



'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCamelCase__:  Tuple				      =     logging.get_logger(__name__)


def 					snake_case_	(   _lowerCAmelCase    :		Dict    ) ->  Any:
							if isinstance(UpperCamelCase_  , np.ndarray    ):
														return list(tensor.shape    )

							UpperCAmelCase					:			Dict									=						tf.shape(UpperCamelCase_    )

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

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

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


def 					snake_case_	(   _lowerCAmelCase    :		Optional[Any]  , _lowerCAmelCase    :		List[Any] = None  , _lowerCAmelCase    :		Any = None    ) ->  Dict:
							return tf.nn.softmax(logits=logits + 1e-9  , axis=UpperCamelCase_  , name=UpperCamelCase_    )


def 					snake_case_	(   _lowerCAmelCase    :		List[Any]  , _lowerCAmelCase    :		List[Any]  , _lowerCAmelCase    :		Union[str, Any]  , _lowerCAmelCase    :		Any=1e-5  , _lowerCAmelCase    :		List[str]=-1    ) ->  Tuple:
							# 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(UpperCamelCase_  , UpperCamelCase_    ):
														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
							UpperCAmelCase				,						UpperCAmelCase					:			str									=						tf.nn.moments(UpperCamelCase_  , axes=[axis]  , keepdims=UpperCamelCase_    )

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

							# Compute layer normalization using the batch_normalization
							# function.
							UpperCAmelCase					:			Dict									=						tf.nn.batch_normalization(
							    UpperCamelCase_  , UpperCamelCase_  , UpperCamelCase_  , offset=UpperCamelCase_  , scale=UpperCamelCase_  , variance_epsilon=UpperCamelCase_  , )
							return outputs


def 					snake_case_	(   _lowerCAmelCase    :		List[Any]  , _lowerCAmelCase    :		List[str]=0  , _lowerCAmelCase    :		str=-1    ) ->  List[str]:
							# 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

							UpperCAmelCase					:			int									=						tf.shape(UpperCamelCase_    )
							UpperCAmelCase					:			Tuple									=						tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]    )
							UpperCAmelCase					:			str									=						tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  , axis=0    )
							return tf.reshape(UpperCamelCase_  , UpperCamelCase_    )


def 					snake_case_	(   _lowerCAmelCase    :		Dict    ) ->  int:
							if not isinstance(UpperCamelCase_  , tf.Tensor    ):
														UpperCAmelCase					:			List[Any]									=						tf.convert_to_tensor(UpperCamelCase_    )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														UpperCAmelCase					:			Optional[int]									=						encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														UpperCAmelCase					:			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))
							UpperCAmelCase					:			Any									=						(
							    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_	(   _lowerCAmelCase    :		Dict  , _lowerCAmelCase    :		Optional[int]  , _lowerCAmelCase    :		Dict = "input_ids"    ) ->  Optional[Any]:
							tf.debugging.assert_less(
							    UpperCamelCase_  , tf.cast(UpperCamelCase_  , dtype=tensor.dtype    )  , message=(
							        f"""The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCamelCase_    )}) 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_	(   _lowerCAmelCase    :		Dict  , _lowerCAmelCase    :		Optional[Any]  , _lowerCAmelCase    :		str    ) ->  Optional[Any]:
							UpperCAmelCase					:			Dict									=						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.
							UpperCAmelCase					:			List[Any]									=						[x for x in data if len(UpperCamelCase_    ) > 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}"""    )

							UpperCAmelCase					:			Any									=						np.asarray(UpperCamelCase_    )

							UpperCAmelCase					:			Dict									=						1
							UpperCAmelCase					:			str									=						np.array_split(UpperCamelCase_  , UpperCamelCase_    )

							# 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
														UpperCAmelCase					:			int									=						np.array_split(UpperCamelCase_  , UpperCamelCase_    )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase_    ):
																					UpperCAmelCase					:			Dict									=						chunk_data
							else:
														UpperCAmelCase					:			List[str]									=						data


def 					snake_case_	(   _lowerCAmelCase    :		List[str]  , _lowerCAmelCase    :		Any    ) ->  Any:
							if name in group.attrs:
														UpperCAmelCase					:			Optional[Any]									=						[n.decode('''utf8'''    ) if hasattr(UpperCamelCase_  , '''decode'''    ) else n for n in group.attrs[name]]
							else:
														UpperCAmelCase					:			Union[str, Any]									=						[]
														UpperCAmelCase					:			List[Any]									=						0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode('''utf8'''    ) if hasattr(UpperCamelCase_  , '''decode'''    ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]    )
																					chunk_id += 1
							return data




def 					snake_case_	(   _lowerCAmelCase    :		Tuple    ) ->  Optional[int]:

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

							return tf.nest.map_structure(_expand_single_ad_tensor  , UpperCamelCase_    )









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						="gptsan-japanese"
						_UpperCAmelCase		:       Union[str, Any]   						=[
						    "past_key_values",
						]
						_UpperCAmelCase		:       int   						={
						    "hidden_size": "d_model",
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      Dict=3_60_00 ,  lowerCAmelCase							:      Union[str, Any]=12_80 ,  lowerCAmelCase							:      Tuple=10_24 ,  lowerCAmelCase							:      Any=81_92 ,  lowerCAmelCase							:      List[str]=40_96 ,  lowerCAmelCase							:      Dict=1_28 ,  lowerCAmelCase							:      int=10 ,  lowerCAmelCase							:      str=0 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      str=1_28 ,  lowerCAmelCase							:      Dict=0.0 ,  lowerCAmelCase							:      List[str]=1e-5 ,  lowerCAmelCase							:      List[str]=False ,  lowerCAmelCase							:      int=0.0 ,  lowerCAmelCase							:      Tuple="float32" ,  lowerCAmelCase							:      Optional[int]=False ,  lowerCAmelCase							:      int=False ,  lowerCAmelCase							:      Tuple=False ,  lowerCAmelCase							:      Optional[Any]=0.0_0_2 ,  lowerCAmelCase							:      Tuple=False ,  lowerCAmelCase							:      str=True ,  lowerCAmelCase							:      Optional[int]=3_59_98 ,  lowerCAmelCase							:      Optional[Any]=3_59_95 ,  lowerCAmelCase							:      Tuple=3_59_99 ,  **lowerCAmelCase							:      Dict ,  ):
													A_			    =		vocab_size
													A_			    =		max_position_embeddings
													A_			    =		d_model
													A_			    =		d_ff
													A_			    =		d_ext
													A_			    =		d_spout
													A_			    =		num_switch_layers
													A_			    =		num_ext_layers
													A_			    =		num_switch_layers + num_ext_layers
													A_			    =		num_heads
													A_			    =		num_experts
													A_			    =		expert_capacity
													A_			    =		dropout_rate
													A_			    =		layer_norm_epsilon
													A_			    =		router_bias
													A_			    =		router_jitter_noise
													A_			    =		router_dtype
													A_			    =		router_ignore_padding_tokens
													A_			    =		output_hidden_states
													A_			    =		output_attentions
													A_			    =		initializer_factor
													A_			    =		output_router_logits
													A_			    =		use_cache

													super().__init__(
													    separator_token_id=lowerCAmelCase ,  pad_token_id=lowerCAmelCase ,  eos_token_id=lowerCAmelCase ,  **lowerCAmelCase ,  )





