





'''simple docstring'''

# 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			,		A=None			,		**A	)		->       Tuple:
        super().__init__(features=_A	)
        _SCREAMING_SNAKE_CASE     =      tf_tensor_kwargs
        import tensorflow as tf  # noqa: import tf at initialization


    def       snake_case_(		self			,		A	)		->       int:
        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			,		A	)		->       Optional[int]:
        import tensorflow as tf

        if value is None:
            return value

        _SCREAMING_SNAKE_CASE     =      {}

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

            if isinstance(_A			,		PIL.Image.Image	):
                _SCREAMING_SNAKE_CASE     =      np.asarray(_A	)

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


    def       snake_case_(		self			,		A	)		->       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	):
            _SCREAMING_SNAKE_CASE     =      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			,		A	)		->       Tuple:
        return map_nested(self._recursive_tensorize			,		_A			,		map_list=_A	)


    def       snake_case_(		self			,		A	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.numpy_arrow_extractor().extract_row(_A	)
        _SCREAMING_SNAKE_CASE     =      self.python_features_decoder.decode_row(_A	)
        return self.recursive_tensorize(_A	)


    def       snake_case_(		self			,		A	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.numpy_arrow_extractor().extract_column(_A	)
        _SCREAMING_SNAKE_CASE     =      self.python_features_decoder.decode_column(_A			,		pa_table.column_names[0]	)
        _SCREAMING_SNAKE_CASE     =      self.recursive_tensorize(_A	)
        _SCREAMING_SNAKE_CASE     =      self._consolidate(_A	)
        return column





    def       snake_case_(		self			,		A	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      self.numpy_arrow_extractor().extract_batch(_A	)
        _SCREAMING_SNAKE_CASE     =      self.python_features_decoder.decode_batch(_A	)
        _SCREAMING_SNAKE_CASE     =      self.recursive_tensorize(_A	)
        for column_name in batch:
            _SCREAMING_SNAKE_CASE     =      self._consolidate(batch[column_name]	)
        return batch











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



