




import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE      :				Optional[int]				      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      :				Union[str, Any]				      =[
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

__SCREAMING_SNAKE_CASE      :				Dict				      ={
    """esm1b_t33_650M_UR50S""": esm_module.pretrained.esm1b_t33_650M_UR50S,
    """esm1v_t33_650M_UR90S_1""": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    """esm1v_t33_650M_UR90S_2""": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    """esm1v_t33_650M_UR90S_3""": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    """esm1v_t33_650M_UR90S_4""": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    """esm1v_t33_650M_UR90S_5""": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    """esm2_t48_15B_UR50D""": esm_module.pretrained.esm2_t48_15B_UR50D,
    """esm2_t36_3B_UR50D""": esm_module.pretrained.esm2_t36_3B_UR50D,
    """esm2_t33_650M_UR50D""": esm_module.pretrained.esm2_t33_650M_UR50D,
    """esm2_t30_150M_UR50D""": esm_module.pretrained.esm2_t30_150M_UR50D,
    """esm2_t12_35M_UR50D""": esm_module.pretrained.esm2_t12_35M_UR50D,
    """esm2_t6_8M_UR50D""": esm_module.pretrained.esm2_t6_8M_UR50D,
    """esmfold_v1""": esmfold_va,
}

__SCREAMING_SNAKE_CASE      :				List[str]				      =list("""ARNDCQEGHILKMFPSTWYV""")

__SCREAMING_SNAKE_CASE      :				str				      =restypes + ["""X"""]
__SCREAMING_SNAKE_CASE      :				Any				      =restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]

def      lowerCAmelCase_     (					)   ->     Any:


						'''simple docstring'''




						with TemporaryDirectory() as tempdir:
												lowerCamelCase__ 					=			'\n'.join(lowerCamelCase  )
												lowerCamelCase__ 					=			Path(lowerCamelCase  ) / 'vocab.txt'
												vocab_file.write_text(lowerCamelCase  )
												lowerCamelCase__ 					=			EsmTokenizer(vocab_file=str(lowerCamelCase  )  )
						lowerCamelCase__ 					=			0  # Overlaps with 'A' but that seems to be what they want
						return hf_tokenizer

def      lowerCAmelCase_     (					lowerCamelCase:    Any       ,			lowerCamelCase:    Optional[Any]  )   ->     int:


						'''simple docstring'''




						lowerCamelCase__ 					=			our_module.load_state_dict(original_module.state_dict()  )
						if status.missing_keys:
												raise ValueError(f"""Missing keys: {status.missing_keys}"""  )
						if status.unexpected_keys:
												raise ValueError(f"""Unexpected keys: {status.unexpected_keys}"""  )

def      lowerCAmelCase_     (					lowerCamelCase:    str       ,			lowerCamelCase:    str       ,			lowerCamelCase:    bool       ,			lowerCamelCase:    str       ,			lowerCamelCase:    str  )   ->     Optional[int]:


						'''simple docstring'''




						if model.startswith('esmfold'  ):
												lowerCamelCase__ 					=			MODEL_MAPPING[model]()
						else:
												lowerCamelCase__					,						lowerCamelCase__ 					=			MODEL_MAPPING[model]()
						esm.eval()  # disable dropout

