'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE	:					List[Any]					=  logging.get_logger(__name__)

def      UpperCamelCase_							( _UpperCAmelCase  :				List[Any]      ,     _UpperCAmelCase  :				Optional[Any]=False      ,     _UpperCAmelCase  :				Union[str, Any]=False   )   ->   Optional[int]:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[Any]											=   "backbone." if is_semantic else ""

       _UpperCAmelCase					:  str											=   []
       for i in range(config.num_hidden_layers   ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")   )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")   )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")   )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               (F"""{prefix}cls_token""", "beit.embeddings.cls_token"),
               (F"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
               (F"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
           ]   )

       if has_lm_head:
              # mask token + shared relative position bias + layernorm
              rename_keys.extend(
                  [
                      ("mask_token", "beit.embeddings.mask_token"),
                      (
                          "rel_pos_bias.relative_position_bias_table",
                          "beit.encoder.relative_position_bias.relative_position_bias_table",
                      ),
                      (
                          "rel_pos_bias.relative_position_index",
                          "beit.encoder.relative_position_bias.relative_position_index",
                      ),
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                  ]   )
       elif is_semantic:
              # semantic segmentation classification heads
              rename_keys.extend(
                  [
                      ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                      ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                      ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                      ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
                  ]   )
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ("fc_norm.weight", "beit.pooler.layernorm.weight"),
                      ("fc_norm.bias", "beit.pooler.layernorm.bias"),
                      ("head.weight", "classifier.weight"),
                      ("head.bias", "classifier.bias"),
                  ]   )

       return rename_keys

def      UpperCamelCase_							( _UpperCAmelCase  :				Union[str, Any]      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				int=False      ,     _UpperCAmelCase  :				Union[str, Any]=False   )   ->   Union[str, Any]:


       """simple docstring"""







       for i in range(config.num_hidden_layers   ):
              _UpperCAmelCase					:  Union[str, Any]											=   "backbone." if is_semantic else ""
              # queries, keys and values
              _UpperCAmelCase					:  Tuple											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""   )
              _UpperCAmelCase					:  Any											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""   )
              _UpperCAmelCase					:  Optional[int]											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""   )

              _UpperCAmelCase					:  Tuple											=   in_proj_weight[
                  : config.hidden_size, :
              ]
              _UpperCAmelCase					:  List[str]											=   q_bias
              _UpperCAmelCase					:  List[Any]											=   in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _UpperCAmelCase					:  str											=   in_proj_weight[
                  -config.hidden_size :, :
              ]
              _UpperCAmelCase					:  Optional[Any]											=   v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              _UpperCAmelCase					:  Tuple											=   state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""   )
              _UpperCAmelCase					:  List[Any]											=   state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""   )

              _UpperCAmelCase					:  Optional[Any]											=   gamma_a
              _UpperCAmelCase					:  Optional[Any]											=   gamma_a

              # relative_position bias table + index
              if not has_lm_head:
                     # each layer has its own relative position bias
                     _UpperCAmelCase					:  List[Any]											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""   )
                     _UpperCAmelCase					:  List[str]											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""   )

                     _UpperCAmelCase					:  Optional[int]											=   table
                     _UpperCAmelCase					:  Dict											=   index

def      UpperCamelCase_							( _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				Any      ,     _UpperCAmelCase  :				Any   )   ->   int:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[int]											=   dct.pop(_UpperCAmelCase   )
       _UpperCAmelCase					:  List[str]											=   val

def      UpperCamelCase_							( )   ->   Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase					:  int											=   "http://images.cocodataset.org/val2017/000000039769.jpg"
       _UpperCAmelCase					:  Tuple											=   Image.open(requests.get(_UpperCAmelCase      ,     stream=_UpperCAmelCase   ).raw   )
       return im

