'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowercase          =  logging.get_logger(__name__)






def 				snake_case__							(			_A:					Union[tf.Tensor, np.ndarray] )    ->	List[int]:

		'''simple docstring'''


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

		lowerCAmelCase   					=  tf.shape(_A )

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

		lowerCAmelCase   					=  tensor.shape.as_list()

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






def 				snake_case__							(			_A:					tf.Tensor      ,   _A:					Optional[int] = None      ,   _A:					Optional[str] = None )    ->	tf.Tensor:

		'''simple docstring'''


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






def 				snake_case__							(			_A:					Optional[Any]      ,   _A:					List[str]      ,   _A:					int      ,   _A:					Union[str, Any]=1e-5      ,   _A:					Optional[Any]=-1 )    ->	List[Any]:

		'''simple docstring'''


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

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

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






def 				snake_case__							(			_A:					int      ,   _A:					int=0      ,   _A:					List[str]=-1 )    ->	int:

		'''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

		lowerCAmelCase   					=  tf.shape(_A )
		lowerCAmelCase   					=  tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
		lowerCAmelCase   					=  tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]      ,   axis=0 )
		return tf.reshape(_A      ,   _A )






def 				snake_case__							(			_A:					tf.Tensor )    ->	tf.Tensor:

		'''simple docstring'''


		if not isinstance(_A      ,   tf.Tensor ):
				lowerCAmelCase   					=  tf.convert_to_tensor(_A )  # Catches stray NumPy inputs
		if encoder_attention_mask.shape.rank == 3:
				lowerCAmelCase   					=  encoder_attention_mask[:, None, :, :]
		if encoder_attention_mask.shape.rank == 2:
				lowerCAmelCase   					=  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))
		lowerCAmelCase   					=  (
		    tf.cast(1      ,   encoder_attention_mask.dtype ) - encoder_extended_attention_mask
		) * encoder_extended_attention_mask.dtype.min

		return encoder_extended_attention_mask






def 				snake_case__							(			_A:					tf.Tensor      ,   _A:					int      ,   _A:					str = "input_ids" )    ->	None:

		'''simple docstring'''


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






def 				snake_case__							(			_A:					Dict      ,   _A:					Union[str, Any]      ,   _A:					Any )    ->	List[str]:

		'''simple docstring'''


		lowerCAmelCase   					=  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.
		lowerCAmelCase   					=  [x for x in data if len(_A ) > 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}" )

		lowerCAmelCase   					=  np.asarray(_A )

		lowerCAmelCase   					=  1
		lowerCAmelCase   					=  np.array_split(_A      ,   _A )

		# 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
				lowerCAmelCase   					=  np.array_split(_A      ,   _A )

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






def 				snake_case__							(			_A:					Dict      ,   _A:					Optional[int] )    ->	List[str]:

		'''simple docstring'''


		if name in group.attrs:
				lowerCAmelCase   					=  [n.decode("""utf8""" ) if hasattr(_A      ,   """decode""" ) else n for n in group.attrs[name]]
		else:
				lowerCAmelCase   					=  []
				lowerCAmelCase   					=  0
				while "%s%d" % (name, chunk_id) in group.attrs:
						data.extend(
						    [n.decode("""utf8""" ) if hasattr(_A      ,   """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
						chunk_id += 1
		return data






def 				snake_case__							(			_A:					Any )    ->	Optional[Any]:

		'''simple docstring'''


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

		return tf.nest.map_structure(_expand_single_ad_tensor      ,   _A )





'''simple docstring'''




import math

import flax.linen as nn
import jax.numpy as jnp






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




