




import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


lowerCamelCase__					:  int      							=				datasets.utils.logging.get_logger(__name__)

lowerCamelCase__					:  str      							=				['names', 'prefix']
lowerCamelCase__					:  List[Any]      							=				['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCamelCase__					:  Optional[int]      							=				['encoding_errors', 'on_bad_lines']
lowerCamelCase__					:  str      							=				['date_format']





@dataclass
class 			lowerCamelCase_				( datasets.BuilderConfig   ):


 '''simple docstring'''







 lowercase_							       =       ","
 lowercase_							       =       None
 lowercase_							       =       "infer"
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       True
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       False
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       True
 lowercase_							       =       True
 lowercase_							       =       False
 lowercase_							       =       True
 lowercase_							       =       None
 lowercase_							       =       "."
 lowercase_							       =       None
 lowercase_							       =       '"'
 lowercase_							       =       0
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       None
 lowercase_							       =       True
 lowercase_							       =       True
 lowercase_							       =       0
 lowercase_							       =       True
 lowercase_							       =       False
 lowercase_							       =       None
 lowercase_							       =       10_000
 lowercase_							       =       None
 lowercase_							       =       "strict"
 lowercase_							       =       "error"
 lowercase_							       =       None



 def  lowerCAmelCase_    (				self    :						Any		):
       if self.delimiter is not None:
             SCREAMING_SNAKE_CASE_      		=     self.delimiter
       if self.column_names is not None:
             SCREAMING_SNAKE_CASE_      		=     self.column_names




 @property
 def  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     {
           'sep': self.sep,
           'header': self.header,
           'names': self.names,
           'index_col': self.index_col,
           'usecols': self.usecols,
           'prefix': self.prefix,
           'mangle_dupe_cols': self.mangle_dupe_cols,
           'engine': self.engine,
           'converters': self.converters,
           'true_values': self.true_values,
           'false_values': self.false_values,
           'skipinitialspace': self.skipinitialspace,
           'skiprows': self.skiprows,
           'nrows': self.nrows,
           'na_values': self.na_values,
           'keep_default_na': self.keep_default_na,
           'na_filter': self.na_filter,
           'verbose': self.verbose,
           'skip_blank_lines': self.skip_blank_lines,
           'thousands': self.thousands,
           'decimal': self.decimal,
           'lineterminator': self.lineterminator,
           'quotechar': self.quotechar,
           'quoting': self.quoting,
           'escapechar': self.escapechar,
           'comment': self.comment,
           'encoding': self.encoding,
           'dialect': self.dialect,
           'error_bad_lines': self.error_bad_lines,
           'warn_bad_lines': self.warn_bad_lines,
           'skipfooter': self.skipfooter,
           'doublequote': self.doublequote,
           'memory_map': self.memory_map,
           'float_precision': self.float_precision,
           'chunksize': self.chunksize,
           'encoding_errors': self.encoding_errors,
           'on_bad_lines': self.on_bad_lines,
           'date_format': self.date_format,
       }

       # some kwargs must not be passed if they don't have a default value
       # some others are deprecated and we can also not pass them if they are the default value
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
             if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()							,							_lowerCAmelCase		):
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 2):
             for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
       if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
             for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                   del pd_read_csv_kwargs[pd_read_csv_parameter]

       return pd_read_csv_kwargs





class 			lowerCamelCase_				( datasets.ArrowBasedBuilder   ):


 '''simple docstring'''







 lowercase_							       =       CsvConfig



 def  lowerCAmelCase_    (				self    :						Tuple		):
       return datasets.DatasetInfo(features=self.config.features		)



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Optional[int]		):
       if not self.config.data_files:
             raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"		)
       SCREAMING_SNAKE_CASE_      		=     dl_manager.download_and_extract(self.config.data_files		)
       if isinstance(_lowerCAmelCase							,							(str, list, tuple)		):
             SCREAMING_SNAKE_CASE_      		=     data_files
             if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
                   SCREAMING_SNAKE_CASE_      		=     [files]
             SCREAMING_SNAKE_CASE_      		=     [dl_manager.iter_files(_lowerCAmelCase		) for file in files]
             return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,							gen_kwargs={'files': files}		)]
       SCREAMING_SNAKE_CASE_      		=     []
       for split_name, files in data_files.items():
             if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
                   SCREAMING_SNAKE_CASE_      		=     [files]
             SCREAMING_SNAKE_CASE_      		=     [dl_manager.iter_files(_lowerCAmelCase		) for file in files]
             splits.append(datasets.SplitGenerator(name=_lowerCAmelCase							,							gen_kwargs={'files': files}		)		)
       return splits