						if model.startswith('esmfold'  ):
												lowerCamelCase__ 					=			esm.esm.embed_dim
												lowerCamelCase__ 					=			esm.esm.num_layers
												lowerCamelCase__ 					=			esm.esm.attention_heads
												lowerCamelCase__ 					=			4 * embed_dim
												lowerCamelCase__ 					=			esm.esm.token_dropout
												lowerCamelCase__ 					=			False  # This code path does not exist in ESM-2
												lowerCamelCase__ 					=			'rotary'
												lowerCamelCase__ 					=			True
												lowerCamelCase__ 					=			EsmFoldConfig()
												for key, val in esm.cfg.items():
																		if hasattr(lowerCamelCase       ,			lowerCamelCase  ) and key != "trunk":
																								setattr(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
												for key, val in esm.cfg.trunk.items():
																		if hasattr(esmfold_config.trunk       ,			lowerCamelCase  ) and key != "structure_module":
																								setattr(esmfold_config.trunk       ,			lowerCamelCase       ,			lowerCamelCase  )
												for key, val in esm.cfg.trunk.structure_module.items():
																		if hasattr(esmfold_config.trunk.structure_module       ,			lowerCamelCase  ):
																								setattr(esmfold_config.trunk.structure_module       ,			lowerCamelCase       ,			lowerCamelCase  )
						elif hasattr(lowerCamelCase       ,			'args'  ):
												# Indicates an ESM-1b or ESM-1v model
												lowerCamelCase__ 					=			esm.args.embed_dim
												lowerCamelCase__ 					=			esm.args.layers
												lowerCamelCase__ 					=			esm.args.attention_heads
												lowerCamelCase__ 					=			esm.args.ffn_embed_dim
												lowerCamelCase__ 					=			esm.args.token_dropout
												lowerCamelCase__ 					=			True if esm.emb_layer_norm_before else False
												lowerCamelCase__ 					=			'absolute'
												lowerCamelCase__ 					=			False
												lowerCamelCase__ 					=			None
						else:
												# Indicates an ESM-2 model
												lowerCamelCase__ 					=			esm.embed_dim
												lowerCamelCase__ 					=			esm.num_layers
												lowerCamelCase__ 					=			esm.attention_heads
												lowerCamelCase__ 					=			4 * embed_dim  # This is hardcoded in ESM-2
												lowerCamelCase__ 					=			esm.token_dropout
												lowerCamelCase__ 					=			False  # This code path does not exist in ESM-2
												lowerCamelCase__ 					=			'rotary'
												lowerCamelCase__ 					=			False
												lowerCamelCase__ 					=			None

						if is_folding_model:
												lowerCamelCase__ 					=			esm.esm.alphabet
						lowerCamelCase__ 					=			tuple(alphabet.all_toks  )
						lowerCamelCase__ 					=			alphabet.mask_idx
						lowerCamelCase__ 					=			alphabet.padding_idx

						if is_folding_model:
												lowerCamelCase__ 					=			esm.esm
						else:
												lowerCamelCase__ 					=			esm

						lowerCamelCase__ 					=			EsmConfig(
						    vocab_size=original_esm_model.embed_tokens.num_embeddings       ,			mask_token_id=lowerCamelCase       ,			hidden_size=lowerCamelCase       ,			num_hidden_layers=lowerCamelCase       ,			num_attention_heads=lowerCamelCase       ,			intermediate_size=lowerCamelCase       ,			max_position_embeddings=10_26       ,			layer_norm_eps=1e-5       ,			attention_probs_dropout_prob=0.0       ,			hidden_dropout_prob=0.0       ,			pad_token_id=lowerCamelCase       ,			emb_layer_norm_before=lowerCamelCase       ,			token_dropout=lowerCamelCase       ,			position_embedding_type=lowerCamelCase       ,			is_folding_model=lowerCamelCase       ,			esmfold_config=lowerCamelCase       ,			vocab_list=lowerCamelCase       ,			)
						if classification_head:
												lowerCamelCase__ 					=			esm.classification_heads['mnli'].out_proj.weight.shape[0]
						print('Our ESM config:'       ,			lowerCamelCase  )

						if model.startswith('esmfold'  ):
												lowerCamelCase__ 					=			EsmForProteinFolding
						elif classification_head:
												lowerCamelCase__ 					=			EsmForSequenceClassification
						else:
												lowerCamelCase__ 					=			EsmForMaskedLM
						lowerCamelCase__ 					=			model_class(lowerCamelCase  )
						model.eval()

						# Now let's copy all the weights.
						# Embeddings
						lowerCamelCase__ 					=			original_esm_model.embed_tokens.weight
						if position_embedding_type == "absolute":
												lowerCamelCase__ 					=			original_esm_model.embed_positions.weight

						if config.emb_layer_norm_before:
												lowerCamelCase__ 					=			original_esm_model.emb_layer_norm_before.weight
												lowerCamelCase__ 					=			original_esm_model.emb_layer_norm_before.bias

						lowerCamelCase__ 					=			original_esm_model.emb_layer_norm_after.weight
						lowerCamelCase__ 					=			original_esm_model.emb_layer_norm_after.bias

						for i in range(config.num_hidden_layers  ):
												# Encoder: start of layer
												lowerCamelCase__ 					=			model.esm.encoder.layer[i]
												# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
												lowerCamelCase__ 					=			original_esm_model.layers[i]

