




from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


a_						     =    logging.get_logger(__name__)

def  lowerCamelCase__ (					_a							,			_a):
	try:
		with open(_a							,			"rb") as flax_state_f:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       from_bytes(_a							,			flax_state_f.read())
	except UnpicklingError as e:
		try:
			with open(_a) as f:
				if f.read().startswith("version"):
					raise OSError(
					    "You seem to have cloned a repository without having git-lfs installed. Please"
					    " install git-lfs and run `git lfs install` followed by `git lfs pull` in the"
					    " folder you cloned.")
				else:
					raise ValueError from e
		except (UnicodeDecodeError, ValueError):
			raise EnvironmentError(f"Unable to convert {model_file} to Flax deserializable object. ")

	return load_flax_weights_in_pytorch_model(_a							,			_a)


def  lowerCamelCase__ (					_a							,			_a):

	try:
		import torch  # noqa: F401
	except ImportError:
		logger.error(
		    "Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"
		    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
		    " instructions.")
		raise

	# check if we have bf16 weights
	SCREAMING_SNAKE_CASE   :      int				     =       flatten_dict(jax.tree_util.tree_map(lambda _a: x.dtype == jnp.bfloataa							,			_a)).values()
	if any(_a):
		# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

		# and bf16 is not fully supported in PT yet.
		logger.warning(
		    "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
		    "before loading those in PyTorch model.")
		SCREAMING_SNAKE_CASE   :      Tuple				     =       jax.tree_util.tree_map(
		    lambda _a: params.astype(np.floataa) if params.dtype == jnp.bfloataa else params							,			_a)

	SCREAMING_SNAKE_CASE   :      List[str]				     =       ""

	SCREAMING_SNAKE_CASE   :      Any				     =       flatten_dict(_a							,			sep=".")
	SCREAMING_SNAKE_CASE   :      str				     =       pt_model.state_dict()

	# keep track of unexpected & missing keys
	SCREAMING_SNAKE_CASE   :      Any				     =       []
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       set(pt_model_dict.keys())

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		SCREAMING_SNAKE_CASE   :      Tuple				     =       flax_key_tuple.split(".")

		if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       flax_key_tuple_array[:-1] + ["weight"]
			SCREAMING_SNAKE_CASE   :      int				     =       jnp.transpose(_a							,			(3, 2, 0, 1))
		elif flax_key_tuple_array[-1] == "kernel":
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       flax_key_tuple_array[:-1] + ["weight"]
			SCREAMING_SNAKE_CASE   :      Dict				     =       flax_tensor.T
		elif flax_key_tuple_array[-1] == "scale":
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       flax_key_tuple_array[:-1] + ["weight"]

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(_a):
				SCREAMING_SNAKE_CASE   :      str				     =       (
				    flax_key_tuple_string.replace("_0"							,			".0")
				    .replace("_1"							,			".1")
				    .replace("_2"							,			".2")
				    .replace("_3"							,			".3")
				    .replace("_4"							,			".4")
				    .replace("_5"							,			".5")
				    .replace("_6"							,			".6")
				    .replace("_7"							,			".7")
				    .replace("_8"							,			".8")
				    .replace("_9"							,			".9")
				)

		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       ".".join(_a)

		if flax_key in pt_model_dict:
			if flax_tensor.shape != pt_model_dict[flax_key].shape:
				raise ValueError(
				    f"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
				    f"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.")
			else:
				# add weight to pytorch dict
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.asarray(_a) if not isinstance(_a							,			np.ndarray) else flax_tensor
				SCREAMING_SNAKE_CASE   :      int				     =       torch.from_numpy(_a)
				# remove from missing keys
				missing_keys.remove(_a)
		else:
			# weight is not expected by PyTorch model
			unexpected_keys.append(_a)

	pt_model.load_state_dict(_a)

	# re-transform missing_keys to list
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       list(_a)

	if len(_a) > 0:
		logger.warning(
		    "Some weights of the Flax model were not used when initializing the PyTorch model"
		    f" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
		    f" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
		    " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
		    f" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
		    " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
		    " FlaxBertForSequenceClassification model).")
	if len(_a) > 0:
		logger.warning(
		    f"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
		    f" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
		    " use it for predictions and inference.")

	return pt_model


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


a_						     =    datasets.utils.logging.get_logger(__name__)






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""simple docstring"""

				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   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) for file in files]
					# Infer features is they are stoed in the arrow schema
					if self.info.features is None:
						for file in itertools.chain.from_iterable(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       pa.Table:

				"""simple docstring"""

				if self.info.features is not None:
					# more expensive cast to support nested features with keys in a different order
					# allows str <-> int/float or str to Audio for example
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.info.features.arrow_schema if self.info.features is not None else None
				if self.info.features is not None and self.config.columns is not None:
					if sorted(field.name for field in schema	) != sorted(self.config.columns	):
						raise ValueError(
						    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"	)
				for file_idx, file in enumerate(itertools.chain.from_iterable(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       pa.Table.from_batches([record_batch]	)
								# 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 F"{file_idx}_{batch_idx}", self._cast_table(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise