



import collections
import os
import re
from pathlib import Path


__A							       =		'''src/transformers'''


# Matches is_xxx_available()
__A							       =		re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
__A							       =		re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__A							       =		re.compile(r'''\s+\"\S*\":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
__A							       =		re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
__A							       =		re.compile(r'''^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__A							       =		re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
__A							       =		re.compile(r'''^\s+\"([^\"]+)\",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
__A							       =		re.compile(r'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
__A							       =		re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
__A							       =		re.compile(r'''^\s*try:''')
# Catches a line with else:
__A							       =		re.compile(r'''^\s*else:''')



def 			__a	(       lowerCAmelCase_  :   Dict							) ->					List[Any]:






						'''simple docstring'''



						if _re_test_backend.search(__A							) is None:
												return None
						UpperCAmelCase_=  [b[0] for b in _re_backend.findall(__A							)]
						backends.sort()
						return "_and_".join(__A							)



def 			__a	(       lowerCAmelCase_  :   Tuple							) ->					str:






						'''simple docstring'''



						with open(__A     ,"""r"""     ,encoding="""utf-8"""     ,newline="""\n"""							) as f:
												UpperCAmelCase_=  f.readlines()

						UpperCAmelCase_=  0
						while line_index < len(__A							) and not lines[line_index].startswith("""_import_structure = {"""							):
												line_index += 1

						# If this is a traditional init, just return.
						if line_index >= len(__A							):
												return None

						# First grab the objects without a specific backend in _import_structure
						UpperCAmelCase_=  []
						while not lines[line_index].startswith("""if TYPE_CHECKING"""							) and find_backend(lines[line_index]							) is None:
												UpperCAmelCase_=  lines[line_index]
												# If we have everything on a single line, let's deal with it.
												if _re_one_line_import_struct.search(__A							):
																		UpperCAmelCase_=  _re_one_line_import_struct.search(__A							).groups()[0]
																		UpperCAmelCase_=  re.findall(r"""\[([^\]]+)\]"""     ,__A							)
																		for imp in imports:
																								objects.extend([obj[1:-1] for obj in imp.split(""", """							)]							)
																		line_index += 1
																		continue
												UpperCAmelCase_=  _re_import_struct_key_value.search(__A							)
												if single_line_import_search is not None:
																		UpperCAmelCase_=  [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """							) if len(__A							) > 0]
																		objects.extend(__A							)
												elif line.startswith(""" """ * 8 + """\""""							):
																		objects.append(line[9:-3]							)
												line_index += 1

						UpperCAmelCase_=  {"""none""": objects}
						# Let's continue with backend-specific objects in _import_structure
						while not lines[line_index].startswith("""if TYPE_CHECKING"""							):
												# If the line is an if not is_backend_available, we grab all objects associated.
												UpperCAmelCase_=  find_backend(lines[line_index]							)
												# Check if the backend declaration is inside a try block:
												if _re_try.search(lines[line_index - 1]							) is None:
																		UpperCAmelCase_=  None

												if backend is not None:
																		line_index += 1

																		# Scroll until we hit the else block of try-except-else
																		while _re_else.search(lines[line_index]							) is None:
																								line_index += 1

																		line_index += 1

																		UpperCAmelCase_=  []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]							) <= 1 or lines[line_index].startswith(""" """ * 4							):
																								UpperCAmelCase_=  lines[line_index]
																								if _re_import_struct_add_one.search(__A							) is not None:
																														objects.append(_re_import_struct_add_one.search(__A							).groups()[0]							)
																								elif _re_import_struct_add_many.search(__A							) is not None:
																														UpperCAmelCase_=  _re_import_struct_add_many.search(__A							).groups()[0].split(""", """							)
																														UpperCAmelCase_=  [obj[1:-1] for obj in imports if len(__A							) > 0]
																														objects.extend(__A							)
																								elif _re_between_brackets.search(__A							) is not None:
																														UpperCAmelCase_=  _re_between_brackets.search(__A							).groups()[0].split(""", """							)
																														UpperCAmelCase_=  [obj[1:-1] for obj in imports if len(__A							) > 0]
																														objects.extend(__A							)
																								elif _re_quote_object.search(__A							) is not None:
																														objects.append(_re_quote_object.search(__A							).groups()[0]							)
																								elif line.startswith(""" """ * 8 + """\""""							):
																														objects.append(line[9:-3]							)
																								elif line.startswith(""" """ * 12 + """\""""							):
																														objects.append(line[13:-3]							)
																								line_index += 1

