





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_lowerCamelCase   				=			logging.get_logger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		if isinstance(_lowerCAmelCase					,   np.ndarray    ):
				return list(tensor.shape    )

		SCREAMING_SNAKE_CASE__			=      tf.shape(_lowerCAmelCase    )

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

		SCREAMING_SNAKE_CASE__			=      tensor.shape.as_list()

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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							List[str] = None					,   UpperCamelCase__:							Dict = None    ):
		return tf.nn.softmax(logits=logits + 1e-9					,   axis=_lowerCAmelCase					,   name=_lowerCAmelCase    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Tuple					,   UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							Dict=1e-5					,   UpperCamelCase__:							List[Any]=-1    ):
		# This is a very simplified functional layernorm, designed to duplicate
		# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
		# models in Transformers.

		if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(_lowerCAmelCase					,   _lowerCAmelCase    ):
				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__	,						SCREAMING_SNAKE_CASE__			=      tf.nn.moments(_lowerCAmelCase					,   axes=[axis]					,   keepdims=_lowerCAmelCase    )

		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__			=      [1] * inputs.shape.rank
				SCREAMING_SNAKE_CASE__			=      shape_list(_lowerCAmelCase    )[axis]
				SCREAMING_SNAKE_CASE__			=      tf.reshape(_lowerCAmelCase					,   _lowerCAmelCase    )
				SCREAMING_SNAKE_CASE__			=      tf.reshape(_lowerCAmelCase					,   _lowerCAmelCase    )

		# Compute layer normalization using the batch_normalization
		# function.
		SCREAMING_SNAKE_CASE__			=      tf.nn.batch_normalization(
		    _lowerCAmelCase					,   _lowerCAmelCase					,   _lowerCAmelCase					,   offset=_lowerCAmelCase					,   scale=_lowerCAmelCase					,   variance_epsilon=_lowerCAmelCase					,   )
		return outputs






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Union[str, Any]=0					,   UpperCamelCase__:							List[str]=-1    ):
		# Replicates the behavior of torch.flatten in TF

		# If end_dim or start_dim is negative, count them from the end
		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__			=      tf.shape(_lowerCAmelCase    )
		SCREAMING_SNAKE_CASE__			=      tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]    )
		SCREAMING_SNAKE_CASE__			=      tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]					,   axis=0    )
		return tf.reshape(_lowerCAmelCase					,   _lowerCAmelCase    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
		if not isinstance(_lowerCAmelCase					,   tf.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      tf.convert_to_tensor(_lowerCAmelCase    )  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				SCREAMING_SNAKE_CASE__			=      encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				SCREAMING_SNAKE_CASE__			=      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__			=      (
		    tf.cast(1					,   encoder_attention_mask.dtype    ) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Tuple					,   UpperCamelCase__:							Optional[Any] = "input_ids"    ):
		tf.debugging.assert_less(
		    _lowerCAmelCase					,   tf.cast(_lowerCAmelCase					,   dtype=tensor.dtype    )					,   message=(
		        f'''The maximum value of {tensor_name} ({tf.math.reduce_max(_lowerCAmelCase    )}) must be smaller than the embedding '''
		        f'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
		    )					,   )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							List[Any]    ):
		SCREAMING_SNAKE_CASE__			=      64_512
		# 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__			=      [x for x in data if len(_lowerCAmelCase    ) > 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__			=      np.asarray(_lowerCAmelCase    )

		SCREAMING_SNAKE_CASE__			=      1
		SCREAMING_SNAKE_CASE__			=      np.array_split(_lowerCAmelCase					,   _lowerCAmelCase    )

		# 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__			=      np.array_split(_lowerCAmelCase					,   _lowerCAmelCase    )

		if num_chunks > 1:
				for chunk_id, chunk_data in enumerate(_lowerCAmelCase    ):
						SCREAMING_SNAKE_CASE__			=      chunk_data
		else:
				SCREAMING_SNAKE_CASE__			=      data






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any					,   UpperCamelCase__:							Union[str, Any]    ):
		if name in group.attrs:
				SCREAMING_SNAKE_CASE__			=      [n.decode("""utf8"""    ) if hasattr(_lowerCAmelCase					,   """decode"""    ) else n for n in group.attrs[name]]
		else:
				SCREAMING_SNAKE_CASE__			=      []
				SCREAMING_SNAKE_CASE__			=      0
				while "%s%d" % (name, chunk_id) in group.attrs:
						data.extend(
						    [n.decode("""utf8"""    ) if hasattr(_lowerCAmelCase					,   """decode"""    ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]    )
						chunk_id += 1
		return data






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]    ):

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

		return tf.nest.map_structure(_expand_single_ad_tensor					,   _lowerCAmelCase    )











from math import pow, sqrt






def 				SCREAMING_SNAKE_CASE__							(       *UpperCamelCase__:							float    ):
		SCREAMING_SNAKE_CASE__			=      len(UpperCamelCase__    ) > 0 and all(value > 0.0 for value in values    )
		return result






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError("""Input Error: Molar mass values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate * sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(effusion_rate / sqrt(molar_mass_a / molar_mass_a    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(molar_mass / pow(effusion_rate_a / effusion_rate_a					,   2    )					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							float					,   UpperCamelCase__:							float					,   UpperCamelCase__:							float    ):
		return (
		    round(pow(effusion_rate_a / effusion_rate_a					,   2    ) / molar_mass					,   6    )
		    if validate(UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__    )
		    else ValueError(
		        """Input Error: Molar mass and effusion rate values must greater than 0."""    )
		)