Add files using upload-large-folder tool

.gitattributes

@@ -183,3 +183,4 @@ parrot/lib/libsqlite3.so.0.8.6 filter=lfs diff=lfs merge=lfs -text
 parrot/bin/sqlite3 filter=lfs diff=lfs merge=lfs -text
 parrot/lib/libsqlite3.so filter=lfs diff=lfs merge=lfs -text
 parrot/lib/libtcl8.6.so filter=lfs diff=lfs merge=lfs -text
+wemm/lib/python3.10/site-packages/tokenizers/tokenizers.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

wemm/lib/python3.10/site-packages/Crypto/IO/PKCS8.pyi

@@ -0,0 +1,17 @@
+from typing import Tuple, Optional, Union, Callable
+from typing_extensions import NotRequired
+
+from Crypto.Util.asn1 import DerObject
+from Crypto.IO._PBES import ProtParams
+
+
+def wrap(private_key: bytes,
+         key_oid: str,
+         passphrase: Union[bytes, str] = ...,
+         protection: str = ...,
+         prot_params: Optional[ProtParams] = ...,
+         key_params: Optional[DerObject] = ...,
+         randfunc: Optional[Callable[[int], str]] = ...) -> bytes: ...
+
+
+def unwrap(p8_private_key: bytes, passphrase: Optional[Union[bytes, str]] = ...) -> Tuple[str, bytes, Optional[bytes]]: ...

