import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)






def  snake_case  (       snake_case__			:List[str])						->   Union[str, Any]:
       _A        =  R"""\w+[.]\d+"""
       _A        =  re.findall(snake_case__   ,	snake_case__)
       for pat in pats:
              _A        =  key.replace(snake_case__   ,	"""_""".join(pat.split(""".""")))
       return key






def  snake_case  (       snake_case__			:Any   ,	snake_case__			:Tuple   ,	snake_case__			:List[str])						->   Union[str, Any]:
       _A        =  pt_tuple_key[:-1] + ("""scale""",)
       if (
           any("""norm""" in str_ for str_ in pt_tuple_key)
           and (pt_tuple_key[-1] == "bias")
           and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
           and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
       ):
              _A        =  pt_tuple_key[:-1] + ("""scale""",)
              return renamed_pt_tuple_key, pt_tensor
       elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
              _A        =  pt_tuple_key[:-1] + ("""scale""",)
              return renamed_pt_tuple_key, pt_tensor

       # embedding
       if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
              _A        =  pt_tuple_key[:-1] + ("""embedding""",)
              return renamed_pt_tuple_key, pt_tensor

       # conv layer
       _A        =  pt_tuple_key[:-1] + ("""kernel""",)
       if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
              _A        =  pt_tensor.transpose(2   ,	3   ,	1   ,	0)
              return renamed_pt_tuple_key, pt_tensor

       # linear layer
       _A        =  pt_tuple_key[:-1] + ("""kernel""",)
       if pt_tuple_key[-1] == "weight":
              _A        =  pt_tensor.T
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm weight
       _A        =  pt_tuple_key[:-1] + ("""weight""",)
       if pt_tuple_key[-1] == "gamma":
              return renamed_pt_tuple_key, pt_tensor

       # old PyTorch layer norm bias
       _A        =  pt_tuple_key[:-1] + ("""bias""",)
       if pt_tuple_key[-1] == "beta":
              return renamed_pt_tuple_key, pt_tensor

       return pt_tuple_key, pt_tensor







def  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:Dict   ,	snake_case__			:Tuple=42)						->   Dict:
       # Step 1: Convert pytorch tensor to numpy
       _A        =  {k: v.numpy() for k, v in pt_state_dict.items()}

       # Step 2: Since the model is stateless, get random Flax params
       _A        =  flax_model.init_weights(PRNGKey(snake_case__))

       _A        =  flatten_dict(snake_case__)
       _A        =  {}

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              _A        =  rename_key(snake_case__)
              _A        =  tuple(renamed_pt_key.split("""."""))

              # Correctly rename weight parameters
              _A   ,  _A        =  rename_key_and_reshape_tensor(snake_case__   ,	snake_case__   ,	snake_case__)

              if flax_key in random_flax_state_dict:
                     if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                            raise ValueError(
                                F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                                F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.''')

        # also add unexpected weight so that warning is thrown
              _A        =  jnp.asarray(snake_case__)

       return unflatten_dict(snake_case__)





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)






def  snake_case  (       snake_case__			:Union[str, Any]   ,	snake_case__			:Dict)						->   Any:
       _A        =  []
       for part_id in partition_order:
              _A        =  df.where(F'''SPARK_PARTITION_ID() = {part_id}''').collect()
              for row_idx, row in enumerate(snake_case__):
                     expected_row_ids_and_row_dicts.append((F'''{part_id}_{row_idx}''', row.asDict()))
       return expected_row_ids_and_row_dicts






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Optional[Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
       # that each partition can hold 2 rows.
       spark_builder._repartition_df_if_needed(max_shard_size=16)
       # Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
       assert spark_builder.df.rdd.getNumPartitions() == 50






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(2)
       _A        =  [1, 0]
       _A        =  _generate_iterable_examples(snake_case__   ,	snake_case__)  # Reverse the partitions.
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	snake_case__)

       for i, (row_id, row_dict) in enumerate(generate_fn()):
              _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   int:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(10).repartition(1)
       _A        =  SparkExamplesIterable(snake_case__)
       assert it.n_shards == 1
       for i, (row_id, row_dict) in enumerate(snake_case__):
              assert row_id == F'''0_{i}'''
              assert row_dict == {"id": i}






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Union[str, Any]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(30).repartition(3)
       # Mock the generator so that shuffle reverses the partition indices.
       with patch("""numpy.random.Generator""") as generator_mock:
              _A        =  lambda snake_case__: x.reverse()
              _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[2, 1, 0])

              _A        =  SparkExamplesIterable(snake_case__).shuffle_data_sources(snake_case__)
              assert shuffled_it.n_shards == 3
              for i, (row_id, row_dict) in enumerate(snake_case__):
                     _A   ,  _A        =  expected_row_ids_and_row_dicts[i]
                     assert row_id == expected_row_id
                     assert row_dict == expected_row_dict






@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   List[str]:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(20).repartition(4)

       # Partitions 0 and 2
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=0   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[0, 2])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict

       # Partitions 1 and 3
       _A        =  SparkExamplesIterable(snake_case__).shard_data_sources(worker_id=1   ,	num_workers=2)
       assert shard_it_a.n_shards == 2
       _A        =  _get_expected_row_ids_and_row_dicts_for_partition_order(snake_case__   ,	[1, 3])
       for i, (row_id, row_dict) in enumerate(snake_case__):
              _A   ,  _A        =  expected_row_ids_and_row_dicts_a[i]
              assert row_id == expected_row_id
              assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def  snake_case  (       )						->   Tuple:
       _A        =  pyspark.sql.SparkSession.builder.master("""local[*]""").appName("""pyspark""").getOrCreate()
       _A        =  spark.range(100).repartition(1)
       _A        =  Spark(snake_case__)
       # Choose a small max_shard_size for maximum partitioning.
       spark_builder._repartition_df_if_needed(max_shard_size=1)
       # The new number of partitions should not be greater than the number of rows.
       assert spark_builder.df.rdd.getNumPartitions() == 100




