



import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
				    SpeechaTextaDecoder,
				    SpeechaTextaForCausalLM,
				)

@require_torch
class 	__snake_case :







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_=99					,					a_=13					,					a_=16					,					a_=7					,					a_=True					,					a_=True					,					a_=True					,					a_=False					,					a_=True					,					a_=2					,					a_=32					,					a_=4					,					a_=4					,					a_=30					,					a_=0					,					a_=1					,					a_=2					,					a_=None					,					):
											a__			 =						parent
											a__			 =						batch_size
											a__			 =						decoder_seq_length
											# For common tests
											a__			 =						self.decoder_seq_length
											a__			 =						is_training
											a__			 =						use_attention_mask
											a__			 =						use_labels

											a__			 =						vocab_size
											a__			 =						d_model
											a__			 =						d_model
											a__			 =						decoder_layers
											a__			 =						decoder_layers
											a__			 =						decoder_ffn_dim
											a__			 =						decoder_attention_heads
											a__			 =						decoder_attention_heads
											a__			 =						eos_token_id
											a__			 =						bos_token_id
											a__			 =						pad_token_id
											a__			 =						decoder_start_token_id
											a__			 =						use_cache
											a__			 =						max_position_embeddings

											a__			 =						None
											a__			 =						decoder_seq_length
											a__			 =						2
											a__			 =						1


							def _a (							self						):
											a__			 =						ids_tensor([self.batch_size, self.decoder_seq_length]					,					self.vocab_size						)

											a__			 =						None
											if self.use_attention_mask:
															a__			 =						ids_tensor([self.batch_size, self.decoder_seq_length]					,					vocab_size=2						)

											a__			 =						None
											if self.use_labels:
															a__			 =						ids_tensor([self.batch_size, self.decoder_seq_length]					,					self.vocab_size						)

											a__			 =						SpeechaTextaConfig(
											    vocab_size=self.vocab_size					,					d_model=self.d_model					,					decoder_layers=self.decoder_layers					,					decoder_ffn_dim=self.decoder_ffn_dim					,					decoder_attention_heads=self.decoder_attention_heads					,					eos_token_id=self.eos_token_id					,					bos_token_id=self.bos_token_id					,					use_cache=self.use_cache					,					pad_token_id=self.pad_token_id					,					decoder_start_token_id=self.decoder_start_token_id					,					max_position_embeddings=self.max_position_embeddings					,					)

											return (
											    config,
											    input_ids,
											    attention_mask,
											    lm_labels,
											)


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					):
											a__			 =						True
											a__			 =						SpeechaTextaDecoder(config=a_						).to(a_						).eval()
											a__			 =						input_ids[:2]

											input_ids[input_ids == 0] += 1
											# first forward pass
											a__			 =						model(a_					,					use_cache=a_						)
											a__			 =						model(a_						)
											a__			 =						model(a_					,					use_cache=a_						)

											self.parent.assertTrue(len(a_						) == len(a_						)						)
											self.parent.assertTrue(len(a_						) == len(a_						) + 1						)

											a__			 =						outputs["""past_key_values"""]

											# create hypothetical next token and extent to next_input_ids
											a__			 =						ids_tensor((2, 1)					,					config.vocab_size - 1						) + 1

											# append to next input_ids and
											a__			 =						torch.cat([input_ids, next_tokens]					,					dim=-1						)

											print(a_						)

											a__			 =						model(a_						)["""last_hidden_state"""]
											a__			 =						model(a_					,					past_key_values=a_						)["""last_hidden_state"""]

											# select random slice
											a__			 =						ids_tensor((1,)					,					output_from_past.shape[-1]						).item()
											a__			 =						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
											a__			 =						output_from_past[:, 0, random_slice_idx].detach()

											# test that outputs are equal for slice
											assert torch.allclose(a_					,					a_					,					atol=1E-3						)







							def _a (							self						):
											a__			 =						self.prepare_config_and_inputs()
											(
											    (
											    a__  
											) ,     (
											    a__  
											) ,     (
											    a__  
											) ,     (
											    a__  
											) ,       
											)			 =						config_and_inputs

											a__			 =						{
											    """input_ids""": input_ids,
											    """attention_mask""": attention_mask,
											}
											return config, inputs_dict




@require_torch
class 	__snake_case (				_a     ,_a     ,_a     ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: Union[str, Any]             =   (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
							UpperCamelCase__			: int             =   (SpeechaTextaForCausalLM,) if is_torch_available() else ()
							UpperCamelCase__			: str             =   {"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
							UpperCamelCase__			: Optional[Any]             =   True
							UpperCamelCase__			: List[str]             =   False


							def _a (							self					,					):
											a__			 =						SpeechaTextaStandaloneDecoderModelTester(self					,					is_training=a_						)
											a__			 =						ConfigTester(self					,					config_class=a_						)


							def _a (							self						):
											pass


							def _a (							self						):
											pass


							def _a (							self						):
											pass


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


							def _a (							self						):
											a__			 =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_decoder_model_past(*a_						)







							def _a (							self						):
											return






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						gpt.pos_emb
				trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()						)
				trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()						)
				trajectory_transformer.head.load_state_dict(gpt.head.state_dict()						)

				for i, block in enumerate(gpt.blocks						):
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()						)
								trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()						)

								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()						)
								trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()						)
								trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()						)
								trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()						)

				torch.save(trajectory_transformer.state_dict()	,						"""pytorch_model.bin"""						)


if __name__ == "__main__":
	UpperCAmelCase								=   Parser().parse_args("""plan""")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)

