




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 lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      []
     for part_id in partition_order:
          UpperCAmelCase_      :							List[Any]       =      df.where(f'''SPARK_PARTITION_ID() = {part_id}'''				).collect()
          for row_idx, row in enumerate(_lowercase				):
               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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Dict       =      spark.range(100				).repartition(1				)
     UpperCAmelCase_      :							Optional[Any]       =      Spark(_lowercase				)
     # 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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Dict       =      spark.range(10				).repartition(2				)
     UpperCAmelCase_      :							str       =      [1, 0]
     UpperCAmelCase_      :							List[str]       =      _generate_iterable_examples(_lowercase			,						_lowercase				)  # Reverse the partitions.
     UpperCAmelCase_      :							Optional[Any]       =      _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,						_lowercase				)

     for i, (row_id, row_dict) in enumerate(generate_fn()				):
          UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Union[str, Any]       =      spark.range(10				).repartition(1				)
     UpperCAmelCase_      :							Optional[int]       =      SparkExamplesIterable(_lowercase				)
     assert it.n_shards == 1
     for i, (row_id, row_dict) in enumerate(_lowercase				):
          assert row_id == f'''0_{i}'''
          assert row_dict == {"id": i}







@require_not_windows
@require_dill_gt_0_3_2
def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							str       =      spark.range(30				).repartition(3				)
     # Mock the generator so that shuffle reverses the partition indices.
     with patch('''numpy.random.Generator'''				) as generator_mock:
          UpperCAmelCase_      :							int       =      lambda _lowercase				: x.reverse()
          UpperCAmelCase_      :							Any       =      _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,						[2, 1, 0]				)

          UpperCAmelCase_      :							List[Any]       =      SparkExamplesIterable(_lowercase				).shuffle_data_sources(_lowercase				)
          assert shuffled_it.n_shards == 3
          for i, (row_id, row_dict) in enumerate(_lowercase				):
               UpperCAmelCase_,   UpperCAmelCase_      :							int       =      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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							Optional[int]       =      spark.range(20				).repartition(4				)

     # Partitions 0 and 2
     UpperCAmelCase_      :							List[str]       =      SparkExamplesIterable(_lowercase				).shard_data_sources(worker_id=0			,						num_workers=2				)
     assert shard_it_a.n_shards == 2
     UpperCAmelCase_      :							Optional[Any]       =      _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,						[0, 2]				)
     for i, (row_id, row_dict) in enumerate(_lowercase				):
          UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      expected_row_ids_and_row_dicts_a[i]
          assert row_id == expected_row_id
          assert row_dict == expected_row_dict

     # Partitions 1 and 3
     UpperCAmelCase_      :							int       =      SparkExamplesIterable(_lowercase				).shard_data_sources(worker_id=1			,						num_workers=2				)
     assert shard_it_a.n_shards == 2
     UpperCAmelCase_      :							str       =      _get_expected_row_ids_and_row_dicts_for_partition_order(_lowercase			,						[1, 3]				)
     for i, (row_id, row_dict) in enumerate(_lowercase				):
          UpperCAmelCase_,   UpperCAmelCase_      :							int       =      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 lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[Any]       =      pyspark.sql.SparkSession.builder.master('''local[*]'''				).appName('''pyspark'''				).getOrCreate()
     UpperCAmelCase_      :							str       =      spark.range(100				).repartition(1				)
     UpperCAmelCase_      :							List[str]       =      Spark(_lowercase				)
     # 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





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

     if model_size == "tiny":
          UpperCAmelCase_      :							Tuple       =      96
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 6, 2)
          UpperCAmelCase_      :							str       =      (3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase_      :							Dict       =      96
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							List[str]       =      (3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase_      :							Tuple       =      128
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Optional[int]       =      (4, 8, 16, 32)
     else:
          UpperCAmelCase_      :							List[str]       =      192
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Union[str, Any]       =      (6, 12, 24, 48)

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if "patch_embed.proj" in name:
          UpperCAmelCase_      :							str       =      name.replace('''patch_embed.proj'''			,						'''embeddings.patch_embeddings.projection'''				)
     if "patch_embed.norm" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''patch_embed.norm'''			,						'''embeddings.norm'''				)
     if "layers" in name:
          UpperCAmelCase_      :							Tuple       =      '''encoder.''' + name
     if "attn.proj" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''attn.proj'''			,						'''attention.output.dense'''				)
     if "attn" in name:
          UpperCAmelCase_      :							int       =      name.replace('''attn'''			,						'''attention.self'''				)
     if "norm1" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''norm1'''			,						'''layernorm_before'''				)
     if "norm2" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''norm2'''			,						'''layernorm_after'''				)
     if "mlp.fc1" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''mlp.fc1'''			,						'''intermediate.dense'''				)
     if "mlp.fc2" in name:
          UpperCAmelCase_      :							Optional[int]       =      name.replace('''mlp.fc2'''			,						'''output.dense'''				)

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               UpperCAmelCase_      :							str       =      key.split('''.'''				)
               UpperCAmelCase_      :							str       =      int(key_split[1]				)
               UpperCAmelCase_      :							Optional[Any]       =      int(key_split[3]				)
               UpperCAmelCase_      :							Optional[int]       =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    UpperCAmelCase_      :							Any       =      val[:dim, :]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_      :							List[str]       =      val[-dim:, :]
               else:
                    UpperCAmelCase_      :							Any       =      val[
                        :dim
                    ]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_      :							Union[str, Any]       =      val[
                        -dim:
                    ]
          else:
               UpperCAmelCase_      :							Optional[Any]       =      val

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_      :							int       =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''			,						'''-'''				)				)				)
     UpperCAmelCase_      :							Optional[Any]       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)
     UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				)

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      model(**_lowercase				).logits

     assert torch.allclose(_lowercase			,						_lowercase			,						atol=1E-3				)

     print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
     image_processor.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     __a          =			parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)