"""simple docstring"""




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 		__A							:






     def __init__(	self       ,  a__       ,  a__=13       ,  a__=7       ,  a__=6       ,  a__=17       ,  a__=23       ,  a__=11       ,  a__=True       ,  ):
       _lowerCAmelCase		: int						    =  parent
       _lowerCAmelCase		: Union[str, Any]						    =  batch_size
       _lowerCAmelCase		: Optional[int]						    =  seq_length
       _lowerCAmelCase		: Optional[int]						    =  act_dim
       _lowerCAmelCase		: Any						    =  state_dim
       _lowerCAmelCase		: Tuple						    =  hidden_size
       _lowerCAmelCase		: Dict						    =  max_length
       _lowerCAmelCase		: int						    =  is_training






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
       _lowerCAmelCase		: Any						    =  floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
       _lowerCAmelCase		: Dict						    =  floats_tensor((self.batch_size, self.seq_length, 1)						)
       _lowerCAmelCase		: Any						    =  floats_tensor((self.batch_size, self.seq_length, 1)						)
       _lowerCAmelCase		: Any						    =  ids_tensor((self.batch_size, self.seq_length)       ,  vocab_size=1000						)
       _lowerCAmelCase		: List[str]						    =  random_attention_mask((self.batch_size, self.seq_length)						)

       _lowerCAmelCase		: str						    =  self.get_config()

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






     def 					__A						(	self						):
       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       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  ):
       _lowerCAmelCase		: Any						    =  DecisionTransformerModel(config=snake_case__						)
       model.to(snake_case__						)
       model.eval()
       _lowerCAmelCase		: List[Any]						    =  model(snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__						)

       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						):
       _lowerCAmelCase		: List[Any]						    =  self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase						
       )    ,			(
           _lowerCAmelCase						
       )    ,			(
           _lowerCAmelCase						
       )    ,			(
           _lowerCAmelCase						
       )    ,			(
           _lowerCAmelCase						
       )    ,			(
           _lowerCAmelCase						
       )    ,			(
           _lowerCAmelCase						
       )    ,									
       )		: Optional[Any]						    =  config_and_inputs
       _lowerCAmelCase		: Dict						    =  {
           """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 		__A							(				__a					,			__a					,			__a					,			unittest.TestCase	):
     _UpperCamelCase							:    Optional[Any]	      =	(DecisionTransformerModel,) if is_torch_available() else ()
     _UpperCamelCase							:    List[str]	      =	()
     _UpperCamelCase							:    Optional[int]	      =	{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

     # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
     _UpperCamelCase							:    Optional[int]	      =	False

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  DecisionTransformerModelTester(self						)
       _lowerCAmelCase		: Tuple						    =  ConfigTester(self       ,  config_class=snake_case__       ,  hidden_size=37						)






     def 					__A						(	self						):
       self.config_tester.run_common_tests()






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case__						)






     @slow
     def 					__A						(	self						):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase		: Optional[Any]						    =  DecisionTransformerModel.from_pretrained(snake_case__						)
         self.assertIsNotNone(snake_case__						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase		: List[str]						    =  model_class(snake_case__						)
         _lowerCAmelCase		: Optional[Any]						    =  inspect.signature(model.forward						)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase		: str						    =  [*signature.parameters.keys()]

         _lowerCAmelCase		: Dict						    =  [
             """states""",
             """actions""",
             """rewards""",
             """returns_to_go""",
             """timesteps""",
             """attention_mask""",
         ]

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







@require_torch
class 		__A							(				unittest.TestCase	):






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

       _lowerCAmelCase		: List[Any]						    =  torch.tensor(
           [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]       ,  device=snake_case__						)

       _lowerCAmelCase		: List[Any]						    =  torch.tensor(snake_case__       ,  device=snake_case__       ,  dtype=torch.floataa						).reshape(1       ,  1       ,  1						)
       _lowerCAmelCase		: int						    =  state
       _lowerCAmelCase		: List[str]						    =  torch.zeros(1       ,  0       ,  config.act_dim       ,  device=snake_case__       ,  dtype=torch.floataa						)
       _lowerCAmelCase		: Dict						    =  torch.zeros(1       ,  0       ,  device=snake_case__       ,  dtype=torch.floataa						)
       _lowerCAmelCase		: Union[str, Any]						    =  torch.tensor(0       ,  device=snake_case__       ,  dtype=torch.long						).reshape(1       ,  1						)

       for step in range(snake_case__						):
         _lowerCAmelCase		: List[Any]						    =  torch.cat([actions, torch.zeros(1       ,  1       ,  config.act_dim       ,  device=snake_case__						)]       ,  dim=1						)
         _lowerCAmelCase		: Union[str, Any]						    =  torch.cat([rewards, torch.zeros(1       ,  1       ,  device=snake_case__						)]       ,  dim=1						)

         _lowerCAmelCase		: List[Any]						    =  torch.ones(1       ,  states.shape[1]						).to(dtype=torch.long       ,  device=states.device						)

         with torch.no_grad():
           _lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Optional[Any]						    =  model(
               states=snake_case__       ,  actions=snake_case__       ,  rewards=snake_case__       ,  returns_to_go=snake_case__       ,  timesteps=snake_case__       ,  attention_mask=snake_case__       ,  return_dict=snake_case__       ,  )

         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		: Tuple						    =  (  # env.step(action)
             torch.randn(1       ,  1       ,  config.state_dim						).to(device=snake_case__       ,  dtype=torch.floataa						),
             1.0,
             False,
             {},
         )