wemm/lib/python3.10/site-packages/Crypto/Math/_IntegerGMP.py

@@ -0,0 +1,799 @@
+# ===================================================================
+#
+# Copyright (c) 2014, Legrandin <helderijs@gmail.com>
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+# 2. Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in
+#    the documentation and/or other materials provided with the
+#    distribution.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+# ===================================================================
+
+import sys
+import struct
+
+from Crypto.Util.py3compat import is_native_int
+
+from Crypto.Util._raw_api import (backend, load_lib,
+                                  c_ulong, c_size_t, c_uint8_ptr)
+
+from ._IntegerBase import IntegerBase
+
+gmp_defs = """typedef unsigned long UNIX_ULONG;
+        typedef struct { int a; int b; void *c; } MPZ;
+        typedef MPZ mpz_t[1];
+        typedef UNIX_ULONG mp_bitcnt_t;
+
+        void __gmpz_init (mpz_t x);
+        void __gmpz_init_set (mpz_t rop, const mpz_t op);
+        void __gmpz_init_set_ui (mpz_t rop, UNIX_ULONG op);
+
+        UNIX_ULONG __gmpz_get_ui (const mpz_t op);
+        void __gmpz_set (mpz_t rop, const mpz_t op);
+        void __gmpz_set_ui (mpz_t rop, UNIX_ULONG op);
+        void __gmpz_add (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        void __gmpz_add_ui (mpz_t rop, const mpz_t op1, UNIX_ULONG op2);
+        void __gmpz_sub_ui (mpz_t rop, const mpz_t op1, UNIX_ULONG op2);
+        void __gmpz_addmul (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        void __gmpz_addmul_ui (mpz_t rop, const mpz_t op1, UNIX_ULONG op2);
+        void __gmpz_submul_ui (mpz_t rop, const mpz_t op1, UNIX_ULONG op2);
+        void __gmpz_import (mpz_t rop, size_t count, int order, size_t size,
+                            int endian, size_t nails, const void *op);
+        void * __gmpz_export (void *rop, size_t *countp, int order,
+                              size_t size,
+                              int endian, size_t nails, const mpz_t op);
+        size_t __gmpz_sizeinbase (const mpz_t op, int base);
+        void __gmpz_sub (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        void __gmpz_mul (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        void __gmpz_mul_ui (mpz_t rop, const mpz_t op1, UNIX_ULONG op2);
+        int __gmpz_cmp (const mpz_t op1, const mpz_t op2);
+        void __gmpz_powm (mpz_t rop, const mpz_t base, const mpz_t exp, const
+                          mpz_t mod);
+        void __gmpz_powm_ui (mpz_t rop, const mpz_t base, UNIX_ULONG exp,
+                             const mpz_t mod);
+        void __gmpz_pow_ui (mpz_t rop, const mpz_t base, UNIX_ULONG exp);
+        void __gmpz_sqrt(mpz_t rop, const mpz_t op);
+        void __gmpz_mod (mpz_t r, const mpz_t n, const mpz_t d);
+        void __gmpz_neg (mpz_t rop, const mpz_t op);
+        void __gmpz_abs (mpz_t rop, const mpz_t op);
+        void __gmpz_and (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        void __gmpz_ior (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        void __gmpz_clear (mpz_t x);
+        void __gmpz_tdiv_q_2exp (mpz_t q, const mpz_t n, mp_bitcnt_t b);
+        void __gmpz_fdiv_q (mpz_t q, const mpz_t n, const mpz_t d);
+        void __gmpz_mul_2exp (mpz_t rop, const mpz_t op1, mp_bitcnt_t op2);
+        int __gmpz_tstbit (const mpz_t op, mp_bitcnt_t bit_index);
+        int __gmpz_perfect_square_p (const mpz_t op);
+        int __gmpz_jacobi (const mpz_t a, const mpz_t b);
+        void __gmpz_gcd (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        UNIX_ULONG __gmpz_gcd_ui (mpz_t rop, const mpz_t op1,
+                                     UNIX_ULONG op2);
+        void __gmpz_lcm (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        int __gmpz_invert (mpz_t rop, const mpz_t op1, const mpz_t op2);
+        int __gmpz_divisible_p (const mpz_t n, const mpz_t d);
+        int __gmpz_divisible_ui_p (const mpz_t n, UNIX_ULONG d);
+
+        size_t __gmpz_size (const mpz_t op);
+        UNIX_ULONG __gmpz_getlimbn (const mpz_t op, size_t n);
+        """
+
+if sys.platform == "win32":
+    raise ImportError("Not using GMP on Windows")
+
+lib = load_lib("gmp", gmp_defs)
+implementation = {"library": "gmp", "api": backend}
+
+if hasattr(lib, "__mpir_version"):
+    raise ImportError("MPIR library detected")
+
+
+# Lazy creation of GMP methods
+class _GMP(object):
+
+    def __getattr__(self, name):
+        if name.startswith("mpz_"):
+            func_name = "__gmpz_" + name[4:]
+        elif name.startswith("gmp_"):
+            func_name = "__gmp_" + name[4:]
+        else:
+            raise AttributeError("Attribute %s is invalid" % name)
+        func = getattr(lib, func_name)
+        setattr(self, name, func)
+        return func
+
+
+_gmp = _GMP()
+
+
+# In order to create a function that returns a pointer to
+# a new MPZ structure, we need to break the abstraction
+# and know exactly what ffi backend we have
+if implementation["api"] == "ctypes":
+    from ctypes import Structure, c_int, c_void_p, byref
+
+    class _MPZ(Structure):
+        _fields_ = [('_mp_alloc', c_int),
+                    ('_mp_size', c_int),
+                    ('_mp_d', c_void_p)]
+
+    def new_mpz():
+        return byref(_MPZ())
+
+    _gmp.mpz_getlimbn.restype = c_ulong
+
+else:
+    # We are using CFFI
+    from Crypto.Util._raw_api import ffi
+
+    def new_mpz():
+        return ffi.new("MPZ*")
+
+
+# Size of a native word
+_sys_bits = 8 * struct.calcsize("P")
+
+
+class IntegerGMP(IntegerBase):
+    """A fast, arbitrary precision integer"""
+
+    _zero_mpz_p = new_mpz()
+    _gmp.mpz_init_set_ui(_zero_mpz_p, c_ulong(0))
+
+    def __init__(self, value):
+        """Initialize the integer to the given value."""
+
+        self._mpz_p = new_mpz()
+        self._initialized = False
+
+        if isinstance(value, float):
+            raise ValueError("A floating point type is not a natural number")
+
+        if is_native_int(value):
+            _gmp.mpz_init(self._mpz_p)
+            self._initialized = True
+            if value == 0:
+                return
+
+            tmp = new_mpz()
+            _gmp.mpz_init(tmp)
+
+            try:
+                positive = value >= 0
+                reduce = abs(value)
+                slots = (reduce.bit_length() - 1) // 32 + 1
+
+                while slots > 0:
+                    slots = slots - 1
+                    _gmp.mpz_set_ui(tmp,
+                                    c_ulong(0xFFFFFFFF & (reduce >> (slots * 32))))
+                    _gmp.mpz_mul_2exp(tmp, tmp, c_ulong(slots * 32))
+                    _gmp.mpz_add(self._mpz_p, self._mpz_p, tmp)
+            finally:
+                _gmp.mpz_clear(tmp)
+
+            if not positive:
+                _gmp.mpz_neg(self._mpz_p, self._mpz_p)
+
+        elif isinstance(value, IntegerGMP):
+            _gmp.mpz_init_set(self._mpz_p, value._mpz_p)
+            self._initialized = True
+        else:
+            raise NotImplementedError
+
+    # Conversions
+    def __int__(self):
+        tmp = new_mpz()
+        _gmp.mpz_init_set(tmp, self._mpz_p)
+
+        try:
+            value = 0
+            slot = 0
+            while _gmp.mpz_cmp(tmp, self._zero_mpz_p) != 0:
+                lsb = _gmp.mpz_get_ui(tmp) & 0xFFFFFFFF
+                value |= lsb << (slot * 32)
+                _gmp.mpz_tdiv_q_2exp(tmp, tmp, c_ulong(32))
+                slot = slot + 1
+        finally:
+            _gmp.mpz_clear(tmp)
+
+        if self < 0:
+            value = -value
+        return int(value)
+
+    def __str__(self):
+        return str(int(self))
+
+    def __repr__(self):
+        return "Integer(%s)" % str(self)
+
+    # Only Python 2.x
+    def __hex__(self):
+        return hex(int(self))
+
+    # Only Python 3.x
+    def __index__(self):
+        return int(self)
+
+    def to_bytes(self, block_size=0, byteorder='big'):
+        """Convert the number into a byte string.
+
+        This method encodes the number in network order and prepends
+        as many zero bytes as required. It only works for non-negative
+        values.
+
+        :Parameters:
+          block_size : integer
+            The exact size the output byte string must have.
+            If zero, the string has the minimal length.
+          byteorder : string
+            'big' for big-endian integers (default), 'little' for litte-endian.
+        :Returns:
+          A byte string.
+        :Raise ValueError:
+          If the value is negative or if ``block_size`` is
+          provided and the length of the byte string would exceed it.
+        """
+
+        if self < 0:
+            raise ValueError("Conversion only valid for non-negative numbers")
+
+        num_limbs = _gmp.mpz_size(self._mpz_p)
+        if _sys_bits == 32:
+            spchar = "L"
+            num_limbs = max(1, num_limbs, (block_size + 3) // 4)
+        elif _sys_bits == 64:
+            spchar = "Q"
+            num_limbs = max(1, num_limbs, (block_size + 7) // 8)
+        else:
