
"""simple docstring"""
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_a   :      List[Any]       =				get_logger(__name__)

# Datasets
_a   :      Optional[Any]       =				"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
_a   :      Union[str, Any]       =				"""https://cdn-datasets.huggingface.co/datasets/datasets"""
_a   :      List[str]       =				"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

# Metrics
_a   :      Tuple       =				"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
_a   :      int       =				"""https://cdn-datasets.huggingface.co/datasets/metric"""
_a   :      List[str]       =				"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

# Hub
_a   :      int       =				os.environ.get("""HF_ENDPOINT""", """https://huggingface.co""")
_a   :      Dict       =				HF_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
_a   :      Dict       =				"""main"""

_a   :      List[str]       =				version.parse(platform.python_version())

if PY_VERSION < version.parse("""3.8"""):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
_a   :      Optional[Any]       =				{"""1""", """ON""", """YES""", """TRUE"""}
_a   :      Dict       =				ENV_VARS_TRUE_VALUES.union({"""AUTO"""})


# Imports
_a   :      str       =				version.parse(importlib_metadata.version("""dill"""))
_a   :      Tuple       =				version.parse(importlib_metadata.version("""pandas"""))
_a   :      int       =				version.parse(importlib_metadata.version("""pyarrow"""))

_a   :      Tuple       =				os.environ.get("""USE_TF""", """AUTO""").upper()
_a   :      Optional[Any]       =				os.environ.get("""USE_TORCH""", """AUTO""").upper()
_a   :      Tuple       =				os.environ.get("""USE_JAX""", """AUTO""").upper()

_a   :      int       =				"""N/A"""
_a   :      str       =				False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
    _a   :      List[str]       =				importlib.util.find_spec("""torch""") is not None
    if TORCH_AVAILABLE:
        try:
            _a   :      str       =				version.parse(importlib_metadata.version("""torch"""))
            logger.info(f'PyTorch version {TORCH_VERSION} available.')
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("""Disabling PyTorch because USE_TF is set""")

_a   :      Optional[int]       =				"""N/A"""
_a   :      str       =				False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
    _a   :      List[Any]       =				importlib.util.find_spec("""tensorflow""") is not None
    if TF_AVAILABLE:
        # For the metadata, we have to look for both tensorflow and tensorflow-cpu
        for package in [
            "tensorflow",
            "tensorflow-cpu",
            "tensorflow-gpu",
            "tf-nightly",
            "tf-nightly-cpu",
            "tf-nightly-gpu",
            "intel-tensorflow",
            "tensorflow-rocm",
            "tensorflow-macos",
        ]:
            try:
                _a   :      Optional[int]       =				version.parse(importlib_metadata.version(package))
            except importlib_metadata.PackageNotFoundError:
                continue
            else:
                break
        else:
            _a   :      int       =				False
    if TF_AVAILABLE:
        if TF_VERSION.major < 2:
            logger.info(f'TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.')
            _a   :      List[str]       =				False
        else:
            logger.info(f'TensorFlow version {TF_VERSION} available.')
else:
    logger.info("""Disabling Tensorflow because USE_TORCH is set""")


_a   :      Union[str, Any]       =				"""N/A"""
_a   :      List[Any]       =				False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
    _a   :      int       =				importlib.util.find_spec("""jax""") is not None and importlib.util.find_spec("""jaxlib""") is not None
    if JAX_AVAILABLE:
        try:
            _a   :      Any       =				version.parse(importlib_metadata.version("""jax"""))
            logger.info(f'JAX version {JAX_VERSION} available.')
        except importlib_metadata.PackageNotFoundError:
            pass
else:
    logger.info("""Disabling JAX because USE_JAX is set to False""")


_a   :      int       =				os.environ.get("""USE_BEAM""", """AUTO""").upper()
_a   :      str       =				"""N/A"""
_a   :      List[str]       =				False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
    try:
        _a   :      List[Any]       =				version.parse(importlib_metadata.version("""apache_beam"""))
        _a   :      int       =				True
        logger.info(f'Apache Beam version {BEAM_VERSION} available.')
    except importlib_metadata.PackageNotFoundError:
        pass
else:
    logger.info("""Disabling Apache Beam because USE_BEAM is set to False""")


