





"""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 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase=13		,     _UpperCAmelCase=7		,     _UpperCAmelCase=6		,     _UpperCAmelCase=17		,     _UpperCAmelCase=23		,     _UpperCAmelCase=11		,     _UpperCAmelCase=True		,     ):
									__a	:  List[Any]         =					parent
									__a	:  Optional[int]         =					batch_size
									__a	:  Any         =					seq_length
									__a	:  str         =					act_dim
									__a	:  str         =					state_dim
									__a	:  Optional[int]         =					hidden_size
									__a	:  List[Any]         =					max_length
									__a	:  int         =					is_training







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

									__a	:  Any         =					self.get_config()

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







					def 	_lowerCamelCase						(  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 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     _UpperCAmelCase		,     ):
									__a	:  Any         =					DecisionTransformerModel(config=_UpperCAmelCase       )
									model.to(_UpperCAmelCase       )
									model.eval()
									__a	:  List[str]         =					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 	_lowerCamelCase						(  self       ):
									__a	:  str         =					self.prepare_config_and_inputs()
									(
									    (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  (
									    __a
									)    ,  
									)	:  str         =					config_and_inputs
									__a	:  List[str]         =					{
									    '''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     (     _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(DecisionTransformerModel,) if is_torch_available() else ()
					__lowerCAmelCase					=					()
					__lowerCAmelCase					=					{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					DecisionTransformerModelTester(self       )
									__a	:  Optional[Any]         =					ConfigTester(self		,     config_class=_UpperCAmelCase		,     hidden_size=37       )







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  List[Any]         =					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase       )







					@slow
					def 	_lowerCamelCase						(  self       ):
									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__a	:  List[str]         =					DecisionTransformerModel.from_pretrained(_UpperCAmelCase       )
													self.assertIsNotNone(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  Optional[int]         =					self.model_tester.prepare_config_and_inputs_for_common()

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

													__a	:  Any         =					[
													    '''states''',
													    '''actions''',
													    '''rewards''',
													    '''returns_to_go''',
													    '''timesteps''',
													    '''attention_mask''',
													]

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






@require_torch
class 			__lowercase     (     unittest.TestCase							):







					'''simple docstring'''




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

									__a	:  List[str]         =					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=_UpperCAmelCase       )

									__a	:  Any         =					torch.tensor(_UpperCAmelCase		,     device=_UpperCAmelCase		,     dtype=torch.floataa       ).reshape(1		,     1		,     1       )
									__a	:  int         =					state
									__a	:  Tuple         =					torch.zeros(1		,     0		,     config.act_dim		,     device=_UpperCAmelCase		,     dtype=torch.floataa       )
									__a	:  Dict         =					torch.zeros(1		,     0		,     device=_UpperCAmelCase		,     dtype=torch.floataa       )
									__a	:  int         =					torch.tensor(0		,     device=_UpperCAmelCase		,     dtype=torch.long       ).reshape(1		,     1       )

									for step in range(_UpperCAmelCase       ):
													__a	:  Any         =					torch.cat([actions, torch.zeros(1		,     1		,     config.act_dim		,     device=_UpperCAmelCase       )]		,     dim=1       )
													__a	:  Optional[Any]         =					torch.cat([rewards, torch.zeros(1		,     1		,     device=_UpperCAmelCase       )]		,     dim=1       )

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

													with torch.no_grad():
																	__a    ,  __a    ,  __a	:  int         =					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       )       )
													__a    ,  __a    ,  __a    ,  __a	:  List[Any]         =					(  # env.step(action)
													    torch.randn(1		,     1		,     config.state_dim       ).to(device=_UpperCAmelCase		,     dtype=torch.floataa       ),
													    1.0,
													    False,
													    {},
													)

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






"""simple docstring"""






import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A	     =					'''src/transformers'''
A	     =					'''docs/source/en/tasks'''

def       __A (     a_  :List[Any]				,    a_  :List[Any]				,    a_  :List[str])						->						List[str]:
				with open(a_				,    '''r'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
								__a	:  List[str]         =					f.readlines()
				# Find the start prompt.
				__a	:  Optional[Any]         =					0
				while not lines[start_index].startswith(a_):
								start_index += 1
				start_index += 1

				__a	:  int         =					start_index
				while not lines[end_index].startswith(a_):
								end_index += 1
				end_index -= 1

				while len(lines[start_index]) <= 1:
								start_index += 1
				while len(lines[end_index]) <= 1:
								end_index -= 1
				end_index += 1
				return "".join(lines[start_index:end_index]), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A	     =					direct_transformers_import(TRANSFORMERS_PATH)

A	     =					{
    '''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    '''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    '''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    '''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    '''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    '''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    '''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    '''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    '''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    '''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    '''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    '''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    '''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    '''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    '''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A	     =					{
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}

def       __A (     a_  :Optional[Any])						->						Any:
				__a	:  List[Any]         =					TASK_GUIDE_TO_MODELS[task_guide]
				__a	:  List[Any]         =					SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(a_				,    set())
				__a	:  Any         =					{
				    code: name
				    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
				    if (code in model_maping_names or code in special_model_types)
				}
				return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()]) + "\n"

def       __A (     a_  :Optional[int]				,    a_  :Dict=False)						->						Any:
				__a    ,  __a    ,  __a    ,  __a	:  Any         =					_find_text_in_file(
				    filename=os.path.join(a_				,    a_)				,    start_prompt='''<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'''				,    end_prompt='''<!--End of the generated tip-->'''				,    )

				__a	:  Optional[Any]         =					get_model_list_for_task(a_)

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(a_				,    a_)				,    '''w'''				,    encoding='''utf-8'''				,    newline='''\n''') as f:
																f.writelines(lines[:start_index] + [new_list] + lines[end_index:])
								else:
												raise ValueError(
												    F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
												    ''' to fix this.''')


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		A	     =					parser.parse_args()

		for task_guide in TASK_GUIDE_TO_MODELS.keys():
				check_model_list_for_task(task_guide, args.fix_and_overwrite)