																		UpperCAmelCase_=  objects
												else:
																		line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
						UpperCAmelCase_=  []
						while (
						    line_index < len(__A							)
						    and find_backend(lines[line_index]							) is None
						    and not lines[line_index].startswith("""else"""							)
						):
												UpperCAmelCase_=  lines[line_index]
												UpperCAmelCase_=  _re_import.search(__A							)
												if single_line_import_search is not None:
																		objects.extend(single_line_import_search.groups()[0].split(""", """							)							)
												elif line.startswith(""" """ * 8							):
																		objects.append(line[8:-2]							)
												line_index += 1

						UpperCAmelCase_=  {"""none""": objects}
						# Let's continue with backend-specific objects
						while line_index < len(__A							):
												# If the line is an if is_backend_available, we grab all objects associated.
												UpperCAmelCase_=  find_backend(lines[line_index]							)
												# Check if the backend declaration is inside a try block:
												if _re_try.search(lines[line_index - 1]							) is None:
																		UpperCAmelCase_=  None

												if backend is not None:
																		line_index += 1

																		# Scroll until we hit the else block of try-except-else
																		while _re_else.search(lines[line_index]							) is None:
																								line_index += 1

																		line_index += 1

																		UpperCAmelCase_=  []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]							) <= 1 or lines[line_index].startswith(""" """ * 8							):
																								UpperCAmelCase_=  lines[line_index]
																								UpperCAmelCase_=  _re_import.search(__A							)
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(""", """							)							)
																								elif line.startswith(""" """ * 12							):
																														objects.append(line[12:-2]							)
																								line_index += 1

																		UpperCAmelCase_=  objects
												else:
																		line_index += 1

						return import_dict_objects, type_hint_objects



def 			__a	(       lowerCAmelCase_  :   Dict     ,lowerCAmelCase_  :   Dict							) ->					Optional[Any]:






						'''simple docstring'''



						def find_duplicates(lowerCAmelCase_  :   Tuple							):
												return [k for k, v in collections.Counter(__A							).items() if v > 1]

						if list(import_dict_objects.keys()							) != list(type_hint_objects.keys()							):
												return ["Both sides of the init do not have the same backends!"]

						UpperCAmelCase_=  []
						for key in import_dict_objects.keys():
												UpperCAmelCase_=  find_duplicates(import_dict_objects[key]							)
												if duplicate_imports:
																		errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""							)
												UpperCAmelCase_=  find_duplicates(type_hint_objects[key]							)
												if duplicate_type_hints:
																		errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""							)

												if sorted(set(import_dict_objects[key]							)							) != sorted(set(type_hint_objects[key]							)							):
																		UpperCAmelCase_=  """base imports""" if key == """none""" else F"""{key} backend"""
																		errors.append(F"""Differences for {name}:"""							)
																		for a in type_hint_objects[key]:
																								if a not in import_dict_objects[key]:
																														errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""							)
																		for a in import_dict_objects[key]:
																								if a not in type_hint_objects[key]:
																														errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""							)
						return errors



def 			__a	(       ) ->					Dict:






						'''simple docstring'''