+            raise ValueError("Unknown limb size")
+
+        # mpz_getlimbn returns 0 if i is larger than the number of actual limbs
+        limbs = [_gmp.mpz_getlimbn(self._mpz_p, num_limbs - i - 1) for i in range(num_limbs)]
+
+        result = struct.pack(">" + spchar * num_limbs, *limbs)
+        cutoff_len = len(result) - block_size
+        if block_size == 0:
+            result = result.lstrip(b'\x00')
+        elif cutoff_len > 0:
+            if result[:cutoff_len] != b'\x00' * (cutoff_len):
+                raise ValueError("Number is too big to convert to "
+                                 "byte string of prescribed length")
+            result = result[cutoff_len:]
+        elif cutoff_len < 0:
+            result = b'\x00' * (-cutoff_len) + result
+
+        if byteorder == 'little':
+            result = result[::-1]
+        elif byteorder == 'big':
+            pass
+        else:
+            raise ValueError("Incorrect byteorder")
+
+        if len(result) == 0:
+            result = b'\x00'
+
+        return result
+
+    @staticmethod
+    def from_bytes(byte_string, byteorder='big'):
+        """Convert a byte string into a number.
+
+        :Parameters:
+          byte_string : byte string
+            The input number, encoded in network order.
+            It can only be non-negative.
+          byteorder : string
+            'big' for big-endian integers (default), 'little' for litte-endian.
+
+        :Return:
+          The ``Integer`` object carrying the same value as the input.
+        """
+        result = IntegerGMP(0)
+        if byteorder == 'big':
+            pass
+        elif byteorder == 'little':
+            byte_string = bytearray(byte_string)
+            byte_string.reverse()
+        else:
+            raise ValueError("Incorrect byteorder")
+        _gmp.mpz_import(
+                        result._mpz_p,
+                        c_size_t(len(byte_string)),  # Amount of words to read
+                        1,            # Big endian
+                        c_size_t(1),  # Each word is 1 byte long
+                        0,            # Endianess within a word - not relevant
+                        c_size_t(0),  # No nails
+                        c_uint8_ptr(byte_string))
+        return result
+
+    # Relations
+    def _apply_and_return(self, func, term):
+        if not isinstance(term, IntegerGMP):
+            term = IntegerGMP(term)
+        return func(self._mpz_p, term._mpz_p)
+
+    def __eq__(self, term):
+        if not (isinstance(term, IntegerGMP) or is_native_int(term)):
+            return False
+        return self._apply_and_return(_gmp.mpz_cmp, term) == 0
+
+    def __ne__(self, term):
+        if not (isinstance(term, IntegerGMP) or is_native_int(term)):
+            return True
+        return self._apply_and_return(_gmp.mpz_cmp, term) != 0
+
+    def __lt__(self, term):
+        return self._apply_and_return(_gmp.mpz_cmp, term) < 0
+
+    def __le__(self, term):
+        return self._apply_and_return(_gmp.mpz_cmp, term) <= 0
+
+    def __gt__(self, term):
+        return self._apply_and_return(_gmp.mpz_cmp, term) > 0
+
+    def __ge__(self, term):
+        return self._apply_and_return(_gmp.mpz_cmp, term) >= 0
+
+    def __nonzero__(self):
+        return _gmp.mpz_cmp(self._mpz_p, self._zero_mpz_p) != 0
+    __bool__ = __nonzero__
+
+    def is_negative(self):
+        return _gmp.mpz_cmp(self._mpz_p, self._zero_mpz_p) < 0
+
+    # Arithmetic operations
+    def __add__(self, term):
+        result = IntegerGMP(0)
+        if not isinstance(term, IntegerGMP):
+            try:
+                term = IntegerGMP(term)
+            except NotImplementedError:
+                return NotImplemented
+        _gmp.mpz_add(result._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return result
+
+    def __sub__(self, term):
+        result = IntegerGMP(0)
+        if not isinstance(term, IntegerGMP):
+            try:
+                term = IntegerGMP(term)
+            except NotImplementedError:
+                return NotImplemented
+        _gmp.mpz_sub(result._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return result
+
+    def __mul__(self, term):
+        result = IntegerGMP(0)
+        if not isinstance(term, IntegerGMP):
+            try:
+                term = IntegerGMP(term)
+            except NotImplementedError:
+                return NotImplemented
+        _gmp.mpz_mul(result._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return result
+
+    def __floordiv__(self, divisor):
+        if not isinstance(divisor, IntegerGMP):
+            divisor = IntegerGMP(divisor)
+        if _gmp.mpz_cmp(divisor._mpz_p,
+                        self._zero_mpz_p) == 0:
+            raise ZeroDivisionError("Division by zero")
+        result = IntegerGMP(0)
+        _gmp.mpz_fdiv_q(result._mpz_p,
+                        self._mpz_p,
+                        divisor._mpz_p)
+        return result
+
+    def __mod__(self, divisor):
+        if not isinstance(divisor, IntegerGMP):
+            divisor = IntegerGMP(divisor)
+        comp = _gmp.mpz_cmp(divisor._mpz_p,
+                            self._zero_mpz_p)
+        if comp == 0:
+            raise ZeroDivisionError("Division by zero")
+        if comp < 0:
+            raise ValueError("Modulus must be positive")
+        result = IntegerGMP(0)
+        _gmp.mpz_mod(result._mpz_p,
+                     self._mpz_p,
+                     divisor._mpz_p)
+        return result
+
+    def inplace_pow(self, exponent, modulus=None):
+
+        if modulus is None:
+            if exponent < 0:
+                raise ValueError("Exponent must not be negative")
+
+            # Normal exponentiation
+            if exponent > 256:
+                raise ValueError("Exponent is too big")
+            _gmp.mpz_pow_ui(self._mpz_p,
+                            self._mpz_p,   # Base
+                            c_ulong(int(exponent))
+                            )
+        else:
+            # Modular exponentiation
+            if not isinstance(modulus, IntegerGMP):
+                modulus = IntegerGMP(modulus)
+            if not modulus:
+                raise ZeroDivisionError("Division by zero")
+            if modulus.is_negative():
+                raise ValueError("Modulus must be positive")
+            if is_native_int(exponent):
+                if exponent < 0:
+                    raise ValueError("Exponent must not be negative")
+                if exponent < 65536:
+                    _gmp.mpz_powm_ui(self._mpz_p,
+                                     self._mpz_p,
+                                     c_ulong(exponent),
+                                     modulus._mpz_p)
+                    return self
+                exponent = IntegerGMP(exponent)
+            elif exponent.is_negative():
+                raise ValueError("Exponent must not be negative")
+            _gmp.mpz_powm(self._mpz_p,
+                          self._mpz_p,
+                          exponent._mpz_p,
+                          modulus._mpz_p)
+        return self
+
+    def __pow__(self, exponent, modulus=None):
+        result = IntegerGMP(self)
+        return result.inplace_pow(exponent, modulus)
+
+    def __abs__(self):
+        result = IntegerGMP(0)
+        _gmp.mpz_abs(result._mpz_p, self._mpz_p)
+        return result
+
+    def sqrt(self, modulus=None):
+        """Return the largest Integer that does not
+        exceed the square root"""
+
+        if modulus is None:
+            if self < 0:
+                raise ValueError("Square root of negative value")
+            result = IntegerGMP(0)
+            _gmp.mpz_sqrt(result._mpz_p,
+                          self._mpz_p)
+        else:
+            if modulus <= 0:
+                raise ValueError("Modulus must be positive")
+            modulus = int(modulus)
+            result = IntegerGMP(self._tonelli_shanks(int(self) % modulus, modulus))
+
+        return result
+
+    def __iadd__(self, term):
+        if is_native_int(term):
+            if 0 <= term < 65536:
+                _gmp.mpz_add_ui(self._mpz_p,
+                                self._mpz_p,
+                                c_ulong(term))
+                return self
+            if -65535 < term < 0:
+                _gmp.mpz_sub_ui(self._mpz_p,
+                                self._mpz_p,
+                                c_ulong(-term))
+                return self
+            term = IntegerGMP(term)
+        _gmp.mpz_add(self._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return self
+
+    def __isub__(self, term):
+        if is_native_int(term):
+            if 0 <= term < 65536:
+                _gmp.mpz_sub_ui(self._mpz_p,
+                                self._mpz_p,
+                                c_ulong(term))
+                return self
+            if -65535 < term < 0:
+                _gmp.mpz_add_ui(self._mpz_p,
+                                self._mpz_p,
+                                c_ulong(-term))
+                return self
+            term = IntegerGMP(term)