@torch.no_grad()
def      UpperCamelCase_							( _UpperCAmelCase  :				Tuple      ,     _UpperCAmelCase  :				str   )   ->   Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase					:  Union[str, Any]											=   BeitConfig()
       _UpperCAmelCase					:  List[str]											=   False
       _UpperCAmelCase					:  int											=   False
       _UpperCAmelCase					:  int											=   "huggingface/label-files"
       # set config parameters based on URL
       if checkpoint_url[-9:-4] == "pt22k":
              # masked image modeling
              _UpperCAmelCase					:  Tuple											=   True
              _UpperCAmelCase					:  List[str]											=   True
              _UpperCAmelCase					:  Union[str, Any]											=   True
       elif checkpoint_url[-9:-4] == "ft22k":
              # intermediate fine-tuning on ImageNet-22k
              _UpperCAmelCase					:  str											=   True
              _UpperCAmelCase					:  Tuple											=   21_841
              _UpperCAmelCase					:  Tuple											=   "imagenet-22k-id2label.json"
              _UpperCAmelCase					:  int											=   json.load(open(hf_hub_download(_UpperCAmelCase      ,     _UpperCAmelCase      ,     repo_type="dataset"   )      ,     "r"   )   )
              _UpperCAmelCase					:  int											=   {int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
              # this dataset contains 21843 labels but the model only has 21841
              # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
              del idalabel[9_205]
              del idalabel[15_027]
              _UpperCAmelCase					:  Union[str, Any]											=   idalabel
              _UpperCAmelCase					:  List[str]											=   {v: k for k, v in idalabel.items()}
       elif checkpoint_url[-8:-4] == "to1k":
              # fine-tuning on ImageNet-1k
              _UpperCAmelCase					:  int											=   True
              _UpperCAmelCase					:  Optional[Any]											=   1_000
              _UpperCAmelCase					:  Dict											=   "imagenet-1k-id2label.json"
              _UpperCAmelCase					:  int											=   json.load(open(hf_hub_download(_UpperCAmelCase      ,     _UpperCAmelCase      ,     repo_type="dataset"   )      ,     "r"   )   )
              _UpperCAmelCase					:  str											=   {int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
              _UpperCAmelCase					:  Optional[int]											=   idalabel
              _UpperCAmelCase					:  int											=   {v: k for k, v in idalabel.items()}
              if "384" in checkpoint_url:
                     _UpperCAmelCase					:  Any											=   384
              if "512" in checkpoint_url:
                     _UpperCAmelCase					:  int											=   512
       elif "ade20k" in checkpoint_url:
              # fine-tuning
              _UpperCAmelCase					:  Optional[int]											=   True
              _UpperCAmelCase					:  List[Any]											=   150
              _UpperCAmelCase					:  Optional[Any]											=   "ade20k-id2label.json"
              _UpperCAmelCase					:  List[Any]											=   json.load(open(hf_hub_download(_UpperCAmelCase      ,     _UpperCAmelCase      ,     repo_type="dataset"   )      ,     "r"   )   )
              _UpperCAmelCase					:  List[Any]											=   {int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
              _UpperCAmelCase					:  Any											=   idalabel
              _UpperCAmelCase					:  List[str]											=   {v: k for k, v in idalabel.items()}
              _UpperCAmelCase					:  Dict											=   640
              _UpperCAmelCase					:  List[Any]											=   True
       else:
              raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"   )

       # size of the architecture
       if "base" in checkpoint_url:
              pass
       elif "large" in checkpoint_url:
              _UpperCAmelCase					:  Optional[Any]											=   1_024
              _UpperCAmelCase					:  Optional[int]											=   4_096
              _UpperCAmelCase					:  List[Any]											=   24
              _UpperCAmelCase					:  Dict											=   16
              if "ade20k" in checkpoint_url:
                     _UpperCAmelCase					:  Optional[Any]											=   640
                     _UpperCAmelCase					:  List[str]											=   [7, 11, 15, 23]
       else:
              raise ValueError("Should either find 'base' or 'large' in checkpoint URL"   )

       # load state_dict of original model, remove and rename some keys
       _UpperCAmelCase					:  Any											=   torch.hub.load_state_dict_from_url(_UpperCAmelCase      ,     map_location="cpu"      ,     check_hash=_UpperCAmelCase   )
       _UpperCAmelCase					:  Tuple											=   state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

       _UpperCAmelCase					:  Any											=   create_rename_keys(_UpperCAmelCase      ,     has_lm_head=_UpperCAmelCase      ,     is_semantic=_UpperCAmelCase   )
       for src, dest in rename_keys:
              rename_key(_UpperCAmelCase      ,     _UpperCAmelCase      ,     _UpperCAmelCase   )
       read_in_q_k_v(_UpperCAmelCase      ,     _UpperCAmelCase      ,     has_lm_head=_UpperCAmelCase      ,     is_semantic=_UpperCAmelCase   )
       if is_semantic:
              # add prefix to decoder keys
              for key, val in state_dict.copy().items():
                     _UpperCAmelCase					:  Optional[Any]											=   state_dict.pop(_UpperCAmelCase   )
                     if key.startswith("backbone.fpn"   ):
                            _UpperCAmelCase					:  List[str]											=   key.replace("backbone.fpn"      ,     "fpn"   )
                     _UpperCAmelCase					:  str											=   val