         _lowerCAmelCase		: Union[str, Any]						    =  action_pred[0, -1]
         _lowerCAmelCase		: List[Any]						    =  torch.cat([states, state]       ,  dim=1						)
         _lowerCAmelCase		: List[str]						    =  returns_to_go[0, -1] - reward
         _lowerCAmelCase		: Tuple						    =  torch.cat([returns_to_go, pred_return.reshape(1       ,  1       ,  1						)]       ,  dim=1						)
         _lowerCAmelCase		: Any						    =  torch.cat(
             [timesteps, torch.ones((1, 1)       ,  device=snake_case__       ,  dtype=torch.long						) * (step + 1)]       ,  dim=1						)





"""simple docstring"""




import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizer
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizerFast
     _UpperCamelCase							:    Tuple	      =	True
     _UpperCamelCase							:    List[Any]	      =	{"add_prefix_space": True}
     _UpperCamelCase							:    str	      =	False






     def 					__A						(	self						):
       super().setUp()

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

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






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  """lower newer"""
       _lowerCAmelCase		: Tuple						    =  """lower newer"""
       return input_text, output_text






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  CodeGenTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map						)
       _lowerCAmelCase		: int						    =  """lower newer"""
       _lowerCAmelCase		: List[str]						    =  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       self.assertListEqual(a__       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokens + [tokenizer.unk_token]
       _lowerCAmelCase		: List[str]						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self						):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizer()
       _lowerCAmelCase		: Optional[int]						    =  self.get_rust_tokenizer(add_prefix_space=a__						)

       _lowerCAmelCase		: Any						    =  """lower newer"""

       # Testing tokenization
       _lowerCAmelCase		: Any						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: int						    =  rust_tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids without special tokens
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.encode(a__       ,  add_special_tokens=a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Dict						    =  rust_tokenizer.encode(a__       ,  add_special_tokens=a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids with special tokens
       _lowerCAmelCase		: int						    =  self.get_rust_tokenizer(add_prefix_space=a__						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.encode(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Any						    =  rust_tokenizer.encode(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing the unknown token
       _lowerCAmelCase		: List[str]						    =  tokens + [rust_tokenizer.unk_token]
       _lowerCAmelCase		: Dict						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self       ,  *a__       ,  **a__						):
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass






     def 					__A						(	self       ,  a__=15						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
           _lowerCAmelCase		: List[Any]						    =  self.rust_tokenizer_class.from_pretrained(a__       ,  **a__						)

           # Simple input
           _lowerCAmelCase		: Dict						    =  """This is a simple input"""
           _lowerCAmelCase		: Optional[int]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]
           _lowerCAmelCase		: Optional[int]						    =  ("""This is a simple input""", """This is a pair""")
           _lowerCAmelCase		: str						    =  [
               ("""This is a simple input 1""", """This is a simple input 2"""),
               ("""This is a simple pair 1""", """This is a simple pair 2"""),
           ]

           # Simple input tests
           self.assertRaises(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""						)

       # Simple input
       _lowerCAmelCase		: Union[str, Any]						    =  """This is a simple input"""
       _lowerCAmelCase		: Dict						    =  ["""This is a simple input looooooooong""", """This is a simple input"""]
       _lowerCAmelCase		: Any						    =  ("""This is a simple input""", """This is a pair""")
       _lowerCAmelCase		: Optional[int]						    =  [
           ("""This is a simple input loooooong""", """This is a simple input"""),
           ("""This is a simple pair loooooong""", """This is a simple pair"""),
       ]

       _lowerCAmelCase		: Optional[int]						    =  tokenizer.pad_token_id

       _lowerCAmelCase		: Any						    =  tokenizer(a__       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: str						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(*a__       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)

       # s
       # test single string max_length padding
       self.assertEqual(out_s["""input_ids"""].shape[-1]       ,  30						)
       self.assertTrue(pad_token_id in out_s["""input_ids"""]						)
       self.assertTrue(0 in out_s["""attention_mask"""]						)

       # s2
       # test automatic padding
       self.assertEqual(out_sa["""input_ids"""].shape[-1]       ,  33						)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]						)
       self.assertFalse(0 in out_sa["""attention_mask"""][0]						)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]						)
       self.assertTrue(0 in out_sa["""attention_mask"""][1]						)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p["""input_ids"""].shape[-1]       ,  60						)
       self.assertTrue(pad_token_id in out_p["""input_ids"""]						)
       self.assertTrue(0 in out_p["""attention_mask"""]						)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa["""input_ids"""].shape[-1]       ,  52						)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]						)
       self.assertFalse(0 in out_pa["""attention_mask"""][0]						)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]						)
       self.assertTrue(0 in out_pa["""attention_mask"""][1]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  """$$$"""
       _lowerCAmelCase		: str						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=a__       ,  add_bos_token=a__						)

       _lowerCAmelCase		: Tuple						    =  """This is a simple input"""
       _lowerCAmelCase		: Union[str, Any]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]

       _lowerCAmelCase		: List[str]						    =  tokenizer.bos_token_id

       _lowerCAmelCase		: str						    =  tokenizer(a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer(a__						)

       self.assertEqual(out_s.input_ids[0]       ,  a__						)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.decode(out_s.input_ids						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.batch_decode(out_sa.input_ids						)

       self.assertEqual(decode_s.split()[0]       ,  a__						)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""						)

       _lowerCAmelCase		: Optional[int]						    =  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
       _lowerCAmelCase		: List[Any]						    =  """\nif len_a > len_b:      result = a\nelse:      result = b"""

       _lowerCAmelCase		: Tuple						    =  tokenizer.encode(a__						)
       _lowerCAmelCase		: Optional[Any]						    =  ["""^#""", re.escape("""<|endoftext|>"""						), """^'''""", """^\"\"\"""", """\n\n\n"""]
       _lowerCAmelCase		: int						    =  tokenizer.decode(a__       ,  truncate_before_pattern=a__						)
       self.assertEqual(a__       ,  a__						)






     def 					__A						(	self						):
       pass