+        _gmp.mpz_sub(self._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return self
+
+    def __imul__(self, term):
+        if is_native_int(term):
+            if 0 <= term < 65536:
+                _gmp.mpz_mul_ui(self._mpz_p,
+                                self._mpz_p,
+                                c_ulong(term))
+                return self
+            if -65535 < term < 0:
+                _gmp.mpz_mul_ui(self._mpz_p,
+                                self._mpz_p,
+                                c_ulong(-term))
+                _gmp.mpz_neg(self._mpz_p, self._mpz_p)
+                return self
+            term = IntegerGMP(term)
+        _gmp.mpz_mul(self._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return self
+
+    def __imod__(self, divisor):
+        if not isinstance(divisor, IntegerGMP):
+            divisor = IntegerGMP(divisor)
+        comp = _gmp.mpz_cmp(divisor._mpz_p,
+                            divisor._zero_mpz_p)
+        if comp == 0:
+            raise ZeroDivisionError("Division by zero")
+        if comp < 0:
+            raise ValueError("Modulus must be positive")
+        _gmp.mpz_mod(self._mpz_p,
+                     self._mpz_p,
+                     divisor._mpz_p)
+        return self
+
+    # Boolean/bit operations
+    def __and__(self, term):
+        result = IntegerGMP(0)
+        if not isinstance(term, IntegerGMP):
+            term = IntegerGMP(term)
+        _gmp.mpz_and(result._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return result
+
+    def __or__(self, term):
+        result = IntegerGMP(0)
+        if not isinstance(term, IntegerGMP):
+            term = IntegerGMP(term)
+        _gmp.mpz_ior(result._mpz_p,
+                     self._mpz_p,
+                     term._mpz_p)
+        return result
+
+    def __rshift__(self, pos):
+        result = IntegerGMP(0)
+        if pos < 0:
+            raise ValueError("negative shift count")
+        if pos > 65536:
+            if self < 0:
+                return -1
+            else:
+                return 0
+        _gmp.mpz_tdiv_q_2exp(result._mpz_p,
+                             self._mpz_p,
+                             c_ulong(int(pos)))
+        return result
+
+    def __irshift__(self, pos):
+        if pos < 0:
+            raise ValueError("negative shift count")
+        if pos > 65536:
+            if self < 0:
+                return -1
+            else:
+                return 0
+        _gmp.mpz_tdiv_q_2exp(self._mpz_p,
+                             self._mpz_p,
+                             c_ulong(int(pos)))
+        return self
+
+    def __lshift__(self, pos):
+        result = IntegerGMP(0)
+        if not 0 <= pos < 65536:
+            raise ValueError("Incorrect shift count")
+        _gmp.mpz_mul_2exp(result._mpz_p,
+                          self._mpz_p,
+                          c_ulong(int(pos)))
+        return result
+
+    def __ilshift__(self, pos):
+        if not 0 <= pos < 65536:
+            raise ValueError("Incorrect shift count")
+        _gmp.mpz_mul_2exp(self._mpz_p,
+                          self._mpz_p,
+                          c_ulong(int(pos)))
+        return self
+
+    def get_bit(self, n):
+        """Return True if the n-th bit is set to 1.
+        Bit 0 is the least significant."""
+
+        if self < 0:
+            raise ValueError("no bit representation for negative values")
+        if n < 0:
+            raise ValueError("negative bit count")
+        if n > 65536:
+            return 0
+        return bool(_gmp.mpz_tstbit(self._mpz_p,
+                                    c_ulong(int(n))))
+
+    # Extra
+    def is_odd(self):
+        return _gmp.mpz_tstbit(self._mpz_p, 0) == 1
+
+    def is_even(self):
+        return _gmp.mpz_tstbit(self._mpz_p, 0) == 0
+
+    def size_in_bits(self):
+        """Return the minimum number of bits that can encode the number."""
+
+        if self < 0:
+            raise ValueError("Conversion only valid for non-negative numbers")
+        return _gmp.mpz_sizeinbase(self._mpz_p, 2)
+
+    def size_in_bytes(self):
+        """Return the minimum number of bytes that can encode the number."""
+        return (self.size_in_bits() - 1) // 8 + 1
+
+    def is_perfect_square(self):
+        return _gmp.mpz_perfect_square_p(self._mpz_p) != 0
+
+    def fail_if_divisible_by(self, small_prime):
+        """Raise an exception if the small prime is a divisor."""
+
+        if is_native_int(small_prime):
+            if 0 < small_prime < 65536:
+                if _gmp.mpz_divisible_ui_p(self._mpz_p,
+                                           c_ulong(small_prime)):
+                    raise ValueError("The value is composite")
+                return
+            small_prime = IntegerGMP(small_prime)
+        if _gmp.mpz_divisible_p(self._mpz_p,
+                                small_prime._mpz_p):
+            raise ValueError("The value is composite")
+
+    def multiply_accumulate(self, a, b):
+        """Increment the number by the product of a and b."""
+
+        if not isinstance(a, IntegerGMP):
+            a = IntegerGMP(a)
+        if is_native_int(b):
+            if 0 < b < 65536:
+                _gmp.mpz_addmul_ui(self._mpz_p,
+                                   a._mpz_p,
+                                   c_ulong(b))
+                return self
+            if -65535 < b < 0:
+                _gmp.mpz_submul_ui(self._mpz_p,
+                                   a._mpz_p,
+                                   c_ulong(-b))
+                return self
+            b = IntegerGMP(b)
+        _gmp.mpz_addmul(self._mpz_p,
+                        a._mpz_p,
+                        b._mpz_p)
+        return self
+
+    def set(self, source):
+        """Set the Integer to have the given value"""
+
+        if not isinstance(source, IntegerGMP):
+            source = IntegerGMP(source)
+        _gmp.mpz_set(self._mpz_p,
+                     source._mpz_p)
+        return self
+
+    def inplace_inverse(self, modulus):
+        """Compute the inverse of this number in the ring of
+        modulo integers.
+
+        Raise an exception if no inverse exists.
+        """
+
+        if not isinstance(modulus, IntegerGMP):
+            modulus = IntegerGMP(modulus)
+
+        comp = _gmp.mpz_cmp(modulus._mpz_p,
+                            self._zero_mpz_p)
+        if comp == 0:
+            raise ZeroDivisionError("Modulus cannot be zero")
+        if comp < 0:
+            raise ValueError("Modulus must be positive")
+
+        result = _gmp.mpz_invert(self._mpz_p,
+                                 self._mpz_p,
+                                 modulus._mpz_p)
+        if not result:
+            raise ValueError("No inverse value can be computed")
+        return self
+
+    def inverse(self, modulus):
+        result = IntegerGMP(self)
+        result.inplace_inverse(modulus)
+        return result
+
+    def gcd(self, term):
+        """Compute the greatest common denominator between this
+        number and another term."""
+
+        result = IntegerGMP(0)
+        if is_native_int(term):
+            if 0 < term < 65535:
+                _gmp.mpz_gcd_ui(result._mpz_p,
+                                self._mpz_p,
+                                c_ulong(term))
+                return result
+            term = IntegerGMP(term)
+        _gmp.mpz_gcd(result._mpz_p, self._mpz_p, term._mpz_p)
+        return result
+
+    def lcm(self, term):
+        """Compute the least common multiplier between this
+        number and another term."""
+
+        result = IntegerGMP(0)
+        if not isinstance(term, IntegerGMP):
+            term = IntegerGMP(term)
+        _gmp.mpz_lcm(result._mpz_p, self._mpz_p, term._mpz_p)
+        return result
+
+    @staticmethod
+    def jacobi_symbol(a, n):
+        """Compute the Jacobi symbol"""
+
+        if not isinstance(a, IntegerGMP):
+            a = IntegerGMP(a)
+        if not isinstance(n, IntegerGMP):
+            n = IntegerGMP(n)
+        if n <= 0 or n.is_even():
+            raise ValueError("n must be positive odd for the Jacobi symbol")
+        return _gmp.mpz_jacobi(a._mpz_p, n._mpz_p)
+
+    @staticmethod
+    def _mult_modulo_bytes(term1, term2, modulus):
+        if not isinstance(term1, IntegerGMP):
+            term1 = IntegerGMP(term1)
+        if not isinstance(term2, IntegerGMP):
+            term2 = IntegerGMP(term2)
+        if not isinstance(modulus, IntegerGMP):
+            modulus = IntegerGMP(modulus)
+
+        if modulus < 0:
+            raise ValueError("Modulus must be positive")
+        if modulus == 0:
+            raise ZeroDivisionError("Modulus cannot be zero")
+        if (modulus & 1) == 0:
+            raise ValueError("Odd modulus is required")
+
+        product = (term1 * term2) % modulus
+        return product.to_bytes(modulus.size_in_bytes())
+
+    # Clean-up
+    def __del__(self):
+
+        try:
+            if self._mpz_p is not None:
+                if self._initialized:
+                    _gmp.mpz_clear(self._mpz_p)
+
+            self._mpz_p = None
+        except AttributeError:
+            pass