						UpperCAmelCase_=  []
						for root, _, files in os.walk(__A							):
												if "__init__.py" in files:
																		UpperCAmelCase_=  os.path.join(__A     ,"""__init__.py"""							)
																		UpperCAmelCase_=  parse_init(__A							)
																		if objects is not None:
																								UpperCAmelCase_=  analyze_results(*__A							)
																								if len(__A							) > 0:
																														UpperCAmelCase_=  F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																														failures.append("""\n""".join(__A							)							)
						if len(__A							) > 0:
												raise ValueError("""\n\n""".join(__A							)							)



def 			__a	(       ) ->					Tuple:






						'''simple docstring'''



						UpperCAmelCase_=  []
						for path, directories, files in os.walk(__A							):
												for folder in directories:
																		# Ignore private modules
																		if folder.startswith("""_"""							):
																								directories.remove(__A							)
																								continue
																		# Ignore leftovers from branches (empty folders apart from pycache)
																		if len(list((Path(__A							) / folder).glob("""*.py"""							)							)							) == 0:
																								continue
																		UpperCAmelCase_=  str((Path(__A							) / folder).relative_to(__A							)							)
																		UpperCAmelCase_=  short_path.replace(os.path.sep     ,"""."""							)
																		submodules.append(__A							)
												for fname in files:
																		if fname == "__init__.py":
																								continue
																		UpperCAmelCase_=  str((Path(__A							) / fname).relative_to(__A							)							)
																		UpperCAmelCase_=  short_path.replace(""".py"""     ,""""""							).replace(os.path.sep     ,"""."""							)
																		if len(submodule.split("""."""							)							) == 1:
																								submodules.append(__A							)
						return submodules


__A							       =		[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]



def 			__a	(       ) ->					int:






						'''simple docstring'''



						from transformers.utils import direct_transformers_import

						UpperCAmelCase_=  direct_transformers_import(__A							)

						UpperCAmelCase_=  set(transformers._import_structure.keys()							)
						# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
						# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
						# (potentiall re-) add them.
						with open(os.path.join(__A     ,"""__init__.py"""							)     ,"""r"""							) as f:
												UpperCAmelCase_=  f.read()
						import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]"""     ,__A							)							)							)

						UpperCAmelCase_=  [
						    module
						    for module in get_transformers_submodules()
						    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
						]

						if len(__A							) > 0:
												UpperCAmelCase_=  """\n""".join(F"""- {module}""" for module in module_not_registered							)
												raise ValueError(
												    """The following submodules are not properly registed in the main init of Transformers:\n"""
												    F"""{list_of_modules}\n"""
												    """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""							)


if __name__ == "__main__":
	check_all_inits()
	check_submodules()









'''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     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=6					,  A=17					,  A=23					,  A=11					,  A=True					,  )       ->	Tuple:


      """simple docstring"""



      _a           =    parent
      _a           =    batch_size
      _a           =    seq_length
      _a           =    act_dim
      _a           =    state_dim
      _a           =    hidden_size
      _a           =    max_length
      _a           =    is_training



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    ids_tensor((self.batch_size, self.seq_length)					,  vocab_size=1_000			)
      _a           =    random_attention_mask((self.batch_size, self.seq_length)			)

      _a           =    self.get_config()

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



 def    a__    (self			)       ->	str:


      """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    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	List[Any]:


      """simple docstring"""



      _a           =    DecisionTransformerModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    model(A					,  A					,  A					,  A					,  A					,  A			)

      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			)       ->	Dict:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''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		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			(DecisionTransformerModel,) if is_torch_available() else ()
 __lowerCamelCase       :			List[str]		=			()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    DecisionTransformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a           =    DecisionTransformerModel.from_pretrained(A			)
           self.assertIsNotNone(A			)



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    2  # number of steps of autoregressive prediction we will perform
      _a           =    10  # defined by the RL environment, may be normalized
      _a           =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''			)
      _a           =    model.to(A			)
      _a           =    model.config
      torch.manual_seed(0			)
      _a           =    torch.randn(1					,  1					,  config.state_dim			).to(device=A					,  dtype=torch.floataa			)  # env.reset()

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

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

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

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

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

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

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



