
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     lowercase :



  def __init__(			self    ,				A_    ,				A_=13    ,				A_=7    ,				A_=6    ,				A_=17    ,				A_=23    ,				A_=11    ,				A_=True    ,				)  -> List[Any]:
    """simple docstring"""
    UpperCamelCase          =					parent
    UpperCamelCase          =					batch_size
    UpperCamelCase          =					seq_length
    UpperCamelCase          =					act_dim
    UpperCamelCase          =					state_dim
    UpperCamelCase          =					hidden_size
    UpperCamelCase          =					max_length
    UpperCamelCase          =					is_training



  def __UpperCamelCase			(			self	)  -> Optional[Any]:
    """simple docstring"""
    UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, self.state_dim)	)
    UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, self.act_dim)	)
    UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, 1)	)
    UpperCamelCase          =					floats_tensor((self.batch_size, self.seq_length, 1)	)
    UpperCamelCase          =					ids_tensor((self.batch_size, self.seq_length)    ,				vocab_size=1_000	)
    UpperCamelCase          =					random_attention_mask((self.batch_size, self.seq_length)	)

    UpperCamelCase          =					self.get_config()

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



  def __UpperCamelCase			(			self	)  -> Any:
    """simple docstring"""
    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 __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				)  -> Optional[int]:
    """simple docstring"""
    UpperCamelCase          =					DecisionTransformerModel(config=_lowerCamelCase	)
    model.to(_lowerCamelCase	)
    model.eval()
    UpperCamelCase          =					model(_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase	)

    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 __UpperCamelCase			(			self	)  -> Optional[Any]:
    """simple docstring"""
    UpperCamelCase          =					self.prepare_config_and_inputs()
    (
        UpperCamelCase    
    )          =					config_and_inputs
    UpperCamelCase          =					{
        '''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     lowercase (		a__  ,       a__  ,       a__  ,       unittest.TestCase       ):
  __lowercase	:	Dict     					=    (DecisionTransformerModel,) if is_torch_available() else ()
  __lowercase	:	Tuple     					=    ()
  __lowercase	:	str     					=    {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

  # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
  __lowercase	:	List[str]     					=    False

  # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
  __lowercase	:	int     					=    False
  __lowercase	:	Optional[int]     					=    False
  __lowercase	:	Optional[int]     					=    False
  __lowercase	:	List[Any]     					=    False
  __lowercase	:	Tuple     					=    False
  __lowercase	:	str     					=    False
  __lowercase	:	int     					=    False
  __lowercase	:	List[str]     					=    False
  __lowercase	:	List[Any]     					=    False



  def __UpperCamelCase			(			self	)  -> Dict:
    """simple docstring"""
    UpperCamelCase          =					DecisionTransformerModelTester(self	)
    UpperCamelCase          =					ConfigTester(self    ,				config_class=_lowerCamelCase    ,				hidden_size=37	)



  def __UpperCamelCase			(			self	)  -> List[Any]:
    """simple docstring"""
    self.config_tester.run_common_tests()



  def __UpperCamelCase			(			self	)  -> Optional[Any]:
    """simple docstring"""
    UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowerCamelCase	)



  @slow
  def __UpperCamelCase			(			self	)  -> Optional[Any]:
    """simple docstring"""
    for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      UpperCamelCase          =					DecisionTransformerModel.from_pretrained(_lowerCamelCase	)
      self.assertIsNotNone(_lowerCamelCase	)



  def __UpperCamelCase			(			self	)  -> Optional[Any]:
    """simple docstring"""
    UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

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

      UpperCamelCase          =					[
          '''states''',
          '''actions''',
          '''rewards''',
          '''returns_to_go''',
          '''timesteps''',
          '''attention_mask''',
      ]

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






@require_torch
class     lowercase (		unittest.TestCase       ):



  @slow
  def __UpperCamelCase			(			self	)  -> Optional[int]:
    """simple docstring"""
    UpperCamelCase          =					2  # number of steps of autoregressive prediction we will perform
    UpperCamelCase          =					10  # defined by the RL environment, may be normalized
    UpperCamelCase          =					DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-expert'	)
    UpperCamelCase          =					model.to(_lowerCamelCase	)
    UpperCamelCase          =					model.config
    torch.manual_seed(0	)
    UpperCamelCase          =					torch.randn(1    ,				1    ,				config.state_dim	).to(device=_lowerCamelCase    ,				dtype=torch.floataa	)  # env.reset()

    UpperCamelCase          =					torch.tensor(
        [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]    ,				device=_lowerCamelCase	)

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

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

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

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

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

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


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase :			Union[str, Any]              =	get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")


@require_sentencepiece
@require_tokenizers
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    GPTSwaTokenizer
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Union[str, Any]     					=    True
		__lowercase	:	Tuple     					=    False



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCamelCase          =					GPTSwaTokenizer(A_    ,				eos_token='<unk>'    ,				bos_token='<unk>'    ,				pad_token='<unk>'	)

				tokenizer.save_pretrained(self.tmpdirname	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'This is a test'
				UpperCamelCase          =					'This is a test'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					'<s>'
				UpperCamelCase          =					1

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



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					list(self.get_tokenizer().get_vocab().keys()	)

				self.assertEqual(vocab_keys[0]    ,				'<unk>'	)
				self.assertEqual(vocab_keys[1]    ,				'<s>'	)
				self.assertEqual(vocab_keys[-1]    ,				'j'	)
				self.assertEqual(len(A_	)    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)

				UpperCamelCase          =					tokenizer.tokenize('This is a test'	)
				self.assertListEqual(A_    ,				['▁This', '▁is', '▁a', '▁t', 'est']	)

				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				[465, 287, 265, 631, 842]	)

				UpperCamelCase          =					tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']    ,				)
				# fmt: on

				UpperCamelCase          =					tokenizer.convert_tokens_to_ids(A_	)
				self.assertListEqual(
				    A_    ,				[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]    ,				)

				UpperCamelCase          =					tokenizer.convert_ids_to_tokens(A_	)
				# fmt: off
				self.assertListEqual(
				    A_    ,				['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']	)
				# fmt: on



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)
				UpperCamelCase          =					['This is a test', 'I was born in 92000, and this is falsé.']
				UpperCamelCase          =					[
				    [465, 287, 265, 631, 842],
				    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
				]

				# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
				for text, expected_ids in zip(A_    ,				A_	):
						self.assertListEqual(tokenizer.encode_fast(A_	)    ,				A_	)

				# Test that decode_fast returns the input text
				for text, token_ids in zip(A_    ,				A_	):
						self.assertEqual(tokenizer.decode_fast(A_	)    ,				A_	)



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[
				    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
				    'Hey there, how are you doing this fine day?',
				    'This is a text with a trailing spaces followed by a dot     .',
				    'Häj sväjs lillebrör! =)',
				    'Det är inget fel på Mr. Cool',
				]

				# fmt: off
				UpperCamelCase          =					{'input_ids': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[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, 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]], '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, 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, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
				# fmt: on
				self.tokenizer_integration_test_util(
				    expected_encoding=A_    ,				model_name='AI-Sweden/gpt-sw3-126m'    ,				sequences=A_    ,				)