wemm/lib/python3.10/site-packages/Crypto/Math/_IntegerGMP.pyi

@@ -0,0 +1,3 @@
+from ._IntegerBase import IntegerBase
+class IntegerGMP(IntegerBase):
+    pass

wemm/lib/python3.10/site-packages/Crypto/Signature/PKCS1_PSS.pyi

@@ -0,0 +1,28 @@
+from typing import Union, Callable, Optional
+from typing_extensions import Protocol
+
+from Crypto.PublicKey.RSA import RsaKey
+
+
+class Hash(Protocol):
+    def digest(self) -> bytes: ...
+    def update(self, bytes) -> None: ...
+
+
+class HashModule(Protocol):
+    @staticmethod
+    def new(data: Optional[bytes]) -> Hash: ...
+
+
+MaskFunction = Callable[[bytes, int, Union[Hash, HashModule]], bytes]
+RndFunction = Callable[[int], bytes]
+
+class PSS_SigScheme:
+    def __init__(self, key: RsaKey, mgfunc: MaskFunction, saltLen: int, randfunc: RndFunction) -> None: ...
+    def can_sign(self) -> bool: ...
+    def sign(self, msg_hash: Hash) -> bytes: ...
+    def verify(self, msg_hash: Hash, signature: bytes) -> bool: ...
+
+
+
+def new(rsa_key: RsaKey, mgfunc: Optional[MaskFunction]=None, saltLen: Optional[int]=None, randfunc: Optional[RndFunction]=None) -> PSS_SigScheme: ...

wemm/lib/python3.10/site-packages/Crypto/Signature/pss.pyi

@@ -0,0 +1,30 @@
+from typing import Union, Callable, Optional
+from typing_extensions import Protocol
+
+from Crypto.PublicKey.RSA import RsaKey
+
+
+class Hash(Protocol):
+    def digest(self) -> bytes: ...
+    def update(self, bytes) -> None: ...
+
+
+class HashModule(Protocol):
+    @staticmethod
+    def new(data: Optional[bytes]) -> Hash: ...
+
+
+MaskFunction = Callable[[bytes, int, Union[Hash, HashModule]], bytes]
+RndFunction = Callable[[int], bytes]
+
+class PSS_SigScheme:
+    def __init__(self, key: RsaKey, mgfunc: MaskFunction, saltLen: int, randfunc: RndFunction) -> None: ...
+    def can_sign(self) -> bool: ...
+    def sign(self, msg_hash: Hash) -> bytes: ...
+    def verify(self, msg_hash: Hash, signature: bytes) -> None: ...
+
+
+MGF1 : MaskFunction
+def _EMSA_PSS_ENCODE(mhash: Hash, emBits: int, randFunc: RndFunction, mgf:MaskFunction, sLen: int) -> str: ...
+def _EMSA_PSS_VERIFY(mhash: Hash, em: str, emBits: int, mgf: MaskFunction, sLen: int) -> None: ...
+def new(rsa_key: RsaKey, **kwargs: Union[MaskFunction, RndFunction, int]) -> PSS_SigScheme: ...

