dim=1     )





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 	_lowerCAmelCase					(	__a					,      unittest.TestCase       ):
				_lowercase					=CLIPTokenizer
				_lowercase					=CLIPTokenizerFast
				_lowercase					=True
				_lowercase					={}
				_lowercase					=False




				def      __a						(					self     )				->      Dict:
							super().setUp()

							# fmt: off
							lowerCAmelCase_						=	["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
							lowerCAmelCase_						=	dict(zip(_UpperCamelCase				,				range(len(_UpperCamelCase     )     )     )     )
							lowerCAmelCase_						=	["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
							lowerCAmelCase_						=	{"unk_token": "<unk>"}

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




				def      __a						(					self				,				**_UpperCamelCase     )				->      Any:
							kwargs.update(self.special_tokens_map     )
							return CLIPTokenizer.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      int:
							kwargs.update(self.special_tokens_map     )
							return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	"lower newer"
							lowerCAmelCase_						=	"lower newer"
							return input_text, output_text




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	CLIPTokenizer(self.vocab_file				,				self.merges_file				,				**self.special_tokens_map     )
							lowerCAmelCase_						=	"lower newer"
							lowerCAmelCase_						=	["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
							lowerCAmelCase_						=	tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

							lowerCAmelCase_						=	tokens + [tokenizer.unk_token]
							lowerCAmelCase_						=	[10, 2, 16, 9, 3, 2, 16, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )				,				_UpperCamelCase     )




				@require_ftfy
				def      __a						(					self     )				->      Union[str, Any]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
													lowerCAmelCase_						=	self.tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )
													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,				**_UpperCamelCase     )

													lowerCAmelCase_						=	"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
													lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
													lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

													self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

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

													self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on unicode of space type
													lowerCAmelCase_						=	[
													    "\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:
																lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
																lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

																self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )

													# Test that the tokenization is identical on unicode of line break type
													lowerCAmelCase_						=	[
													    "\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:
																lowerCAmelCase_						=	tokenizer_s.tokenize(_UpperCamelCase     )
																lowerCAmelCase_						=	tokenizer_r.tokenize(_UpperCamelCase     )

																self.assertListEqual(_UpperCamelCase				,				_UpperCamelCase     )




				def      __a						(					self     )				->      str:
							# 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})"""     ):
													lowerCAmelCase_						=	"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													lowerCAmelCase_						=	f"""{text_of_1_token} {text_of_1_token}"""

													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
													    _UpperCamelCase				,				use_fast=_UpperCamelCase				,				)
													lowerCAmelCase_						=	tokenizer_r(_UpperCamelCase				,				return_offsets_mapping=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     )
													self.assertEqual(encoding.offset_mapping[0]				,				(0, len(_UpperCamelCase     ))     )
													self.assertEqual(
													    encoding.offset_mapping[1]				,				(len(_UpperCamelCase     ) + 1, len(_UpperCamelCase     ) + 1 + len(_UpperCamelCase     ))				,				)

													lowerCAmelCase_						=	f""" {text}"""

													lowerCAmelCase_						=	self.rust_tokenizer_class.from_pretrained(
													    _UpperCamelCase				,				use_fast=_UpperCamelCase				,				)
													lowerCAmelCase_						=	tokenizer_r(_UpperCamelCase				,				return_offsets_mapping=_UpperCamelCase				,				add_special_tokens=_UpperCamelCase     )
													self.assertEqual(encoding.offset_mapping[0]				,				(1, 1 + len(_UpperCamelCase     ))     )
													self.assertEqual(
													    encoding.offset_mapping[1]				,				(1 + len(_UpperCamelCase     ) + 1, 1 + len(_UpperCamelCase     ) + 1 + len(_UpperCamelCase     ))				,				)




				def      __a						(					self     )				->      Optional[Any]:
							# 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(_UpperCamelCase     ) 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      __a						(					self     )				->      str:
							super().test_tokenization_python_rust_equals()




				def      __a						(					self     )				->      Any:
							# CLIP always lower cases letters
							pass