    # load HuggingFace model
       if checkpoint_url[-9:-4] == "pt22k":
              _UpperCAmelCase					:  Optional[Any]											=   BeitForMaskedImageModeling(_UpperCAmelCase   )
       elif "ade20k" in checkpoint_url:
              _UpperCAmelCase					:  int											=   BeitForSemanticSegmentation(_UpperCAmelCase   )
       else:
              _UpperCAmelCase					:  Union[str, Any]											=   BeitForImageClassification(_UpperCAmelCase   )
       model.eval()
       model.load_state_dict(_UpperCAmelCase   )

       # Check outputs on an image
       if is_semantic:
              _UpperCAmelCase					:  List[str]											=   BeitImageProcessor(size=config.image_size      ,     do_center_crop=_UpperCAmelCase   )
              _UpperCAmelCase					:  List[Any]											=   load_dataset("hf-internal-testing/fixtures_ade20k"      ,     split="test"   )
              _UpperCAmelCase					:  Optional[Any]											=   Image.open(ds[0]["file"]   )
       else:
              _UpperCAmelCase					:  Any											=   BeitImageProcessor(
                  size=config.image_size      ,     resample=PILImageResampling.BILINEAR      ,     do_center_crop=_UpperCAmelCase   )
              _UpperCAmelCase					:  Optional[int]											=   prepare_img()

       _UpperCAmelCase					:  int											=   image_processor(images=_UpperCAmelCase      ,     return_tensors="pt"   )
       _UpperCAmelCase					:  List[str]											=   encoding["pixel_values"]

       _UpperCAmelCase					:  List[str]											=   model(_UpperCAmelCase   )
       _UpperCAmelCase					:  List[str]											=   outputs.logits

       # verify logits
       _UpperCAmelCase					:  Any											=   torch.Size([1, 1_000]   )
       if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"   ):
              _UpperCAmelCase					:  Dict											=   torch.Size([1, 196, 8_192]   )
       elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"   ):
              _UpperCAmelCase					:  Optional[int]											=   torch.Size([1, 196, 8_192]   )
       elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"   ):
              _UpperCAmelCase					:  int											=   torch.Size([1, 21_841]   )
              _UpperCAmelCase					:  List[str]											=   torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]   )
              _UpperCAmelCase					:  Optional[int]											=   2_397
       elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"   ):
              _UpperCAmelCase					:  str											=   torch.Size([1, 21_841]   )
              _UpperCAmelCase					:  Dict											=   torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]   )
              _UpperCAmelCase					:  Optional[Any]											=   2_396
       elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"   ):
              _UpperCAmelCase					:  str											=   torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]   )
              _UpperCAmelCase					:  Dict											=   285
       elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"   ):
              _UpperCAmelCase					:  Any											=   torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]   )
              _UpperCAmelCase					:  str											=   281
       elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"   ):
              _UpperCAmelCase					:  Optional[Any]											=   torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]   )
              _UpperCAmelCase					:  int											=   761
       elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"   ):
              _UpperCAmelCase					:  str											=   torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]   )
              _UpperCAmelCase					:  Tuple											=   761
       elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"   ):
              _UpperCAmelCase					:  Dict											=   torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]   )
              _UpperCAmelCase					:  Dict											=   761
       elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"   ):
              _UpperCAmelCase					:  Union[str, Any]											=   torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]   )
              _UpperCAmelCase					:  List[Any]											=   761
       elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"   ):
              _UpperCAmelCase					:  Optional[Any]											=   torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]   )
              _UpperCAmelCase					:  str											=   761
       elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"   ):
              _UpperCAmelCase					:  Optional[int]											=   (1, 150, 160, 160)
              _UpperCAmelCase					:  Dict											=   torch.tensor(
                  [
                      [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
                      [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
                      [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
                  ]   )
       elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"   ):
              _UpperCAmelCase					:  Optional[Any]											=   (1, 150, 160, 160)
              _UpperCAmelCase					:  List[str]											=   torch.tensor(
                  [
                      [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
                      [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
                      [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
                  ]   )
       else:
              raise ValueError("Can't verify logits as model is not supported"   )

       assert logits.shape == expected_shape, "Shape of logits not as expected"
       if not has_lm_head:
              if is_semantic:
                     assert torch.allclose(
                         logits[0, :3, :3, :3]      ,     _UpperCAmelCase      ,     atol=1e-3   ), "First elements of logits not as expected"
              else:
                     print("Predicted class idx:"      ,     logits.argmax(-1   ).item()   )
                     assert torch.allclose(
                         logits[0, :3]      ,     _UpperCAmelCase      ,     atol=1e-3   ), "First elements of logits not as expected"
                     assert logits.argmax(-1   ).item() == expected_class_idx, "Predicted class index not as expected"