# Optional tools for data loading
_a   :      Union[str, Any]       =				importlib.util.find_spec("""sqlalchemy""") is not None

# Optional tools for feature decoding
_a   :      Tuple       =				importlib.util.find_spec("""PIL""") is not None
_a   :      Tuple       =				importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.0.31""")
_a   :      Dict       =				importlib.util.find_spec("""soundfile""") is not None and version.parse(
    importlib.import_module("""soundfile""").__libsndfile_version__
) >= version.parse("""1.1.0""")

# Optional compression tools
_a   :      Dict       =				importlib.util.find_spec("""rarfile""") is not None
_a   :      Union[str, Any]       =				importlib.util.find_spec("""zstandard""") is not None
_a   :      Tuple       =				importlib.util.find_spec("""lz4""") is not None
_a   :      str       =				importlib.util.find_spec("""py7zr""") is not None


# Cache location
_a   :      int       =				"""~/.cache"""
_a   :      Optional[Any]       =				os.getenv("""XDG_CACHE_HOME""", DEFAULT_XDG_CACHE_HOME)
_a   :      List[Any]       =				os.path.join(XDG_CACHE_HOME, """huggingface""")
_a   :      Union[str, Any]       =				os.path.expanduser(os.getenv("""HF_HOME""", DEFAULT_HF_CACHE_HOME))

_a   :      Optional[Any]       =				os.path.join(HF_CACHE_HOME, """datasets""")
_a   :      Tuple       =				Path(os.getenv("""HF_DATASETS_CACHE""", DEFAULT_HF_DATASETS_CACHE))

_a   :      List[Any]       =				os.path.join(HF_CACHE_HOME, """metrics""")
_a   :      Union[str, Any]       =				Path(os.getenv("""HF_METRICS_CACHE""", DEFAULT_HF_METRICS_CACHE))

_a   :      Optional[int]       =				os.path.join(HF_CACHE_HOME, """modules""")
_a   :      Dict       =				Path(os.getenv("""HF_MODULES_CACHE""", DEFAULT_HF_MODULES_CACHE))

_a   :      Optional[int]       =				"""downloads"""
_a   :      List[Any]       =				os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_a   :      List[str]       =				Path(os.getenv("""HF_DATASETS_DOWNLOADED_DATASETS_PATH""", DEFAULT_DOWNLOADED_DATASETS_PATH))

_a   :      Tuple       =				"""extracted"""
_a   :      List[str]       =				os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_a   :      Union[str, Any]       =				Path(os.getenv("""HF_DATASETS_EXTRACTED_DATASETS_PATH""", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_a   :      Optional[Any]       =				(
    os.environ.get("""HF_UPDATE_DOWNLOAD_COUNTS""", """AUTO""").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
_a   :      Optional[int]       =				1_000

# Size of the preloaded record batch in `Dataset.__iter__`
_a   :      Dict       =				10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
_a   :      List[str]       =				4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
_a   :      List[Any]       =				"""500MB"""

# Parquet configuration
_a   :      Optional[Any]       =				100
_a   :      Any       =				100
_a   :      Tuple       =				100

# Offline mode
_a   :      Union[str, Any]       =				os.environ.get("""HF_DATASETS_OFFLINE""", """AUTO""").upper() in ENV_VARS_TRUE_VALUES

# In-memory
_a   :      str       =				0  # Disabled
_a   :      str       =				float(os.environ.get("""HF_DATASETS_IN_MEMORY_MAX_SIZE""", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
_a   :      List[Any]       =				"""dataset.arrow"""
_a   :      List[Any]       =				"""indices.arrow"""
_a   :      Optional[int]       =				"""state.json"""
_a   :      str       =				"""dataset_info.json"""
_a   :      Optional[Any]       =				"""dataset_infos.json"""
_a   :      List[Any]       =				"""LICENSE"""
_a   :      Union[str, Any]       =				"""metric_info.json"""
_a   :      List[Any]       =				"""dataset_dict.json"""