wemm/lib/python3.10/site-packages/attrs-24.3.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+pip

wemm/lib/python3.10/site-packages/attrs-24.3.0.dist-info/licenses/LICENSE

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2015 Hynek Schlawack and the attrs contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

wemm/lib/python3.10/site-packages/botocore/data/cloudfront/2014-05-31/endpoint-rule-set-1.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a06b3ceba5eaeb6c6f1759e44ec4eb1d041492dbeb669d033a0f92a05fe513a2
+size 1839

wemm/lib/python3.10/site-packages/botocore/data/elastictranscoder/2012-09-25/endpoint-rule-set-1.json.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:190a177a61ada13dd88dabd5c81eaf6885cc2c6823df30485ce2262e4d941118
+size 1153

wemm/lib/python3.10/site-packages/tokenizers/__init__.pyi

@@ -0,0 +1,1200 @@
+# Generated content DO NOT EDIT
+class AddedToken:
+    """
+    Represents a token that can be be added to a :class:`~tokenizers.Tokenizer`.
+    It can have special options that defines the way it should behave.
+
+    Args:
+        content (:obj:`str`): The content of the token
+
+        single_word (:obj:`bool`, defaults to :obj:`False`):
+            Defines whether this token should only match single words. If :obj:`True`, this
+            token will never match inside of a word. For example the token ``ing`` would match
+            on ``tokenizing`` if this option is :obj:`False`, but not if it is :obj:`True`.
+            The notion of "`inside of a word`" is defined by the word boundaries pattern in
+            regular expressions (ie. the token should start and end with word boundaries).
+
+        lstrip (:obj:`bool`, defaults to :obj:`False`):
+            Defines whether this token should strip all potential whitespaces on its left side.
+            If :obj:`True`, this token will greedily match any whitespace on its left. For
+            example if we try to match the token ``[MASK]`` with ``lstrip=True``, in the text
+            ``"I saw a [MASK]"``, we would match on ``" [MASK]"``. (Note the space on the left).
+
+        rstrip (:obj:`bool`, defaults to :obj:`False`):
+            Defines whether this token should strip all potential whitespaces on its right
+            side. If :obj:`True`, this token will greedily match any whitespace on its right.
+            It works just like :obj:`lstrip` but on the right.
+
+        normalized (:obj:`bool`, defaults to :obj:`True` with :meth:`~tokenizers.Tokenizer.add_tokens` and :obj:`False` with :meth:`~tokenizers.Tokenizer.add_special_tokens`):
+            Defines whether this token should match against the normalized version of the input
+            text. For example, with the added token ``"yesterday"``, and a normalizer in charge of
+            lowercasing the text, the token could be extract from the input ``"I saw a lion
+            Yesterday"``.
+        special (:obj:`bool`, defaults to :obj:`False` with :meth:`~tokenizers.Tokenizer.add_tokens` and :obj:`False` with :meth:`~tokenizers.Tokenizer.add_special_tokens`):
+            Defines whether this token should be skipped when decoding.
+
+    """
+    def __init__(self, content, single_word=False, lstrip=False, rstrip=False, normalized=True, special=False):
+        pass
+
+    @property
+    def content(self):
+        """
+        Get the content of this :obj:`AddedToken`
+        """
+        pass
+
+    @property
+    def lstrip(self):
+        """
+        Get the value of the :obj:`lstrip` option
+        """
+        pass
+
+    @property
+    def normalized(self):
+        """
+        Get the value of the :obj:`normalized` option
+        """
+        pass
+
+    @property
+    def rstrip(self):
+        """
+        Get the value of the :obj:`rstrip` option
+        """
+        pass
+
+    @property
+    def single_word(self):
+        """
+        Get the value of the :obj:`single_word` option
+        """
+        pass
+
+    @property
+    def special(self):
+        """
+        Get the value of the :obj:`special` option
+        """
+        pass
+
+class Encoding:
+    """
+    The :class:`~tokenizers.Encoding` represents the output of a :class:`~tokenizers.Tokenizer`.
+    """
+    @property
+    def attention_mask(self):
+        """
+        The attention mask
+
+        This indicates to the LM which tokens should be attended to, and which should not.
+        This is especially important when batching sequences, where we need to applying
+        padding.
+
+        Returns:
+           :obj:`List[int]`: The attention mask
+        """
+        pass
+
+    def char_to_token(self, char_pos, sequence_index=0):
+        """
+        Get the token that contains the char at the given position in the input sequence.
+
+        Args:
+            char_pos (:obj:`int`):
+                The position of a char in the input string
+            sequence_index (:obj:`int`, defaults to :obj:`0`):
+                The index of the sequence that contains the target char
+
+        Returns:
+            :obj:`int`: The index of the token that contains this char in the encoded sequence
+        """
+        pass
+
+    def char_to_word(self, char_pos, sequence_index=0):
+        """
+        Get the word that contains the char at the given position in the input sequence.
+
+        Args:
+            char_pos (:obj:`int`):
+                The position of a char in the input string
+            sequence_index (:obj:`int`, defaults to :obj:`0`):
+                The index of the sequence that contains the target char
+
+        Returns:
+            :obj:`int`: The index of the word that contains this char in the input sequence
+        """
+        pass
+
+    @property
+    def ids(self):
+        """
+        The generated IDs
+
+        The IDs are the main input to a Language Model. They are the token indices,
+        the numerical representations that a LM understands.
+
+        Returns:
+            :obj:`List[int]`: The list of IDs
+        """
+        pass
+
+    @staticmethod
+    def merge(encodings, growing_offsets=True):
+        """
+        Merge the list of encodings into one final :class:`~tokenizers.Encoding`
+
+        Args:
+            encodings (A :obj:`List` of :class:`~tokenizers.Encoding`):
+                The list of encodings that should be merged in one
+
+            growing_offsets (:obj:`bool`, defaults to :obj:`True`):
+                Whether the offsets should accumulate while merging
+
+        Returns:
+            :class:`~tokenizers.Encoding`: The resulting Encoding
+        """
+        pass
+
+    @property
+    def n_sequences(self):
+        """
+        The number of sequences represented
+
+        Returns:
+            :obj:`int`: The number of sequences in this :class:`~tokenizers.Encoding`
+        """
+        pass
+
+    @property
+    def offsets(self):
+        """
+        The offsets associated to each token
+
+        These offsets let's you slice the input string, and thus retrieve the original
+        part that led to producing the corresponding token.
+
+        Returns:
+            A :obj:`List` of :obj:`Tuple[int, int]`: The list of offsets
+        """
+        pass
+
+    @property
+    def overflowing(self):
+        """
+        A :obj:`List` of overflowing :class:`~tokenizers.Encoding`
+
+        When using truncation, the :class:`~tokenizers.Tokenizer` takes care of splitting
+        the output into as many pieces as required to match the specified maximum length.
+        This field lets you retrieve all the subsequent pieces.
+
+        When you use pairs of sequences, the overflowing pieces will contain enough
+        variations to cover all the possible combinations, while respecting the provided
+        maximum length.
+        """
+        pass
+
+    def pad(self, length, direction="right", pad_id=0, pad_type_id=0, pad_token="[PAD]"):
+        """
+        Pad the :class:`~tokenizers.Encoding` at the given length
+
+        Args:
+            length (:obj:`int`):
+                The desired length
+
+            direction: (:obj:`str`, defaults to :obj:`right`):
+                The expected padding direction. Can be either :obj:`right` or :obj:`left`
+
+            pad_id (:obj:`int`, defaults to :obj:`0`):
+                The ID corresponding to the padding token
+
+            pad_type_id (:obj:`int`, defaults to :obj:`0`):
+                The type ID corresponding to the padding token
+
+            pad_token (:obj:`str`, defaults to `[PAD]`):
+                The pad token to use
+        """
+        pass
+
+    @property
+    def sequence_ids(self):
+        """
+        The generated sequence indices.
+
+        They represent the index of the input sequence associated to each token.
+        The sequence id can be None if the token is not related to any input sequence,
+        like for example with special tokens.
+
+        Returns:
+            A :obj:`List` of :obj:`Optional[int]`: A list of optional sequence index.
+        """
+        pass
+
+    def set_sequence_id(self, sequence_id):
+        """
+        Set the given sequence index
+
+        Set the given sequence index for the whole range of tokens contained in this
+        :class:`~tokenizers.Encoding`.
+        """
+        pass
+
+    @property
+    def special_tokens_mask(self):
+        """
+        The special token mask
+
+        This indicates which tokens are special tokens, and which are not.
+
+        Returns:
+            :obj:`List[int]`: The special tokens mask
+        """
+        pass
+
+    def token_to_chars(self, token_index):
+        """
+        Get the offsets of the token at the given index.
+
+        The returned offsets are related to the input sequence that contains the
+        token.  In order to determine in which input sequence it belongs, you
+        must call :meth:`~tokenizers.Encoding.token_to_sequence()`.
+
+        Args:
+            token_index (:obj:`int`):
+                The index of a token in the encoded sequence.
+
+        Returns:
+            :obj:`Tuple[int, int]`: The token offsets :obj:`(first, last + 1)`
+        """
+        pass
+
+    def token_to_sequence(self, token_index):
+        """
+        Get the index of the sequence represented by the given token.
+
+        In the general use case, this method returns :obj:`0` for a single sequence or
+        the first sequence of a pair, and :obj:`1` for the second sequence of a pair
+
+        Args:
+            token_index (:obj:`int`):
+                The index of a token in the encoded sequence.
+
+        Returns:
+            :obj:`int`: The sequence id of the given token
+        """
+        pass
+
+    def token_to_word(self, token_index):
+        """
+        Get the index of the word that contains the token in one of the input sequences.
+
+        The returned word index is related to the input sequence that contains
+        the token.  In order to determine in which input sequence it belongs, you
+        must call :meth:`~tokenizers.Encoding.token_to_sequence()`.
+
+        Args:
+            token_index (:obj:`int`):
+                The index of a token in the encoded sequence.
+
+        Returns:
+            :obj:`int`: The index of the word in the relevant input sequence.
+        """
+        pass
+
+    @property
+    def tokens(self):
+        """
+        The generated tokens
+
+        They are the string representation of the IDs.
+
+        Returns:
+            :obj:`List[str]`: The list of tokens
+        """
+        pass
+
+    def truncate(self, max_length, stride=0, direction="right"):
+        """
+        Truncate the :class:`~tokenizers.Encoding` at the given length
+
+        If this :class:`~tokenizers.Encoding` represents multiple sequences, when truncating
+        this information is lost. It will be considered as representing a single sequence.
+
+        Args:
+            max_length (:obj:`int`):
+                The desired length
+
+            stride (:obj:`int`, defaults to :obj:`0`):
+                The length of previous content to be included in each overflowing piece
+
+            direction (:obj:`str`, defaults to :obj:`right`):
+                Truncate direction
+        """
+        pass
+
+    @property
+    def type_ids(self):
+        """
+        The generated type IDs