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						pa.Table		):
       if self.config.features is not None:
             SCREAMING_SNAKE_CASE_      		=     self.config.features.arrow_schema
             if all(not require_storage_cast(_lowerCAmelCase		) for feature in self.config.features.values()		):
                   # cheaper cast
                   SCREAMING_SNAKE_CASE_      		=     pa.Table.from_arrays([pa_table[field.name] for field in schema]							,							schema=_lowerCAmelCase		)
             else:
                   # more expensive cast; allows str <-> int/float or str to Audio for example
                   SCREAMING_SNAKE_CASE_      		=     table_cast(_lowerCAmelCase							,							_lowerCAmelCase		)
       return pa_table




 def  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.config.features.arrow_schema if self.config.features else None
       # dtype allows reading an int column as str
       SCREAMING_SNAKE_CASE_      		=     (
           {
               name: dtype.to_pandas_dtype() if not require_storage_cast(_lowerCAmelCase		) else object
               for name, dtype, feature in zip(schema.names							,							schema.types							,							self.config.features.values()		)
           }
           if schema is not None
           else None
       )
       for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase		)		):
             SCREAMING_SNAKE_CASE_      		=     pd.read_csv(_lowerCAmelCase							,							iterator=_lowerCAmelCase							,							dtype=_lowerCAmelCase							,							**self.config.pd_read_csv_kwargs		)
             try:
                   for batch_idx, df in enumerate(_lowerCAmelCase		):
                         SCREAMING_SNAKE_CASE_      		=     pa.Table.from_pandas(_lowerCAmelCase		)
                         # Uncomment for debugging (will print the Arrow table size and elements)
                         # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                         # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                         yield (file_idx, batch_idx), self._cast_table(_lowerCAmelCase		)
             except ValueError as e:
                   logger.error(F"Failed to read file '{file}' with error {type(_lowerCAmelCase		)}: {e}"		)
                   raise

import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:str		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:DDPMScheduler		,    lowerCAmelCase__							:Optional[Any]		,    )  ->Optional[int]:
										super().__init__()
										lowercase         =      value_function
										lowercase         =      unet
										lowercase         =      scheduler
										lowercase         =      env
										lowercase         =      env.get_dataset()
										lowercase         =      {}
										for key in self.data.keys():
														try:
																		lowercase         =      self.data[key].mean()
														except:  # noqa: E722
																		pass
										lowercase         =      {}
										for key in self.data.keys():
														try:
																		lowercase         =      self.data[key].std()
														except:  # noqa: E722
																		pass
										lowercase         =      env.observation_space.shape[0]
										lowercase         =      env.action_space.shape[0]





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str     )  ->Any:
										return (x_in - self.means[key]) / self.stds[key]





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:List[Any]     )  ->Optional[Any]:
										return x_in * self.stds[key] + self.means[key]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Tuple:
										if type(lowerCAmelCase__     ) is dict:
														return {k: self.to_torch(lowerCAmelCase__     ) for k, v in x_in.items()}
										elif torch.is_tensor(lowerCAmelCase__     ):
														return x_in.to(self.unet.device     )
										return torch.tensor(lowerCAmelCase__		,    device=self.unet.device     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										for key, val in cond.items():
														lowercase         =      val.clone()
										return x_in





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										lowercase         =      x.shape[0]
										lowercase         =      None
										for i in tqdm.tqdm(self.scheduler.timesteps     ):
														# create batch of timesteps to pass into model
														lowercase         =      torch.full((batch_size,)		,    lowerCAmelCase__		,    device=self.unet.device		,    dtype=torch.long     )
														for _ in range(lowerCAmelCase__     ):
																		with torch.enable_grad():
																						x.requires_grad_()

																						# permute to match dimension for pre-trained models
																						lowercase         =      self.value_function(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample
																						lowercase         =      torch.autograd.grad([y.sum()]		,    [x]     )[0]

																						lowercase         =      self.scheduler._get_variance(lowerCAmelCase__     )
																						lowercase         =      torch.exp(0.5 * posterior_variance     )
																						lowercase         =      model_std * grad

																		lowercase         =      0
																		lowercase         =      x.detach()
																		lowercase         =      x + scale * grad
																		lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )

														lowercase         =      self.unet(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample.permute(0		,    2		,    1     )

														# TODO: verify deprecation of this kwarg
														lowercase         =      self.scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    predict_epsilon=lowerCAmelCase__     )["prev_sample"]

														# apply conditions to the trajectory (set the initial state)
														lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )
														lowercase         =      self.to_torch(lowerCAmelCase__     )
										return x, y





						def __call__(			self							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Optional[Any]=64		,    lowerCAmelCase__							:int=32		,    lowerCAmelCase__							:int=2		,    lowerCAmelCase__							:List[str]=0.1     )  ->Tuple:
										# normalize the observations and create  batch dimension
										lowercase         =      self.normalize(lowerCAmelCase__		,    "observations"     )
										lowercase         =      obs[None].repeat(lowerCAmelCase__		,    axis=0     )

										lowercase         =      {0: self.to_torch(lowerCAmelCase__     )}
										lowercase         =      (batch_size, planning_horizon, self.state_dim + self.action_dim)

										# generate initial noise and apply our conditions (to make the trajectories start at current state)
										lowercase         =      randn_tensor(lowerCAmelCase__		,    device=self.unet.device     )
										lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )
										lowercase         =      self.to_torch(lowerCAmelCase__     )

										# run the diffusion process
										lowercase     ,	lowercase         =      self.run_diffusion(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

										# sort output trajectories by value
										lowercase         =      y.argsort(0		,    descending=lowerCAmelCase__     ).squeeze()
										lowercase         =      x[sorted_idx]
										lowercase         =      sorted_values[:, :, : self.action_dim]
										lowercase         =      actions.detach().cpu().numpy()
										lowercase         =      self.de_normalize(lowerCAmelCase__		,    key="actions"     )

										# select the action with the highest value
										if y is not None:
														lowercase         =      0
										else:
														# if we didn't run value guiding, select a random action
														lowercase         =      np.random.randint(0		,    lowerCAmelCase__     )

										lowercase         =      denorm_actions[selected_index, 0]
										return denorm_actions
