


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case							:       Dict     			=      logging.get_logger(__name__)






def 							__lowercase   ( __lowerCAmelCase							:				Optional[Any]					,    __lowerCAmelCase							:				Dict					,    __lowerCAmelCase							:				str   ):
							a__             =     os.path.abspath(__lowerCAmelCase   )
							logger.info(F'Converting TensorFlow checkpoint from {tf_path}'   )
							# Load weights from TF model
							a__             =     tf.train.list_variables(__lowerCAmelCase   )
							a__             =     []
							a__             =     []
							a__             =     []
							for full_name, shape in init_vars:
														# logger.info(f"Loading TF weight {name} with shape {shape}")
														a__             =     full_name.split('/'   )
														if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																					logger.info(F'Skipping non-model layer {full_name}'   )
																					continue
														if "optimizer" in full_name:
																					logger.info(F'Skipping optimization layer {full_name}'   )
																					continue
														if name[0] == "model":
																					# ignore initial 'model'
																					a__             =     name[1:]
														# figure out how many levels deep the name is
														a__             =     0
														for _name in name:
																					if _name.startswith('layer_with_weights'   ):
																												depth += 1
																					else:
																												break
														layer_depth.append(__lowerCAmelCase   )
														# read data
														a__             =     tf.train.load_variable(__lowerCAmelCase					,    __lowerCAmelCase   )
														names.append('/'.join(__lowerCAmelCase   )   )
														arrays.append(__lowerCAmelCase   )
							logger.info(F'Read a total of {len(__lowerCAmelCase   ):,} layers'   )

							# Sanity check
							if len(set(__lowerCAmelCase   )   ) != 1:
														raise ValueError(F'Found layer names with different depths (layer depth {list(set(__lowerCAmelCase   )   )})'   )
							a__             =     list(set(__lowerCAmelCase   )   )[0]
							if layer_depth != 1:
														raise ValueError(
														    'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
														    ' heads.'   )

							# convert layers
							logger.info('Converting weights...'   )
							for full_name, array in zip(__lowerCAmelCase					,    __lowerCAmelCase   ):
														a__             =     full_name.split('/'   )
														a__             =     model
														a__             =     []
														for i, m_name in enumerate(__lowerCAmelCase   ):
																					if m_name == ".ATTRIBUTES":
																												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																												break
																					if m_name.startswith('layer_with_weights'   ):
																												a__             =     int(m_name.split('-'   )[-1]   )
																												if layer_num <= 2:
																																			# embedding layers
																																			# layer_num 0: word_embeddings
																																			# layer_num 1: position_embeddings
																																			# layer_num 2: token_type_embeddings
																																			continue
																												elif layer_num == 3:
																																			# embedding LayerNorm
																																			trace.extend(['embeddings', 'LayerNorm']   )
																																			a__             =     getattr(__lowerCAmelCase					,    'embeddings'   )
																																			a__             =     getattr(__lowerCAmelCase					,    'LayerNorm'   )
																												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																																			# encoder layers
																																			trace.extend(['encoder', 'layer', str(layer_num - 4   )]   )
																																			a__             =     getattr(__lowerCAmelCase					,    'encoder'   )
																																			a__             =     getattr(__lowerCAmelCase					,    'layer'   )
																																			a__             =     pointer[layer_num - 4]
																												elif layer_num == config.num_hidden_layers + 4:
																																			# pooler layer
																																			trace.extend(['pooler', 'dense']   )
																																			a__             =     getattr(__lowerCAmelCase					,    'pooler'   )
																																			a__             =     getattr(__lowerCAmelCase					,    'dense'   )
																					elif m_name == "embeddings":
																												trace.append('embeddings'   )
																												a__             =     getattr(__lowerCAmelCase					,    'embeddings'   )
																												if layer_num == 0:
																																			trace.append('word_embeddings'   )
																																			a__             =     getattr(__lowerCAmelCase					,    'word_embeddings'   )
																												elif layer_num == 1:
																																			trace.append('position_embeddings'   )
																																			a__             =     getattr(__lowerCAmelCase					,    'position_embeddings'   )
																												elif layer_num == 2:
																																			trace.append('token_type_embeddings'   )
																																			a__             =     getattr(__lowerCAmelCase					,    'token_type_embeddings'   )
																												else:
																																			raise ValueError(F'Unknown embedding layer with name {full_name}'   )
																												trace.append('weight'   )
																												a__             =     getattr(__lowerCAmelCase					,    'weight'   )
																					elif m_name == "_attention_layer":
																												# self-attention layer
																												trace.extend(['attention', 'self']   )
																												a__             =     getattr(__lowerCAmelCase					,    'attention'   )
																												a__             =     getattr(__lowerCAmelCase					,    'self'   )
																					elif m_name == "_attention_layer_norm":
																												# output attention norm
																												trace.extend(['attention', 'output', 'LayerNorm']   )
																												a__             =     getattr(__lowerCAmelCase					,    'attention'   )
																												a__             =     getattr(__lowerCAmelCase					,    'output'   )
																												a__             =     getattr(__lowerCAmelCase					,    'LayerNorm'   )
																					elif m_name == "_attention_output_dense":
																												# output attention dense
																												trace.extend(['attention', 'output', 'dense']   )
																												a__             =     getattr(__lowerCAmelCase					,    'attention'   )
																												a__             =     getattr(__lowerCAmelCase					,    'output'   )
																												a__             =     getattr(__lowerCAmelCase					,    'dense'   )
																					elif m_name == "_output_dense":
																												# output dense
																												trace.extend(['output', 'dense']   )
																												a__             =     getattr(__lowerCAmelCase					,    'output'   )
																												a__             =     getattr(__lowerCAmelCase					,    'dense'   )
																					elif m_name == "_output_layer_norm":
																												# output dense
																												trace.extend(['output', 'LayerNorm']   )
																												a__             =     getattr(__lowerCAmelCase					,    'output'   )
																												a__             =     getattr(__lowerCAmelCase					,    'LayerNorm'   )
																					elif m_name == "_key_dense":
																												# attention key
																												trace.append('key'   )
																												a__             =     getattr(__lowerCAmelCase					,    'key'   )
																					elif m_name == "_query_dense":
																												# attention query
																												trace.append('query'   )
																												a__             =     getattr(__lowerCAmelCase					,    'query'   )
																					elif m_name == "_value_dense":
																												# attention value
																												trace.append('value'   )
																												a__             =     getattr(__lowerCAmelCase					,    'value'   )
																					elif m_name == "_intermediate_dense":
																												# attention intermediate dense
																												trace.extend(['intermediate', 'dense']   )
																												a__             =     getattr(__lowerCAmelCase					,    'intermediate'   )
																												a__             =     getattr(__lowerCAmelCase					,    'dense'   )
																					elif m_name == "_output_layer_norm":
																												# output layer norm
																												trace.append('output'   )
																												a__             =     getattr(__lowerCAmelCase					,    'output'   )
																					# weights & biases
																					elif m_name in ["bias", "beta"]:
																												trace.append('bias'   )
																												a__             =     getattr(__lowerCAmelCase					,    'bias'   )
																					elif m_name in ["kernel", "gamma"]:
																												trace.append('weight'   )
																												a__             =     getattr(__lowerCAmelCase					,    'weight'   )
																					else:
																												logger.warning(F'Ignored {m_name}'   )
        # for certain layers reshape is necessary
														a__             =     '.'.join(__lowerCAmelCase   )
														if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'					,    __lowerCAmelCase   ) or re.match(
														    R'(\S+)\.attention\.output\.dense\.weight'					,    __lowerCAmelCase   ):
																					a__             =     array.reshape(pointer.data.shape   )
														if "kernel" in full_name:
																					a__             =     array.transpose()
														if pointer.shape == array.shape:
																					a__             =     torch.from_numpy(__lowerCAmelCase   )
														else:
																					raise ValueError(
																					    F'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
																					    F' {array.shape}'   )
														logger.info(F'Successfully set variable {full_name} to PyTorch layer {trace}'   )
							return model