+
+        Generally used for tasks like sequence classification or question answering,
+        these tokens let the LM know which input sequence corresponds to each tokens.
+
+        Returns:
+            :obj:`List[int]`: The list of type ids
+        """
+        pass
+
+    @property
+    def word_ids(self):
+        """
+        The generated word indices.
+
+        They represent the index of the word associated to each token.
+        When the input is pre-tokenized, they correspond to the ID of the given input label,
+        otherwise they correspond to the words indices as defined by the
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` that was used.
+
+        For special tokens and such (any token that was generated from something that was
+        not part of the input), the output is :obj:`None`
+
+        Returns:
+            A :obj:`List` of :obj:`Optional[int]`: A list of optional word index.
+        """
+        pass
+
+    def word_to_chars(self, word_index, sequence_index=0):
+        """
+        Get the offsets of the word at the given index in one of the input sequences.
+
+        Args:
+            word_index (:obj:`int`):
+                The index of a word in one of the input sequences.
+            sequence_index (:obj:`int`, defaults to :obj:`0`):
+                The index of the sequence that contains the target word
+
+        Returns:
+            :obj:`Tuple[int, int]`: The range of characters (span) :obj:`(first, last + 1)`
+        """
+        pass
+
+    def word_to_tokens(self, word_index, sequence_index=0):
+        """
+        Get the encoded tokens corresponding to the word at the given index
+        in one of the input sequences.
+
+        Args:
+            word_index (:obj:`int`):
+                The index of a word in one of the input sequences.
+            sequence_index (:obj:`int`, defaults to :obj:`0`):
+                The index of the sequence that contains the target word
+
+        Returns:
+            :obj:`Tuple[int, int]`: The range of tokens: :obj:`(first, last + 1)`
+        """
+        pass
+
+    @property
+    def words(self):
+        """
+        The generated word indices.
+
+        .. warning::
+            This is deprecated and will be removed in a future version.
+            Please use :obj:`~tokenizers.Encoding.word_ids` instead.
+
+        They represent the index of the word associated to each token.
+        When the input is pre-tokenized, they correspond to the ID of the given input label,
+        otherwise they correspond to the words indices as defined by the
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` that was used.
+
+        For special tokens and such (any token that was generated from something that was
+        not part of the input), the output is :obj:`None`
+
+        Returns:
+            A :obj:`List` of :obj:`Optional[int]`: A list of optional word index.
+        """
+        pass
+
+class NormalizedString:
+    """
+    NormalizedString
+
+    A NormalizedString takes care of modifying an "original" string, to obtain a "normalized" one.
+    While making all the requested modifications, it keeps track of the alignment information
+    between the two versions of the string.
+
+    Args:
+        sequence: str:
+            The string sequence used to initialize this NormalizedString
+    """
+    def append(self, s):
+        """
+        Append the given sequence to the string
+        """
+        pass
+
+    def clear(self):
+        """
+        Clears the string
+        """
+        pass
+
+    def filter(self, func):
+        """
+        Filter each character of the string using the given func
+        """
+        pass
+
+    def for_each(self, func):
+        """
+        Calls the given function for each character of the string
+        """
+        pass
+
+    def lowercase(self):
+        """
+        Lowercase the string
+        """
+        pass
+
+    def lstrip(self):
+        """
+        Strip the left of the string
+        """
+        pass
+
+    def map(self, func):
+        """
+        Calls the given function for each character of the string
+
+        Replaces each character of the string using the returned value. Each
+        returned value **must** be a str of length 1 (ie a character).
+        """
+        pass
+
+    def nfc(self):
+        """
+        Runs the NFC normalization
+        """
+        pass
+
+    def nfd(self):
+        """
+        Runs the NFD normalization
+        """
+        pass
+
+    def nfkc(self):
+        """
+        Runs the NFKC normalization
+        """
+        pass
+
+    def nfkd(self):
+        """
+        Runs the NFKD normalization
+        """
+        pass
+
+    @property
+    def normalized(self):
+        """
+        The normalized part of the string
+        """
+        pass
+
+    def prepend(self, s):
+        """
+        Prepend the given sequence to the string
+        """
+        pass
+
+    def replace(self, pattern, content):
+        """
+        Replace the content of the given pattern with the provided content
+
+        Args:
+            pattern: Pattern:
+                A pattern used to match the string. Usually a string or a Regex
+
+            content: str:
+                The content to be used as replacement
+        """
+        pass
+
+    def rstrip(self):
+        """
+        Strip the right of the string
+        """
+        pass
+
+    def slice(self, range):
+        """
+        Slice the string using the given range
+        """
+        pass
+
+    def split(self, pattern, behavior):
+        """
+        Split the NormalizedString using the given pattern and the specified behavior
+
+        Args:
+            pattern: Pattern:
+                A pattern used to split the string. Usually a string or a regex built with `tokenizers.Regex`
+
+            behavior: SplitDelimiterBehavior:
+                The behavior to use when splitting.
+                Choices: "removed", "isolated", "merged_with_previous", "merged_with_next",
+                "contiguous"
+
+        Returns:
+            A list of NormalizedString, representing each split
+        """
+        pass
+
+    def strip(self):
+        """
+        Strip both ends of the string
+        """
+        pass
+
+    def uppercase(self):
+        """
+        Uppercase the string
+        """
+        pass
+
+class PreTokenizedString:
+    """
+    PreTokenizedString
+
+    Wrapper over a string, that provides a way to normalize, pre-tokenize, tokenize the
+    underlying string, while keeping track of the alignment information (offsets).
+
+    The PreTokenizedString manages what we call `splits`. Each split represents a substring
+    which is a subpart of the original string, with the relevant offsets and tokens.
+
+    When calling one of the methods used to modify the PreTokenizedString (namely one of
+    `split`, `normalize` or `tokenize), only the `splits` that don't have any associated
+    tokens will get modified.
+
+    Args:
+        sequence: str:
+            The string sequence used to initialize this PreTokenizedString
+    """
+    def __init__(self, sequence):
+        pass
+
+    def get_splits(self, offset_referential="original", offset_type="char"):
+        """
+        Get the splits currently managed by the PreTokenizedString
+
+        Args:
+            offset_referential: :obj:`str`
+                Whether the returned splits should have offsets expressed relative
+                to the original string, or the normalized one. choices: "original", "normalized".
+
+            offset_type: :obj:`str`
+                Whether the returned splits should have offsets expressed in bytes or chars.
+                When slicing an str, we usually want to use chars, which is the default value.
+                Now in some cases it might be interesting to get these offsets expressed in bytes,
+                so it is possible to change this here.
+                choices: "char", "bytes"
+
+        Returns
+            A list of splits
+        """
+        pass
+
+    def normalize(self, func):
+        """
+        Normalize each split of the `PreTokenizedString` using the given `func`
+
+        Args:
+            func: Callable[[NormalizedString], None]:
+                The function used to normalize each underlying split. This function
+                does not need to return anything, just calling the methods on the provided
+                NormalizedString allow its modification.
+        """
+        pass
+
+    def split(self, func):
+        """
+        Split the PreTokenizedString using the given `func`
+
+        Args:
+            func: Callable[[index, NormalizedString], List[NormalizedString]]:
+                The function used to split each underlying split.
+                It is expected to return a list of `NormalizedString`, that represent the new
+                splits. If the given `NormalizedString` does not need any splitting, we can
+                just return it directly.
+                In order for the offsets to be tracked accurately, any returned `NormalizedString`
+                should come from calling either `.split` or `.slice` on the received one.
+        """
+        pass
+
+    def to_encoding(self, type_id=0, word_idx=None):
+        """
+        Return an Encoding generated from this PreTokenizedString
+
+        Args:
+            type_id: int = 0:
+                The type_id to be used on the generated Encoding.
+
+            word_idx: Optional[int] = None:
+                An optional word index to be used for each token of this Encoding. If provided,
+                all the word indices in the generated Encoding will use this value, instead
+                of the one automatically tracked during pre-tokenization.
+
+        Returns:
+            An Encoding
+        """
+        pass
+
+    def tokenize(self, func):
+        """
+        Tokenize each split of the `PreTokenizedString` using the given `func`
+
+        Args:
+            func: Callable[[str], List[Token]]:
+                The function used to tokenize each underlying split. This function must return
+                a list of Token generated from the input str.
+        """
+        pass
+
+class Regex:
+    """
+    Instantiate a new Regex with the given pattern
+    """
+    def __init__(self, pattern):
+        pass
+
+class Token:
+    pass
+
+class Tokenizer:
+    """
+    A :obj:`Tokenizer` works as a pipeline. It processes some raw text as input
+    and outputs an :class:`~tokenizers.Encoding`.
+
+    Args:
+        model (:class:`~tokenizers.models.Model`):
+            The core algorithm that this :obj:`Tokenizer` should be using.
+
+    """
+    def __init__(self, model):
+        pass
+
+    def add_special_tokens(self, tokens):
+        """
+        Add the given special tokens to the Tokenizer.
+
+        If these tokens are already part of the vocabulary, it just let the Tokenizer know about
+        them. If they don't exist, the Tokenizer creates them, giving them a new id.
+
+        These special tokens will never be processed by the model (ie won't be split into
+        multiple tokens), and they can be removed from the output when decoding.
+
+        Args:
+            tokens (A :obj:`List` of :class:`~tokenizers.AddedToken` or :obj:`str`):
+                The list of special tokens we want to add to the vocabulary. Each token can either
+                be a string or an instance of :class:`~tokenizers.AddedToken` for more
+                customization.
+
+        Returns:
+            :obj:`int`: The number of tokens that were created in the vocabulary
+        """
+        pass
+
+    def add_tokens(self, tokens):
+        """
+        Add the given tokens to the vocabulary
+
+        The given tokens are added only if they don't already exist in the vocabulary.
+        Each token then gets a new attributed id.
+
+        Args:
+            tokens (A :obj:`List` of :class:`~tokenizers.AddedToken` or :obj:`str`):
+                The list of tokens we want to add to the vocabulary. Each token can be either a
+                string or an instance of :class:`~tokenizers.AddedToken` for more customization.
+
+        Returns:
+            :obj:`int`: The number of tokens that were created in the vocabulary
