

# 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 torch


class SCREAMING_SNAKE_CASE	(		TensorFormatter[Mapping, """torch.Tensor""", Mapping]				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase=None			,					**__UpperCamelCase				):

				"""simple docstring"""


				super().__init__(features=__UpperCamelCase				)
				snake_case_   		=			torch_tensor_kwargs
				import torch  # noqa import torch at initialization


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				import torch

				if isinstance(__UpperCamelCase			,					__UpperCamelCase				) and column:
					if all(
					    isinstance(__UpperCamelCase			,					torch.Tensor				) and x.shape == column[0].shape and x.dtype == column[0].dtype
					    for x in column				):
						return torch.stack(__UpperCamelCase				)
				return column


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				import torch

				if isinstance(__UpperCamelCase			,					(str, bytes, type(__UpperCamelCase				))				):
					return value
				elif isinstance(__UpperCamelCase			,					(np.character, np.ndarray)				) and np.issubdtype(value.dtype			,					np.character				):
					return value.tolist()

				snake_case_   		=			{}

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

					if isinstance(__UpperCamelCase			,					PIL.Image.Image				):
						snake_case_   		=			np.asarray(__UpperCamelCase				)
				return torch.tensor(__UpperCamelCase			,					**{**default_dtype, **self.torch_tensor_kwargs}				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				import torch

				# support for torch, tf, jax etc.
				if hasattr(__UpperCamelCase			,					'__array__'				) and not isinstance(__UpperCamelCase			,					torch.Tensor				):
					snake_case_   		=			data_struct.__array__()
				# support for nested types like struct of list of struct
				if isinstance(__UpperCamelCase			,					np.ndarray				):
					if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
						return self._consolidate([self.recursive_tensorize(__UpperCamelCase				) for substruct in data_struct]				)
				elif isinstance(__UpperCamelCase			,					(list, tuple)				):
					return self._consolidate([self.recursive_tensorize(__UpperCamelCase				) for substruct in data_struct]				)
				return self._tensorize(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				return map_nested(self._recursive_tensorize			,					__UpperCamelCase			,					map_list=__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.numpy_arrow_extractor().extract_row(__UpperCamelCase				)
				snake_case_   		=			self.python_features_decoder.decode_row(__UpperCamelCase				)
				return self.recursive_tensorize(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.numpy_arrow_extractor().extract_column(__UpperCamelCase				)
				snake_case_   		=			self.python_features_decoder.decode_column(__UpperCamelCase			,					pa_table.column_names[0]				)
				snake_case_   		=			self.recursive_tensorize(__UpperCamelCase				)
				snake_case_   		=			self._consolidate(__UpperCamelCase				)
				return column







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self.numpy_arrow_extractor().extract_batch(__UpperCamelCase				)
				snake_case_   		=			self.python_features_decoder.decode_batch(__UpperCamelCase				)
				snake_case_   		=			self.recursive_tensorize(__UpperCamelCase				)
				for column_name in batch:
					snake_case_   		=			self._consolidate(batch[column_name]				)
				return batch



from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


A									=  logging.get_logger(__name__)

A									=  'T5Config'


class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig







class SCREAMING_SNAKE_CASE	(		__snake_case				):






			"""simple docstring"""




			__A    	=	"""mt5"""
			__A    	=	MTaConfig