												# self attention
												lowerCamelCase__ 					=			layer.attention.self
												assert (
												    esm_layer.self_attn.k_proj.weight.data.shape
												    == esm_layer.self_attn.q_proj.weight.data.shape
												    == esm_layer.self_attn.v_proj.weight.data.shape
												    == torch.Size((config.hidden_size, config.hidden_size)  )
												)

												lowerCamelCase__ 					=			esm_layer.self_attn.q_proj.weight
												lowerCamelCase__ 					=			esm_layer.self_attn.q_proj.bias
												lowerCamelCase__ 					=			esm_layer.self_attn.k_proj.weight
												lowerCamelCase__ 					=			esm_layer.self_attn.k_proj.bias
												lowerCamelCase__ 					=			esm_layer.self_attn.v_proj.weight
												lowerCamelCase__ 					=			esm_layer.self_attn.v_proj.bias

												if getattr(esm_layer.self_attn       ,			'rot_emb'       ,			lowerCamelCase  ) is not None:
																		# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
																		# During the training of ESM-2 the model was converted to float16 precision, which also converts
																		# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
																		# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
																		# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
																		# we make sure the new model copies the data from the old inv_freq.
																		lowerCamelCase__ 					=			esm_layer.self_attn.rot_emb.inv_freq

												# LayerNorm changes for pre-activation
												lowerCamelCase__ 					=			esm_layer.self_attn_layer_norm.weight
												lowerCamelCase__ 					=			esm_layer.self_attn_layer_norm.bias
												lowerCamelCase__ 					=			esm_layer.final_layer_norm.weight
												lowerCamelCase__ 					=			esm_layer.final_layer_norm.bias

												# self-attention output
												lowerCamelCase__ 					=			layer.attention.output
												assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
												lowerCamelCase__ 					=			esm_layer.self_attn.out_proj.weight
												lowerCamelCase__ 					=			esm_layer.self_attn.out_proj.bias

												# intermediate
												lowerCamelCase__ 					=			layer.intermediate
												assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
												lowerCamelCase__ 					=			esm_layer.fca.weight
												lowerCamelCase__ 					=			esm_layer.fca.bias

												# output
												lowerCamelCase__ 					=			layer.output
												assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
												lowerCamelCase__ 					=			esm_layer.fca.weight
												lowerCamelCase__ 					=			esm_layer.fca.bias
												# end of layer

						if is_folding_model:
												lowerCamelCase__ 					=			esm.esm_s_combine.data
												lowerCamelCase__ 					=			esm.afa_to_esm.data
												transfer_and_check_weights(esm.embedding       ,			model.embedding  )
												transfer_and_check_weights(esm.esm_s_mlp       ,			model.esm_s_mlp  )
												transfer_and_check_weights(esm.trunk       ,			model.trunk  )
												transfer_and_check_weights(esm.distogram_head       ,			model.distogram_head  )
												transfer_and_check_weights(esm.ptm_head       ,			model.ptm_head  )
												transfer_and_check_weights(esm.lm_head       ,			model.lm_head  )
												transfer_and_check_weights(esm.lddt_head       ,			model.lddt_head  )

						elif classification_head:
												lowerCamelCase__ 					=			esm.esm.classification_heads['mnli'].dense.weight
												lowerCamelCase__ 					=			esm.classification_heads['mnli'].dense.bias
												lowerCamelCase__ 					=			esm.classification_heads['mnli'].out_proj.weight
												lowerCamelCase__ 					=			esm.classification_heads['mnli'].out_proj.bias
						else:
												# LM Head
												lowerCamelCase__ 					=			esm.lm_head.dense.weight
												lowerCamelCase__ 					=			esm.lm_head.dense.bias
												lowerCamelCase__ 					=			esm.lm_head.layer_norm.weight
												lowerCamelCase__ 					=			esm.lm_head.layer_norm.bias
												lowerCamelCase__ 					=			esm.lm_head.weight
												lowerCamelCase__ 					=			esm.lm_head.bias

