






"""simple docstring"""



import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
	from cookiecutter.main import cookiecutter

	A__	:    Any			 = True
except ImportError:
	A__	:    Optional[Any]			 = False

A__	:    List[str]			 = logging.get_logger(__name__)  # pylint: disable=invalid-name

def 			_snake_case    (					lowerCamelCase__     :   Namespace  )    ->     Dict:
							return AddNewModelCommand(args.testing    ,						args.testing_file    ,						path=args.path  )







class        lowercase__      (							snake_case__       ):


			@staticmethod
			def      UpperCAmelCase__						(				snake_case__					:  List[str] ):
										lowerCamelCase_	:							Any									=parser.add_parser("add-new-model" )
										add_new_model_parser.add_argument("--testing"       ,	action="store_true"       ,	help="If in testing mode." )
										add_new_model_parser.add_argument("--testing_file"       ,	type=_a       ,	help="Configuration file on which to run." )
										add_new_model_parser.add_argument(
										    "--path"       ,	type=_a       ,	help="Path to cookiecutter. Should only be used for testing purposes." )
										add_new_model_parser.set_defaults(func=_a )


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None       ,	*snake_case__					:  List[Any] ):
										lowerCamelCase_	:							Dict									=testing
										lowerCamelCase_	:							Tuple									=testing_file
										lowerCamelCase_	:							int									=path


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										warnings.warn(
										    "The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
										    "It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
										    "checks, you should use `transformers-cli add-new-model-like` instead." )
										if not _has_cookiecutter:
																	raise ImportError(
																	    "Model creation dependencies are required to use the `add_new_model` command. Install them by running "
																	    "the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" )
										# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
										lowerCamelCase_	:							Any									=[directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
										if len(_a ) > 0:
																	raise ValueError(
																	    "Several directories starting with `cookiecutter-template-` in current working directory. "
																	    "Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
																	    "change your working directory." )

										lowerCamelCase_	:							Tuple									=(
										    Path(_a ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
										)
										lowerCamelCase_	:							List[Any]									=path_to_transformer_root / "templates" / "adding_a_new_model"

										# Execute cookiecutter
										if not self._testing:
																	cookiecutter(str(_a ) )
										else:
																	with open(self._testing_file       ,	"r" ) as configuration_file:
																								lowerCamelCase_	:							int									=json.load(_a )

																	cookiecutter(
																	    str(path_to_cookiecutter if self._path is None else self._path )       ,	no_input=_a       ,	extra_context=_a       ,	)

										lowerCamelCase_	:							Optional[Any]									=[directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

										# Retrieve configuration
										with open(directory + "/configuration.json"       ,	"r" ) as configuration_file:
																	lowerCamelCase_	:							Any									=json.load(_a )

										lowerCamelCase_	:							Tuple									=configuration["lowercase_modelname"]
										lowerCamelCase_	:							Optional[Any]									=configuration["generate_tensorflow_pytorch_and_flax"]
										os.remove(F"""{directory}/configuration.json""" )

										lowerCamelCase_	:							List[Any]									="PyTorch" in generate_tensorflow_pytorch_and_flax
										lowerCamelCase_	:							int									="TensorFlow" in generate_tensorflow_pytorch_and_flax
										lowerCamelCase_	:							List[Any]									="Flax" in generate_tensorflow_pytorch_and_flax

										lowerCamelCase_	:							Tuple									=F"""{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}"""
										os.makedirs(_a       ,	exist_ok=_a )
										os.makedirs(F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}"""       ,	exist_ok=_a )

										# Tests require submodules as they have parent imports
										with open(F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py"""       ,	"w" ):
																	pass

										shutil.move(
										    F"""{directory}/__init__.py"""       ,	F"""{model_dir}/__init__.py"""       ,	)
										shutil.move(
										    F"""{directory}/configuration_{lowercase_model_name}.py"""       ,	F"""{model_dir}/configuration_{lowercase_model_name}.py"""       ,	)

										def remove_copy_lines(snake_case__					:  Any ):
																	with open(_a       ,	"r" ) as f:
																								lowerCamelCase_	:							int									=f.readlines()
																	with open(_a       ,	"w" ) as f:
																								for line in lines:
																															if "# Copied from transformers." not in line:
																																						f.write(_a )

										if output_pytorch:
																	if not self._testing:
																								remove_copy_lines(F"""{directory}/modeling_{lowercase_model_name}.py""" )

																	shutil.move(
																	    F"""{directory}/modeling_{lowercase_model_name}.py"""       ,	F"""{model_dir}/modeling_{lowercase_model_name}.py"""       ,	)

																	shutil.move(
																	    F"""{directory}/test_modeling_{lowercase_model_name}.py"""       ,	F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py"""       ,	)
										else:
																	os.remove(F"""{directory}/modeling_{lowercase_model_name}.py""" )
																	os.remove(F"""{directory}/test_modeling_{lowercase_model_name}.py""" )

										if output_tensorflow:
																	if not self._testing:
																								remove_copy_lines(F"""{directory}/modeling_tf_{lowercase_model_name}.py""" )

																	shutil.move(
																	    F"""{directory}/modeling_tf_{lowercase_model_name}.py"""       ,	F"""{model_dir}/modeling_tf_{lowercase_model_name}.py"""       ,	)