       Path(_UpperCAmelCase   ).mkdir(exist_ok=_UpperCAmelCase   )
       print(F"""Saving model to {pytorch_dump_folder_path}"""   )
       model.save_pretrained(_UpperCAmelCase   )
       print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
       image_processor.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE	:					Tuple					=  argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth""",
      type=str,
      help="""URL to the original PyTorch checkpoint (.pth file).""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  __SCREAMING_SNAKE_CASE	:					Dict					=  parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


'''simple docstring'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class      lowerCamelCase_     (TensorFormatter[Mapping, np.ndarray, Mapping]   ):
      '''simple docstring'''

      def __init__(	self						:    str						, A						:    List[str]=None						, **A						:    Optional[Any]	):
             super().__init__(features=A	)
             _UpperCAmelCase					:  Tuple											=   np_array_kwargs




      def 					_A     (	self						:    str						, A						:    Tuple	):
             if isinstance(A						, A	):
                    if column and all(
                        isinstance(A						, np.ndarray	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
                           return np.stack(A	)
                    else:
                           # don't use np.array(column, dtype=object)
                           # since it fails in certain cases
                           # see https://stackoverflow.com/q/51005699
                           _UpperCAmelCase					:  List[str]											=   np.empty(len(A	)						, dtype=A	)
                           _UpperCAmelCase					:  int											=   column
                           return out
             return column




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             if isinstance(A						, (str, bytes, type(A	))	):
                    return value
             elif isinstance(A						, (np.character, np.ndarray)	) and np.issubdtype(value.dtype						, np.character	):
                    return value
             elif isinstance(A						, np.number	):
                    return value

             _UpperCAmelCase					:  List[str]											=   {}

             if isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.integer	):
                    _UpperCAmelCase					:  str											=   {"dtype": np.intaa}
             elif isinstance(A						, np.ndarray	) and np.issubdtype(value.dtype						, np.floating	):
                    _UpperCAmelCase					:  int											=   {"dtype": np.floataa}
             elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                    import PIL.Image

                    if isinstance(A						, PIL.Image.Image	):
                           return np.asarray(A						, **self.np_array_kwargs	)

             return np.asarray(A						, **{**default_dtype, **self.np_array_kwargs}	)




      def 					_A     (	self						:    Any						, A						:    Optional[Any]	):
             # support for torch, tf, jax etc.
             if config.TORCH_AVAILABLE and "torch" in sys.modules:
                    import torch

                    if isinstance(A						, torch.Tensor	):
                           return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
             if hasattr(A						, "__array__"	) and not isinstance(A						, (np.ndarray, np.character, np.number)	):
                    _UpperCAmelCase					:  Dict											=   data_struct.__array__()
             # support for nested types like struct of list of struct
             if isinstance(A						, np.ndarray	):
                    if data_struct.dtype == object:
                           return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             if isinstance(A						, (list, tuple)	):
                    return self._consolidate([self.recursive_tensorize(A	) for substruct in data_struct]	)
             return self._tensorize(A	)




      def 					_A     (	self						:    List[Any]						, A						:    dict	):
             return map_nested(self._recursive_tensorize						, A						, map_list=A	)




      def 					_A     (	self						:    List[str]						, A						:    pa.Table	):
             _UpperCAmelCase					:  str											=   self.numpy_arrow_extractor().extract_row(A	)
             _UpperCAmelCase					:  Dict											=   self.python_features_decoder.decode_row(A	)
             return self.recursive_tensorize(A	)




      def 					_A     (	self						:    str						, A						:    pa.Table	):
             _UpperCAmelCase					:  List[str]											=   self.numpy_arrow_extractor().extract_column(A	)
             _UpperCAmelCase					:  Optional[Any]											=   self.python_features_decoder.decode_column(A						, pa_table.column_names[0]	)
             _UpperCAmelCase					:  Tuple											=   self.recursive_tensorize(A	)
             _UpperCAmelCase					:  Tuple											=   self._consolidate(A	)
             return column





      def 					_A     (	self						:    Union[str, Any]						, A						:    pa.Table	):
             _UpperCAmelCase					:  Any											=   self.numpy_arrow_extractor().extract_batch(A	)
             _UpperCAmelCase					:  Any											=   self.python_features_decoder.decode_batch(A	)
             _UpperCAmelCase					:  List[str]											=   self.recursive_tensorize(A	)
             for column_name in batch:
                    _UpperCAmelCase					:  Optional[Any]											=   self._consolidate(batch[column_name]	)
             return batch

