

from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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







def       A_				(   _UpperCAmelCase   ):
						if isinstance(_UpperCAmelCase    ,     np.ndarray   ):
												return list(tensor.shape   )

						SCREAMING_SNAKE_CASE_:			Any  		=	tf.shape(_UpperCAmelCase   )

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

						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tensor.shape.as_list()

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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase = None    ,     _UpperCAmelCase = None   ):
						return tf.nn.softmax(logits=logits + 1e-9    ,     axis=_UpperCAmelCase    ,     name=_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=1e-5    ,     _UpperCAmelCase=-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(_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
						SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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
												SCREAMING_SNAKE_CASE_:			List[str]  		=	[1] * inputs.shape.rank
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	shape_list(_UpperCAmelCase   )[axis]
												SCREAMING_SNAKE_CASE_:			int  		=	tf.reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			int  		=	tf.reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )

						# Compute layer normalization using the batch_normalization
						# function.
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	tf.nn.batch_normalization(
						    _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     offset=_UpperCAmelCase    ,     scale=_UpperCAmelCase    ,     variance_epsilon=_UpperCAmelCase    ,     )
						return outputs







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=0    ,     _UpperCAmelCase=-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_:			List[str]  		=	tf.shape(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]    ,     axis=0   )
						return tf.reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )







def       A_				(   _UpperCAmelCase   ):
						if not isinstance(_UpperCAmelCase    ,     tf.Tensor   ):
												SCREAMING_SNAKE_CASE_:			Any  		=	tf.convert_to_tensor(_UpperCAmelCase   )  # Catches stray NumPy inputs
						if encoder_attention_mask.shape.rank == 3:
												SCREAMING_SNAKE_CASE_:			Tuple  		=	encoder_attention_mask[:, None, :, :]
						if encoder_attention_mask.shape.rank == 2:
												SCREAMING_SNAKE_CASE_:			str  		=	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_:			Union[str, Any]  		=	(
						    tf.cast(1    ,     encoder_attention_mask.dtype   ) - encoder_extended_attention_mask
						) * encoder_extended_attention_mask.dtype.min

						return encoder_extended_attention_mask







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = "input_ids"   ):
						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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	6_45_12
						# 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[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}"   )

						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.asarray(_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			str  		=	1
						SCREAMING_SNAKE_CASE_:			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
												SCREAMING_SNAKE_CASE_:			Any  		=	np.array_split(_UpperCAmelCase    ,     _UpperCAmelCase   )

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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						if name in group.attrs:
												SCREAMING_SNAKE_CASE_:			str  		=	[n.decode("utf8"   ) if hasattr(_UpperCAmelCase    ,     "decode"   ) else n for n in group.attrs[name]]
						else:
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
												SCREAMING_SNAKE_CASE_:			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       A_				(   _UpperCAmelCase   ):

						def _expand_single_ad_tensor(_UpperCAmelCase   ):
												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   )









from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  List[str]            =						logging.get_logger(__name__)


lowerCAmelCase							:  Tuple            =						{
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase							:  Any            =						1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase							:  Dict            =						0

lowerCAmelCase							:  Dict            =						0xe000
lowerCAmelCase							:  str            =						0xe001
lowerCAmelCase							:  str            =						0xe002
lowerCAmelCase							:  Optional[int]            =						0xe003
lowerCAmelCase							:  List[Any]            =						0xe004

# Maps special codepoints to human-readable names.
lowerCAmelCase							:  Dict[int, str]            =						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase							:  Dict[str, int]            =						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					str=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					List[Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Optional[int]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=False       ,						lowerCAmelCase__  :					int=2048       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_:			Any  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_:			str  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE_:			Dict  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else mask_token

									super().__init__(
									    bos_token=lowerCAmelCase__       ,						eos_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						add_prefix_space=lowerCAmelCase__       ,						model_max_length=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									# Creates a mapping for looking up the IDs of special symbols.
									SCREAMING_SNAKE_CASE_:			Dict[str, int]  		=	{}
									for codepoint, name in SPECIAL_CODEPOINTS.items():
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	codepoint

									# Creates a mapping for looking up the string forms of special symbol IDs.
									SCREAMING_SNAKE_CASE_:			Dict[int, str]  		=	{
									    codepoint: name for name, codepoint in self._special_codepoints.items()
									}

									SCREAMING_SNAKE_CASE_:			List[str]  		=	UNICODE_VOCAB_SIZE
									SCREAMING_SNAKE_CASE_:			Tuple  		=	len(self._special_codepoints)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self._unicode_vocab_size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					str):
									return list(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					str):
									try:
															return ord(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid token: '{token}'")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					int):
									try:
															if index in SPECIAL_CODEPOINTS:
																					return SPECIAL_CODEPOINTS[index]
															return chr(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid id: {index}")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					Dict):
									return "".join(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			int  		=	cls + token_ids_a + sep
									if token_ids_a is not None:
															result += token_ids_a + sep
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None       ,						lowerCAmelCase__  :					bool = False):
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__       ,						token_ids_a=lowerCAmelCase__       ,						already_has_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	[1] + ([0] * len(lowerCAmelCase__)) + [1]
									if token_ids_a is not None:
															result += ([0] * len(lowerCAmelCase__)) + [1]
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Dict  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	len(cls + token_ids_a + sep) * [0]
									if token_ids_a is not None:
															result += len(token_ids_a + sep) * [1]
									return result




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									return ()






