



from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


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

def   _lowerCAmelCase   (						A__	):
      if isinstance(A__						,  np.ndarray	):
            return list(tensor.shape	)

      lowercase__     =  tf.shape(A__	)

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

      lowercase__     =  tensor.shape.as_list()

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

def   _lowerCAmelCase   (						A__						,  A__ = None						,  A__ = None	):
      return tf.nn.softmax(logits=logits + 1E-9						,  axis=A__						,  name=A__	)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__=1E-5						,  A__=-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(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
      lowercase__,	lowercase__     =  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
            lowercase__     =  [1] * inputs.shape.rank
            lowercase__     =  shape_list(A__	)[axis]
            lowercase__     =  tf.reshape(A__						,  A__	)
            lowercase__     =  tf.reshape(A__						,  A__	)

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

def   _lowerCAmelCase   (						A__						,  A__=0						,  A__=-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

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

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

      return encoder_extended_attention_mask

def   _lowerCAmelCase   (						A__						,  A__						,  A__ = "input_ids"	):
      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   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  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.
      lowercase__     =  [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}'''	)

      lowercase__     =  np.asarray(A__	)

      lowercase__     =  1
      lowercase__     =  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
            lowercase__     =  np.array_split(A__						,  A__	)

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

def   _lowerCAmelCase   (						A__						,  A__	):
      if name in group.attrs:
            lowercase__     =  [n.decode('utf8'	) if hasattr(A__						,  'decode'	) else n for n in group.attrs[name]]
      else:
            lowercase__     =  []
            lowercase__     =  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   _lowerCAmelCase   (						A__	):

      def _expand_single_ad_tensor(A__	):
            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__	)





import math
import sys

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  ''
      try:
            with open(A__						,  'rb'	) as binary_file:
                  lowercase__     =  binary_file.read()
            for dat in data:
                  lowercase__     =  F'''{dat:08b}'''
                  result += curr_byte
            return result
      except OSError:
            print('File not accessible'	)
            sys.exit()

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  {'0': '0', '1': '1'}
      lowercase__,	lowercase__     =  '', ''
      lowercase__     =  len(A__	)

      for i in range(len(A__	)	):
            curr_string += data_bits[i]
            if curr_string not in lexicon:
                  continue

            lowercase__     =  lexicon[curr_string]
            result += last_match_id
            lowercase__     =  last_match_id + '0'

            if math.loga(A__	).is_integer():
                  lowercase__     =  {}
                  for curr_key in list(A__	):
                        lowercase__     =  lexicon.pop(A__	)
                  lowercase__     =  new_lex

            lowercase__     =  last_match_id + '1'
            index += 1
            lowercase__     =  ''
      return result

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  8
      try:
            with open(A__						,  'wb'	) as opened_file:
                  lowercase__     =  [
                      to_write[i : i + byte_length]
                      for i in range(0						,  len(A__	)						,  A__	)
                  ]

                  if len(result_byte_array[-1]	) % byte_length == 0:
                        result_byte_array.append('10000000'	)
                  else:
                        result_byte_array[-1] += "1" + "0" * (
                            byte_length - len(result_byte_array[-1]	) - 1
                        )

                  for elem in result_byte_array[:-1]:
                        opened_file.write(int(A__						,  2	).to_bytes(1						,  byteorder='big'	)	)
      except OSError:
            print('File not accessible'	)
            sys.exit()

def   _lowerCAmelCase   (						A__	):
      lowercase__     =  0
      for letter in data_bits:
            if letter == "1":
                  break
            counter += 1

      lowercase__     =  data_bits[counter:]
      lowercase__     =  data_bits[counter + 1 :]
      return data_bits

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  read_file_binary(A__	)
      lowercase__     =  remove_prefix(A__	)
      lowercase__     =  decompress_data(A__	)
      write_file_binary(A__						,  A__	)


if __name__ == "__main__":
       compress(sys.argv[1], sys.argv[2])