						# Contact prediction head
						transfer_and_check_weights(esm.contact_head       ,			model.esm.contact_head  )

						# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
						if is_folding_model:
												# Folding models aren't trained on masked inputs and don't like mask tokens.
												lowerCamelCase__ 					=			SAMPLE_DATA[:2]
						else:
												lowerCamelCase__ 					=			SAMPLE_DATA

						if is_folding_model:
												lowerCamelCase__ 					=			get_esmfold_tokenizer()
												lowerCamelCase__ 					=			hf_tokenizer(
												    [row[1] for row in sample_data]       ,			return_tensors='pt'       ,			padding=lowerCamelCase       ,			add_special_tokens=lowerCamelCase  )
												lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			esmfold_encode_sequences([row[1] for row in sample_data]  )
												lowerCamelCase__ 					=			torch.all(hf_tokens['input_ids'] == esmfold_aas  ) and torch.all(
												    hf_tokens['attention_mask'] == esmfold_mask  )
						else:
												# Let's check that we get the same results.
												lowerCamelCase__ 					=			alphabet.get_batch_converter()
												lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			batch_converter(lowerCamelCase  )
												# Prepare tokenizer and make sure it matches
												with TemporaryDirectory() as tempdir:
																		lowerCamelCase__ 					=			'\n'.join(alphabet.all_toks  )
																		lowerCamelCase__ 					=			Path(lowerCamelCase  ) / 'vocab.txt'
																		vocab_file.write_text(lowerCamelCase  )
																		lowerCamelCase__ 					=			EsmTokenizer(vocab_file=str(lowerCamelCase  )  )

												lowerCamelCase__ 					=			hf_tokenizer([row[1] for row in sample_data]       ,			return_tensors='pt'       ,			padding=lowerCamelCase  )
												lowerCamelCase__ 					=			torch.all(hf_tokens['input_ids'] == batch_tokens  )

						print('Do both models tokenizers output the same tokens?'       ,			'🔥' if success else '💩'  )
						if not success:
												raise Exception('Tokenization does not match!'  )

						with torch.no_grad():
												if is_folding_model:
																		# Let's test the model in parts
																		# ESMFold always converts the ESM stem to float16, which requires float16 ops
																		# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
																		# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
																		# original and the converted model on the GPU at the same time.
																		lowerCamelCase__ 					=			esm.cuda().infer([row[1] for row in sample_data]  )
																		lowerCamelCase__ 					=			model.cuda()(
																		    input_ids=hf_tokens['input_ids'].cuda()       ,			attention_mask=hf_tokens['attention_mask'].cuda()  )
												else:
																		lowerCamelCase__ 					=			model(**lowerCamelCase       ,			output_hidden_states=lowerCamelCase  )
																		lowerCamelCase__ 					=			our_output['logits']
																		if classification_head:
																								lowerCamelCase__ 					=			esm.model.classification_heads['mnli'](esm.extract_features(lowerCamelCase  )  )
																		else:
																								lowerCamelCase__ 					=			esm(hf_tokens['input_ids']       ,			repr_layers=list(range(9_99  )  )  )
																								lowerCamelCase__ 					=			their_output['logits']

												if is_folding_model:
																		lowerCamelCase__ 					=			torch.max(torch.abs(our_output['positions'] - their_output['positions']  )  ).item()
																		lowerCamelCase__ 					=			torch.allclose(our_output['positions']       ,			their_output['positions']       ,			atol=1e-5  )
												else:
																		lowerCamelCase__ 					=			torch.max(torch.abs(our_output - their_output  )  ).item()
																		lowerCamelCase__ 					=			torch.allclose(lowerCamelCase       ,			lowerCamelCase       ,			atol=1e-5  )

												print(f"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-5
												print('Do both models output the same tensors?'       ,			'🔥' if success else '💩'  )

												if not success:
																		raise Exception('Something went wRoNg'  )

												if not is_folding_model:
																		# Let's check contact prediction too
																		lowerCamelCase__ 					=			model.predict_contacts(hf_tokens['input_ids']       ,			hf_tokens['attention_mask']  )
																		lowerCamelCase__ 					=			esm.predict_contacts(hf_tokens['input_ids']  )
																		lowerCamelCase__ 					=			torch.max(torch.abs(our_output - their_output  )  ).item()
																		lowerCamelCase__ 					=			torch.allclose(lowerCamelCase       ,			lowerCamelCase       ,			atol=1e-5  )

