

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

					_UpperCAmelCase						:  Dict									=			True
except ImportError:
					_UpperCAmelCase						:  Tuple									=			False

_UpperCAmelCase						:  Optional[int]									=			logging.get_logger(__name__)  # pylint: disable=invalid-name





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							return AddNewModelCommand(args.testing  ,				args.testing_file  ,				path=args.path )







class 					lowercase							(			lowercase_     ):




				@staticmethod
				def        a				(   snake_case			):
											snake_case_     							=       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=snake_case ,  help='Configuration file on which to run.'			)
											add_new_model_parser.add_argument(
											    '--path' ,  type=snake_case ,  help='Path to cookiecutter. Should only be used for testing purposes.'			)
											add_new_model_parser.set_defaults(func=snake_case			)




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case=None ,  *snake_case			):
											snake_case_     							=       testing
											snake_case_     							=       testing_file
											snake_case_     							=       path




				def        a				(   self			):
											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
											snake_case_     							=       [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
											if len(snake_case			) > 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.'			)

											snake_case_     							=       (
											    Path(snake_case			).parent.parent.parent.parent if self._path is None else Path(self._path			).parent.parent
											)
											snake_case_     							=       path_to_transformer_root / 'templates' / 'adding_a_new_model'

											# Execute cookiecutter
											if not self._testing:
																		cookiecutter(str(snake_case			)			)
											else:
																		with open(self._testing_file ,  'r'			) as configuration_file:
																									snake_case_     							=       json.load(snake_case			)

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

											snake_case_     							=       [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

											# Retrieve configuration
											with open(directory + '/configuration.json' ,  'r'			) as configuration_file:
																		snake_case_     							=       json.load(snake_case			)

											snake_case_     							=       configuration['lowercase_modelname']
											snake_case_     							=       configuration['generate_tensorflow_pytorch_and_flax']
											os.remove(F'''{directory}/configuration.json'''			)

											snake_case_     							=       'PyTorch' in generate_tensorflow_pytorch_and_flax
											snake_case_     							=       'TensorFlow' in generate_tensorflow_pytorch_and_flax
											snake_case_     							=       'Flax' in generate_tensorflow_pytorch_and_flax

											snake_case_     							=       F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
											os.makedirs(snake_case ,  exist_ok=snake_case			)
											os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}''' ,  exist_ok=snake_case			)

											# 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			):
																		with open(snake_case ,  'r'			) as f:
																									snake_case_     							=       f.readlines()
																		with open(snake_case ,  'w'			) as f:
																									for line in lines:
																																if "# Copied from transformers." not in line:
																																							f.write(snake_case			)

											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 ,  snake_case ,  snake_case			):
																		# Create temp file
																		snake_case_							,       snake_case_     							=       mkstemp()
																		snake_case_     							=       False
																		with fdopen(snake_case ,  'w'			) as new_file:
																									with open(snake_case			) as old_file:
																																for line in old_file:
																																							new_file.write(snake_case			)
																																							if line_to_copy_below in line:
																																														snake_case_     							=       True
																																														for line_to_copy in lines_to_copy:
																																																					new_file.write(snake_case			)

																		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(snake_case ,  snake_case			)
																		# Remove original file
																		remove(snake_case			)
																		# Move new file
																		move(snake_case ,  snake_case			)

											def skip_units(snake_case			):
																		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			):
																		with open(snake_case			) as datafile:
																									snake_case_     							=       []
																									snake_case_     							=       False
																									snake_case_     							=       False
																									for line in datafile:
																																if "# To replace in: " in line and "##" not in line:
																																							snake_case_     							=       line.split('"'			)[1]
																																							snake_case_     							=       skip_units(snake_case			)
																																elif "# Below: " in line and "##" not in line:
																																							snake_case_     							=       line.split('"'			)[1]
																																							snake_case_     							=       skip_units(snake_case			)
																																elif "# End." in line and "##" not in line:
																																							if not skip_file and not skip_snippet:
																																														replace(snake_case ,  snake_case ,  snake_case			)

																																							snake_case_     							=       []
																																elif "# Replace with" in line and "##" not in line:
																																							snake_case_     							=       []
																																elif "##" not in line:
																																							lines_to_copy.append(snake_case			)

																		remove(snake_case			)

											replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py'''			)
											os.rmdir(snake_case			)







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


_UpperCAmelCase						:  List[Any]									=			{
    """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__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

					_UpperCAmelCase						:  Tuple									=			ValueGuidedRLPipeline.from_pretrained(
					    """bglick13/hopper-medium-v2-value-function-hor32""",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase						:  int									=			pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			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())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

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




