






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


snake_case    				=		logging.get_logger(__name__)


def   lowerCamelCase__			(     lowercase				):

		"""simple docstring"""






		if isinstance(lowercase						,     np.ndarray				):
				return list(tensor.shape				)

		SCREAMING_SNAKE_CASE       :      Optional[int]							=    tf.shape(lowercase				)

		if tensor.shape == tf.TensorShape(lowercase				):
				return dynamic

		SCREAMING_SNAKE_CASE       :      List[Any]							=    tensor.shape.as_list()

		return [dynamic[i] if s is None else s for i, s in enumerate(lowercase				)]


def   lowerCamelCase__			(     lowercase						,     lowercase = None						,     lowercase = None				):

		"""simple docstring"""






		return tf.nn.softmax(logits=logits + 1E-9						,     axis=lowercase						,     name=lowercase				)


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase=1E-5						,     lowercase=-1				):

		"""simple docstring"""






		if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowercase						,     lowercase				):
				raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis."				)

		# Get mean and variance on the axis to be normalized
		SCREAMING_SNAKE_CASE       :      str							=    tf.nn.moments(lowercase						,     axes=[axis]						,     keepdims=lowercase				)

		if axis != -1:
				# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
				# on every dimension except axis
				SCREAMING_SNAKE_CASE       :      Dict							=    [1] * inputs.shape.rank
				SCREAMING_SNAKE_CASE       :      List[Any]							=    shape_list(lowercase				)[axis]
				SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tf.reshape(lowercase						,     lowercase				)
				SCREAMING_SNAKE_CASE       :      Dict							=    tf.reshape(lowercase						,     lowercase				)

		# Compute layer normalization using the batch_normalization
		# function.
		SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    tf.nn.batch_normalization(
		    lowercase						,     lowercase						,     lowercase						,     offset=lowercase						,     scale=lowercase						,     variance_epsilon=lowercase						,     )
		return outputs


def   lowerCamelCase__			(     lowercase						,     lowercase=0						,     lowercase=-1				):

		"""simple docstring"""






		if end_dim < 0:
				end_dim += input.shape.rank
		if start_dim < 0:
				start_dim += input.shape.rank

		if start_dim == end_dim:
				return input

		SCREAMING_SNAKE_CASE       :      Dict							=    tf.shape(lowercase				)
		SCREAMING_SNAKE_CASE       :      List[Any]							=    tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]				)
		SCREAMING_SNAKE_CASE       :      Optional[int]							=    tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,     axis=0				)
		return tf.reshape(lowercase						,     lowercase				)


def   lowerCamelCase__			(     lowercase				):

		"""simple docstring"""






		if not isinstance(lowercase						,     tf.Tensor				):
				SCREAMING_SNAKE_CASE       :      Tuple							=    tf.convert_to_tensor(lowercase				)  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				SCREAMING_SNAKE_CASE       :      Dict							=    encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				SCREAMING_SNAKE_CASE       :      Optional[int]							=    encoder_attention_mask[:, None, None, :]
		# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
		# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
		# /transformer/transformer_layers.py#L270
		# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
		# encoder_extended_attention_mask.transpose(-1, -2))
		SCREAMING_SNAKE_CASE       :      int							=    (
		    tf.cast(1						,     encoder_attention_mask.dtype				) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase = "input_ids"				):

		"""simple docstring"""






		tf.debugging.assert_less(
		    lowercase						,     tf.cast(lowercase						,     dtype=tensor.dtype				)						,     message=(
		        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(lowercase				)}) must be smaller than the embedding '''
		        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
		    )						,     )


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

		"""simple docstring"""






		SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    64512
		# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
		# because in that case even chunking the array would not make the saving
		# possible.
		SCREAMING_SNAKE_CASE       :      Optional[int]							=    [x for x in data if len(lowercase				) > HDF5_OBJECT_HEADER_LIMIT]

		# Expecting this to never be true.
		if bad_attributes:
				raise RuntimeError(
				    "The following attributes cannot be saved to HDF5 file because "
				    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
				    F'''bytes: {bad_attributes}'''				)

		SCREAMING_SNAKE_CASE       :      Dict							=    np.asarray(lowercase				)

		SCREAMING_SNAKE_CASE       :      Optional[Any]							=    1
		SCREAMING_SNAKE_CASE       :      str							=    np.array_split(lowercase						,     lowercase				)

		# This will never loop forever thanks to the test above.
		while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data				):
				num_chunks += 1
				SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.array_split(lowercase						,     lowercase				)

		if num_chunks > 1:
				for chunk_id, chunk_data in enumerate(lowercase				):
						SCREAMING_SNAKE_CASE       :      List[Any]							=    chunk_data
		else:
				SCREAMING_SNAKE_CASE       :      List[str]							=    data


def   lowerCamelCase__			(     lowercase						,     lowercase				):

		"""simple docstring"""






		if name in group.attrs:
				SCREAMING_SNAKE_CASE       :      Tuple							=    [n.decode("utf8"				) if hasattr(lowercase						,     "decode"				) else n for n in group.attrs[name]]
		else:
				SCREAMING_SNAKE_CASE       :      List[str]							=    []
				SCREAMING_SNAKE_CASE       :      Dict							=    0
				while "%s%d" % (name, chunk_id) in group.attrs:
						data.extend(
						    [n.decode("utf8"				) if hasattr(lowercase						,     "decode"				) else n for n in group.attrs["%s%d" % (name, chunk_id)]]				)
						chunk_id += 1
		return data


def   lowerCamelCase__			(     lowercase				):

		"""simple docstring"""






		def _expand_single_ad_tensor(lowercase				):
				if isinstance(lowercase						,     tf.Tensor				) and t.shape.rank == 1:
						return tf.expand_dims(lowercase						,     axis=-1				)
				return t

		return tf.nest.map_structure(_expand_single_ad_tensor						,     lowercase				)














from typing import TYPE_CHECKING

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


snake_case    				=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """SwinForImageClassification""",
       """SwinForMaskedImageModeling""",
       """SwinModel""",
       """SwinPreTrainedModel""",
       """SwinBackbone""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFSwinForImageClassification""",
       """TFSwinForMaskedImageModeling""",
       """TFSwinModel""",
       """TFSwinPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