																	shutil.move(
																	    F"""{directory}/test_modeling_tf_{lowercase_model_name}.py"""       ,	F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py"""       ,	)
										else:
																	os.remove(F"""{directory}/modeling_tf_{lowercase_model_name}.py""" )
																	os.remove(F"""{directory}/test_modeling_tf_{lowercase_model_name}.py""" )

										if output_flax:
																	if not self._testing:
																								remove_copy_lines(F"""{directory}/modeling_flax_{lowercase_model_name}.py""" )

																	shutil.move(
																	    F"""{directory}/modeling_flax_{lowercase_model_name}.py"""       ,	F"""{model_dir}/modeling_flax_{lowercase_model_name}.py"""       ,	)

																	shutil.move(
																	    F"""{directory}/test_modeling_flax_{lowercase_model_name}.py"""       ,	F"""{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py"""       ,	)
										else:
																	os.remove(F"""{directory}/modeling_flax_{lowercase_model_name}.py""" )
																	os.remove(F"""{directory}/test_modeling_flax_{lowercase_model_name}.py""" )

										shutil.move(
										    F"""{directory}/{lowercase_model_name}.md"""       ,	F"""{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md"""       ,	)

										shutil.move(
										    F"""{directory}/tokenization_{lowercase_model_name}.py"""       ,	F"""{model_dir}/tokenization_{lowercase_model_name}.py"""       ,	)

										shutil.move(
										    F"""{directory}/tokenization_fast_{lowercase_model_name}.py"""       ,	F"""{model_dir}/tokenization_{lowercase_model_name}_fast.py"""       ,	)

										from os import fdopen, remove
										from shutil import copymode, move
										from tempfile import mkstemp

										def replace(snake_case__					:  Union[str, Any]       ,	snake_case__					:  int       ,	snake_case__					:  Union[str, Any] ):
																	# Create temp file
																	lowerCamelCase_	:							List[str]									=mkstemp()
																	lowerCamelCase_	:							Tuple									=False
																	with fdopen(_a       ,	"w" ) as new_file:
																								with open(_a ) as old_file:
																															for line in old_file:
																																						new_file.write(_a )
																																						if line_to_copy_below in line:
																																													lowerCamelCase_	:							str									=True
																																													for line_to_copy in lines_to_copy:
																																																				new_file.write(_a )

																	if not line_found:
																								raise ValueError(F"""Line {line_to_copy_below} was not found in file.""" )

																	# Copy the file permissions from the old file to the new file
																	copymode(_a       ,	_a )
																	# Remove original file
																	remove(_a )
																	# Move new file
																	move(_a       ,	_a )

										def skip_units(snake_case__					:  List[str] ):
																	return (
																	    ("generating PyTorch" in line and not output_pytorch)
																	    or ("generating TensorFlow" in line and not output_tensorflow)
																	    or ("generating Flax" in line and not output_flax)
																	)

										def replace_in_files(snake_case__					:  Optional[Any] ):
																	with open(_a ) as datafile:
																								lowerCamelCase_	:							Tuple									=[]
																								lowerCamelCase_	:							str									=False
																								lowerCamelCase_	:							Tuple									=False
																								for line in datafile:
																															if "# To replace in: " in line and "##" not in line:
																																						lowerCamelCase_	:							int									=line.split("\"" )[1]
																																						lowerCamelCase_	:							Optional[Any]									=skip_units(_a )
																															elif "# Below: " in line and "##" not in line:
																																						lowerCamelCase_	:							Any									=line.split("\"" )[1]
																																						lowerCamelCase_	:							str									=skip_units(_a )
																															elif "# End." in line and "##" not in line:
																																						if not skip_file and not skip_snippet:
																																													replace(_a       ,	_a       ,	_a )

																																						lowerCamelCase_	:							Optional[Any]									=[]
																															elif "# Replace with" in line and "##" not in line:
																																						lowerCamelCase_	:							Dict									=[]
																															elif "##" not in line:
																																						lines_to_copy.append(_a )

																	remove(_a )

										replace_in_files(F"""{directory}/to_replace_{lowercase_model_name}.py""" )
										os.rmdir(_a )











"""simple docstring"""



import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A__	:    int			 = {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
						A__	:    str			 = 'hopper-medium-v2'
						A__	:    Dict			 = gym.make(env_name)

						A__	:    List[Any]			 = ValueGuidedRLPipeline.from_pretrained(
						    'bglick13/hopper-medium-v2-value-function-hor32',
						    env=env,
						)

						env.seed(0)
						A__	:    Dict			 = env.reset()
						A__	:    Optional[int]			 = 0
						A__	:    str			 = 0
						A__	:    List[Any]			 = 1_000
						A__	:    Tuple			 = [obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__	:    Union[str, Any]			 = pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__ ,						A__ ,						A__ ,						A__	:    Any			 = env.step(denorm_actions)
																		A__	:    List[Any]			 = env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
																		    f' {total_score}'
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		A__	:    Optional[Any]			 = next_observation
						except KeyboardInterrupt:
												pass

						print(f'Total reward: {total_reward}')