_a   :      str       =				"""datasets_modules"""

_a   :      List[Any]       =				255

# Streaming
_a   :      Any       =				20
_a   :      Union[str, Any]       =				5

# Datasets without script
_a   :      Union[str, Any]       =				200
_a   :      List[str]       =				10
_a   :      Dict       =				200

# Progress bars
_a   :      Dict       =				0.05  # 20 progress updates per sec



import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]     )	->		str:
				__lowercase								=   filter(lambda SCREAMING_SNAKE_CASE     : p.requires_grad       ,				model.parameters()     )
				__lowercase								=   sum([np.prod(p.size()     ) for p in model_parameters]     )
				return params


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
class       A__  (		pl.Callback							):





							def      a__					(					self    :  List[str]	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											__lowercase								=   {f"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
											pl_module.logger.log_metrics(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  Dict	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  int=True							)		->						None:



											"""simple docstring"""


											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""							)
											__lowercase								=   trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}							)
											# Log results
											__lowercase								=   Path(pl_module.hparams.output_dir							)
											if type_path == "test":
															__lowercase								=   od / 'test_results.txt'
															__lowercase								=   od / 'test_generations.txt'
											else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															__lowercase								=   od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
															__lowercase								=   od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
															results_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
															generations_file.parent.mkdir(exist_ok=_UpperCAmelCase							)
											with open(_UpperCAmelCase	,					'a+'							) as writer:
															for key in sorted(_UpperCAmelCase							):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__lowercase								=   metrics[key]
																			if isinstance(_UpperCAmelCase	,					torch.Tensor							):
																							__lowercase								=   val.item()
																			__lowercase								=   f"""{key}: {val:.6f}\n"""
																			writer.write(_UpperCAmelCase							)

											if not save_generations:
															return

											if "preds" in metrics:
															__lowercase								=   '\n'.join(metrics['preds']							)
															generations_file.open('w+'							).write(_UpperCAmelCase							)





							@rank_zero_only
							def      a__					(					self    :  str	,					_UpperCAmelCase    :  Optional[int]	,					_UpperCAmelCase    :  str							)		->						Optional[int]:



											"""simple docstring"""


											try:
															__lowercase								=   pl_module.model.model.num_parameters()
											except AttributeError:
															__lowercase								=   pl_module.model.num_parameters()

											__lowercase								=   count_trainable_parameters(_UpperCAmelCase							)
											# mp stands for million parameters
											trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6}							)





							@rank_zero_only
							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  pl.LightningModule							)		->						Optional[Any]:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											return self._write_logs(_UpperCAmelCase	,					_UpperCAmelCase	,					'test'							)





							@rank_zero_only
							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pl.Trainer	,					_UpperCAmelCase    :  Tuple							)		->						str:



											"""simple docstring"""


											save_json(pl_module.metrics	,					pl_module.metrics_save_path							)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: List[str]       ,				SCREAMING_SNAKE_CASE			: Union[str, Any]=1       ,				SCREAMING_SNAKE_CASE			: str=False     )	->		List[str]:
				if metric == "rouge2":
								__lowercase								=   '{val_avg_rouge2:.4f}-{step_count}'
				elif metric == "bleu":
								__lowercase								=   '{val_avg_bleu:.4f}-{step_count}'
				elif metric == "loss":
								__lowercase								=   '{val_avg_loss:.4f}-{step_count}'
				else:
								raise NotImplementedError(
								    F"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
								    ' this function.'     )

				__lowercase								=   ModelCheckpoint(
				    dirpath=SCREAMING_SNAKE_CASE       ,				filename=SCREAMING_SNAKE_CASE       ,				monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				save_top_k=SCREAMING_SNAKE_CASE       ,				)
				return checkpoint_callback




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any       ,				SCREAMING_SNAKE_CASE			: int     )	->		Union[str, Any]:
				return EarlyStopping(
				    monitor=F"""val_{metric}"""       ,				mode='min' if 'loss' in metric else 'max'       ,				patience=SCREAMING_SNAKE_CASE       ,				verbose=SCREAMING_SNAKE_CASE       ,				)