																		print('Contact prediction testing:'  )
																		print(f"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-5
																		print('Do both models output the same tensors?'       ,			'🔥' if success else '💩'  )

																		if not success:
																								raise Exception('Something went wRoNg'  )

												pathlib.Path(lowerCamelCase  ).mkdir(parents=lowerCamelCase       ,			exist_ok=lowerCamelCase  )
												print(f"""Saving model to {pytorch_dump_folder_path}"""  )
												model.save_pretrained(lowerCamelCase  )

												del esm  # Free up some memory before continuing

						print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""  )
						hf_tokenizer.save_pretrained(lowerCamelCase  )

						if push_to_repo:
												model.push_to_hub(repo_id=lowerCamelCase       ,			use_auth_token=lowerCamelCase  )
												hf_tokenizer.push_to_hub(repo_id=lowerCamelCase       ,			use_auth_token=lowerCamelCase  )


if __name__ == "__main__":
						__SCREAMING_SNAKE_CASE      :				Any				      =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--pytorch_dump_folder_path""", type=str, required=True, help="""Path to the output PyTorch model."""
						)
						parser.add_argument(
						    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
						)
						parser.add_argument("""--model""", default=None, type=str, required=True, help="""Name of model to convert.""")
						parser.add_argument("""--push_to_repo""", type=str, help="""Repo to upload to (including username!).""")
						parser.add_argument("""--auth_token""", type=str, help="""HuggingFace auth token.""")
						__SCREAMING_SNAKE_CASE      :				Optional[int]				      =parser.parse_args()
						convert_esm_checkpoint_to_pytorch(
						    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
						)





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
      import tensorflow as tf






class      A     (						TensorFormatter[Mapping, "tf.Tensor", Mapping]						):
     """simple docstring"""

     def __init__(      self					:				int		,					A__					:				Tuple=None		,					**A__					:				Tuple     )			->    Dict:
           super().__init__(features=A__     )
           lowerCamelCase__ 					=			tf_tensor_kwargs
           import tensorflow as tf  # noqa: import tf at initialization




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				List[str]     )			->    Dict:
           import tensorflow as tf

           if isinstance(A__		,					A__     ) and column:
                 if all(
                     isinstance(A__		,					tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                       return tf.stack(A__     )
                 elif all(
                     isinstance(A__		,					(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                       # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                       return tf.ragged.stack(A__     )

           return column




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Optional[int]     )			->    int:
           import tensorflow as tf

           if value is None:
                 return value

           lowerCamelCase__ 					=			{}

           if isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.integer     ):
                 lowerCamelCase__ 					=			{'dtype': tf.intaa}
           elif isinstance(A__		,					(np.number, np.ndarray)     ) and np.issubdtype(value.dtype		,					np.floating     ):
                 lowerCamelCase__ 					=			{'dtype': tf.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(A__		,					PIL.Image.Image     ):
                       lowerCamelCase__ 					=			np.asarray(A__     )

           return tf.convert_to_tensor(A__		,					**{**default_dtype, **self.tf_tensor_kwargs}     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Dict     )			->    List[str]:
           import tensorflow as tf

           # 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__		,					tf.Tensor     ):
                 lowerCamelCase__ 					=			data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(A__		,					np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                       return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           elif isinstance(A__		,					(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A__     ) for substruct in data_struct]     )
           return self._tensorize(A__     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				dict     )			->    List[str]:
           return map_nested(self._recursive_tensorize		,					A__		,					map_list=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_row(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_row(A__     )
           return self.recursive_tensorize(A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				pa.Table     )			->    "tf.Tensor":
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_column(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_column(A__		,					pa_table.column_names[0]     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           lowerCamelCase__ 					=			self._consolidate(A__     )
           return column




     def 	__snake_case				(      self					:				int		,					A__					:				pa.Table     )			->    Mapping:
           lowerCamelCase__ 					=			self.numpy_arrow_extractor().extract_batch(A__     )
           lowerCamelCase__ 					=			self.python_features_decoder.decode_batch(A__     )
           lowerCamelCase__ 					=			self.recursive_tensorize(A__     )
           for column_name in batch:
                 lowerCamelCase__ 					=			self._consolidate(batch[column_name]     )
           return batch