from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__lowercase											=      logging.get_logger(__name__)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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

    __UpperCamelCase  :List[Any]    						=	tf.shape(SCREAMING_SNAKE_CASE			)

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

    __UpperCamelCase  :Tuple    						=	tensor.shape.as_list()

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = None						,    SCREAMING_SNAKE_CASE = None			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=1e-5						,    SCREAMING_SNAKE_CASE=-1			):
    '''simple docstring'''







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

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

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=0						,    SCREAMING_SNAKE_CASE=-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

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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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

    return encoder_extended_attention_mask



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = "input_ids"			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	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.
    __UpperCamelCase  :Dict    						=	[x for x in data if len(SCREAMING_SNAKE_CASE			) > 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  :int    						=	np.asarray(SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[int]    						=	1
    __UpperCamelCase  :List[Any]    						=	np.array_split(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # 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  :List[Any]    						=	np.array_split(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    if num_chunks > 1:
        for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE			):
            __UpperCamelCase  :int    						=	chunk_data
    else:
        __UpperCamelCase  :Tuple    						=	data



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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

    return tf.nest.map_structure(_expand_single_ad_tensor						,    SCREAMING_SNAKE_CASE			)






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   CpmAntTokenizer
    a__			:		Optional[Any]      			=   False







    def     UpperCamelCase__     (			self) ->			Any:
        super().setUp()

        __UpperCamelCase  :Optional[int]    						=	[
            '''<d>''',
            '''</d>''',
            '''<s>''',
            '''</s>''',
            '''</_>''',
            '''<unk>''',
            '''<pad>''',
            '''</n>''',
            '''我''',
            '''是''',
            '''C''',
            '''P''',
            '''M''',
            '''A''',
            '''n''',
            '''t''',
        ]
        __UpperCamelCase  :Optional[int]    						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES['''vocab_file'''])
        with open(self.vocab_file				,				'''w'''				,				encoding='''utf-8''') as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))







    @tooslow
    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Tuple    						=	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''')
        __UpperCamelCase  :Dict    						=	'''今天天气真好！'''
        __UpperCamelCase  :Tuple    						=	['''今天''', '''天气''', '''真''', '''好''', '''！''']
        __UpperCamelCase  :Optional[Any]    						=	tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)
        __UpperCamelCase  :int    						=	'''今天天气真好！'''
        __UpperCamelCase  :List[str]    						=	[tokenizer.bos_token] + tokens

        __UpperCamelCase  :List[str]    						=	[6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)				,				__lowercase)

        __UpperCamelCase  :Dict    						=	tokenizer.decode(__lowercase)
        self.assertEqual(__lowercase				,				__lowercase)