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				Any   ):
							logger.info(F'Loading model based on config from {config_path}...'   )
							a__             =     BertConfig.from_json_file(__lowerCAmelCase   )
							a__             =     BertModel(__lowerCAmelCase   )

							# Load weights from checkpoint
							logger.info(F'Loading weights from checkpoint {tf_checkpoint_path}...'   )
							load_tfa_weights_in_bert(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase   )

							# Save pytorch-model
							logger.info(F'Saving PyTorch model to {pytorch_dump_path}...'   )
							torch.save(model.state_dict()					,    __lowerCAmelCase   )


if __name__ == "__main__":
						snake_case							:       Dict     			=      argparse.ArgumentParser()
						parser.add_argument(
						    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
						)
						parser.add_argument(
						    '''--bert_config_file''',
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''',
						    type=str,
						    required=True,
						    help='''Path to the output PyTorch model (must include filename).''',
						)
						snake_case							:       Union[str, Any]     			=      parser.parse_args()
						convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








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:	Tuple    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Any=13    ,						UpperCamelCase_:	Optional[Any]=7    ,						UpperCamelCase_:	Optional[Any]=6    ,						UpperCamelCase_:	Any=17    ,						UpperCamelCase_:	str=23    ,						UpperCamelCase_:	List[Any]=11    ,						UpperCamelCase_:	Optional[int]=True    ,						)			->   List[Any]:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Tuple:





									"""simple docstring"""







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

									lowercase__            =      self.get_config()

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

							def       lowerCamelCase_ (							self:	Dict							)			->   List[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       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int    ,						)			->   Dict:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModel(config=UpperCamelCase_							)
									model.to(UpperCamelCase_							)
									model.eval()
									lowercase__            =      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:	List[Any]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.prepare_config_and_inputs()
									(
									    (
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		
									)            =      config_and_inputs
									lowercase__            =      {
									    '''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							(			UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			str        = (DecisionTransformerModel,) if is_torch_available() else ()
							_lowercase	:			List[str]        = ()
							_lowercase	:			List[Any]        = {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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

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





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModelTester(self							)
									lowercase__            =      ConfigTester(self    ,						config_class=UpperCamelCase_    ,						hidden_size=37							)

							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									self.config_tester.run_common_tests()

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





									"""simple docstring"""







									lowercase__            =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase_							)

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





									"""simple docstring"""







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


							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

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

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

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


@require_torch
class        _a							(			unittest.TestCase			):

							@slow
							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







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

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

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

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

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

											with torch.no_grad():
													lowercase__     ,		lowercase__     ,		lowercase__            =      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							)							)
											lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      (  # env.step(action)
											    torch.randn(1    ,						1    ,						config.state_dim							).to(device=UpperCamelCase_    ,						dtype=torch.floataa							),
											    1.0,
											    False,
											    {},
											)

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