+        """
+        pass
+
+    def decode(self, ids, skip_special_tokens=True):
+        """
+        Decode the given list of ids back to a string
+
+        This is used to decode anything coming back from a Language Model
+
+        Args:
+            ids (A :obj:`List/Tuple` of :obj:`int`):
+                The list of ids that we want to decode
+
+            skip_special_tokens (:obj:`bool`, defaults to :obj:`True`):
+                Whether the special tokens should be removed from the decoded string
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+    def decode_batch(self, sequences, skip_special_tokens=True):
+        """
+        Decode a batch of ids back to their corresponding string
+
+        Args:
+            sequences (:obj:`List` of :obj:`List[int]`):
+                The batch of sequences we want to decode
+
+            skip_special_tokens (:obj:`bool`, defaults to :obj:`True`):
+                Whether the special tokens should be removed from the decoded strings
+
+        Returns:
+            :obj:`List[str]`: A list of decoded strings
+        """
+        pass
+
+    @property
+    def decoder(self):
+        """
+        The `optional` :class:`~tokenizers.decoders.Decoder` in use by the Tokenizer
+        """
+        pass
+
+    def enable_padding(
+        self, direction="right", pad_id=0, pad_type_id=0, pad_token="[PAD]", length=None, pad_to_multiple_of=None
+    ):
+        """
+        Enable the padding
+
+        Args:
+            direction (:obj:`str`, `optional`, defaults to :obj:`right`):
+                The direction in which to pad. Can be either ``right`` or ``left``
+
+            pad_to_multiple_of (:obj:`int`, `optional`):
+                If specified, the padding length should always snap to the next multiple of the
+                given value. For example if we were going to pad witha length of 250 but
+                ``pad_to_multiple_of=8`` then we will pad to 256.
+
+            pad_id (:obj:`int`, defaults to 0):
+                The id to be used when padding
+
+            pad_type_id (:obj:`int`, defaults to 0):
+                The type id to be used when padding
+
+            pad_token (:obj:`str`, defaults to :obj:`[PAD]`):
+                The pad token to be used when padding
+
+            length (:obj:`int`, `optional`):
+                If specified, the length at which to pad. If not specified we pad using the size of
+                the longest sequence in a batch.
+        """
+        pass
+
+    def enable_truncation(self, max_length, stride=0, strategy="longest_first", direction="right"):
+        """
+        Enable truncation
+
+        Args:
+            max_length (:obj:`int`):
+                The max length at which to truncate
+
+            stride (:obj:`int`, `optional`):
+                The length of the previous first sequence to be included in the overflowing
+                sequence
+
+            strategy (:obj:`str`, `optional`, defaults to :obj:`longest_first`):
+                The strategy used to truncation. Can be one of ``longest_first``, ``only_first`` or
+                ``only_second``.
+
+            direction (:obj:`str`, defaults to :obj:`right`):
+                Truncate direction
+        """
+        pass
+
+    def encode(self, sequence, pair=None, is_pretokenized=False, add_special_tokens=True):
+        """
+        Encode the given sequence and pair. This method can process raw text sequences
+        as well as already pre-tokenized sequences.
+
+        Example:
+            Here are some examples of the inputs that are accepted::
+
+                encode("A single sequence")`
+                encode("A sequence", "And its pair")`
+                encode([ "A", "pre", "tokenized", "sequence" ], is_pretokenized=True)`
+                encode(
+                    [ "A", "pre", "tokenized", "sequence" ], [ "And", "its", "pair" ],
+                    is_pretokenized=True
+                )
+
+        Args:
+            sequence (:obj:`~tokenizers.InputSequence`):
+                The main input sequence we want to encode. This sequence can be either raw
+                text or pre-tokenized, according to the ``is_pretokenized`` argument:
+
+                - If ``is_pretokenized=False``: :class:`~tokenizers.TextInputSequence`
+                - If ``is_pretokenized=True``: :class:`~tokenizers.PreTokenizedInputSequence`
+
+            pair (:obj:`~tokenizers.InputSequence`, `optional`):
+                An optional input sequence. The expected format is the same that for ``sequence``.
+
+            is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
+                Whether the input is already pre-tokenized
+
+            add_special_tokens (:obj:`bool`, defaults to :obj:`True`):
+                Whether to add the special tokens
+
+        Returns:
+            :class:`~tokenizers.Encoding`: The encoded result
+
+        """
+        pass
+
+    def encode_batch(self, input, is_pretokenized=False, add_special_tokens=True):
+        """
+        Encode the given batch of inputs. This method accept both raw text sequences
+        as well as already pre-tokenized sequences.
+
+        Example:
+            Here are some examples of the inputs that are accepted::
+
+                encode_batch([
+                    "A single sequence",
+                    ("A tuple with a sequence", "And its pair"),
+                    [ "A", "pre", "tokenized", "sequence" ],
+                    ([ "A", "pre", "tokenized", "sequence" ], "And its pair")
+                ])
+
+        Args:
+            input (A :obj:`List`/:obj:`Tuple` of :obj:`~tokenizers.EncodeInput`):
+                A list of single sequences or pair sequences to encode. Each sequence
+                can be either raw text or pre-tokenized, according to the ``is_pretokenized``
+                argument:
+
+                - If ``is_pretokenized=False``: :class:`~tokenizers.TextEncodeInput`
+                - If ``is_pretokenized=True``: :class:`~tokenizers.PreTokenizedEncodeInput`
+
+            is_pretokenized (:obj:`bool`, defaults to :obj:`False`):
+                Whether the input is already pre-tokenized
+
+            add_special_tokens (:obj:`bool`, defaults to :obj:`True`):
+                Whether to add the special tokens
+
+        Returns:
+            A :obj:`List` of :class:`~tokenizers.Encoding`: The encoded batch
+
+        """
+        pass
+
+    @property
+    def encode_special_tokens(self):
+        """
+        Modifies the tokenizer in order to use or not the special tokens
+        during encoding.
+
+        Args:
+            value (:obj:`bool`):
+                Whether to use the special tokens or not
+
+        """
+        pass
+
+    @staticmethod
+    def from_buffer(buffer):
+        """
+        Instantiate a new :class:`~tokenizers.Tokenizer` from the given buffer.
+
+        Args:
+            buffer (:obj:`bytes`):
+                A buffer containing a previously serialized :class:`~tokenizers.Tokenizer`
+
+        Returns:
+            :class:`~tokenizers.Tokenizer`: The new tokenizer
+        """
+        pass
+
+    @staticmethod
+    def from_file(path):
+        """
+        Instantiate a new :class:`~tokenizers.Tokenizer` from the file at the given path.
+
+        Args:
+            path (:obj:`str`):
+                A path to a local JSON file representing a previously serialized
+                :class:`~tokenizers.Tokenizer`
+
+        Returns:
+            :class:`~tokenizers.Tokenizer`: The new tokenizer
+        """
+        pass
+
+    @staticmethod
+    def from_pretrained(identifier, revision="main", auth_token=None):
+        """
+        Instantiate a new :class:`~tokenizers.Tokenizer` from an existing file on the
+        Hugging Face Hub.
+
+        Args:
+            identifier (:obj:`str`):
+                The identifier of a Model on the Hugging Face Hub, that contains
+                a tokenizer.json file
+            revision (:obj:`str`, defaults to `main`):
+                A branch or commit id
+            auth_token (:obj:`str`, `optional`, defaults to `None`):
+                An optional auth token used to access private repositories on the
+                Hugging Face Hub
+
+        Returns:
+            :class:`~tokenizers.Tokenizer`: The new tokenizer
+        """
+        pass
+
+    @staticmethod
+    def from_str(json):
+        """
+        Instantiate a new :class:`~tokenizers.Tokenizer` from the given JSON string.
+
+        Args:
+            json (:obj:`str`):
+                A valid JSON string representing a previously serialized
+                :class:`~tokenizers.Tokenizer`
+
+        Returns:
+            :class:`~tokenizers.Tokenizer`: The new tokenizer
+        """
+        pass
+
+    def get_added_tokens_decoder(self):
+        """
+        Get the underlying vocabulary
+
+        Returns:
+            :obj:`Dict[int, AddedToken]`: The vocabulary
+        """
+        pass
+
+    def get_vocab(self, with_added_tokens=True):
+        """
+        Get the underlying vocabulary
+
+        Args:
+            with_added_tokens (:obj:`bool`, defaults to :obj:`True`):
+                Whether to include the added tokens
+
+        Returns:
+            :obj:`Dict[str, int]`: The vocabulary
+        """
+        pass
+
+    def get_vocab_size(self, with_added_tokens=True):
+        """
+        Get the size of the underlying vocabulary
+
+        Args:
+            with_added_tokens (:obj:`bool`, defaults to :obj:`True`):
+                Whether to include the added tokens
+
+        Returns:
+            :obj:`int`: The size of the vocabulary
+        """
+        pass
+
+    def id_to_token(self, id):
+        """
+        Convert the given id to its corresponding token if it exists
+
+        Args:
+            id (:obj:`int`):
+                The id to convert
+
+        Returns:
+            :obj:`Optional[str]`: An optional token, :obj:`None` if out of vocabulary
+        """
+        pass
+
+    @property
+    def model(self):
+        """
+        The :class:`~tokenizers.models.Model` in use by the Tokenizer
+        """
+        pass
+
+    def no_padding(self):
+        """
+        Disable padding
+        """
+        pass
+
+    def no_truncation(self):
+        """
+        Disable truncation
+        """
+        pass
+
+    @property
+    def normalizer(self):
+        """
+        The `optional` :class:`~tokenizers.normalizers.Normalizer` in use by the Tokenizer
+        """
+        pass
+
+    def num_special_tokens_to_add(self, is_pair):
+        """
+        Return the number of special tokens that would be added for single/pair sentences.
+        :param is_pair: Boolean indicating if the input would be a single sentence or a pair
+        :return:
+        """
+        pass
+
+    @property
+    def padding(self):
+        """
+        Get the current padding parameters
+
+        `Cannot be set, use` :meth:`~tokenizers.Tokenizer.enable_padding` `instead`
+
+        Returns:
+            (:obj:`dict`, `optional`):
+                A dict with the current padding parameters if padding is enabled
+        """
+        pass
+
+    def post_process(self, encoding, pair=None, add_special_tokens=True):
+        """
+        Apply all the post-processing steps to the given encodings.
+
+        The various steps are:
+
+            1. Truncate according to the set truncation params (provided with
+               :meth:`~tokenizers.Tokenizer.enable_truncation`)
+            2. Apply the :class:`~tokenizers.processors.PostProcessor`
+            3. Pad according to the set padding params (provided with
+               :meth:`~tokenizers.Tokenizer.enable_padding`)
+
+        Args:
+            encoding (:class:`~tokenizers.Encoding`):
+                The :class:`~tokenizers.Encoding` corresponding to the main sequence.
+
+            pair (:class:`~tokenizers.Encoding`, `optional`):
+                An optional :class:`~tokenizers.Encoding` corresponding to the pair sequence.
+
+            add_special_tokens (:obj:`bool`):
+                Whether to add the special tokens
+
+        Returns:
+            :class:`~tokenizers.Encoding`: The final post-processed encoding
+        """
+        pass
+
+    @property
+    def post_processor(self):
+        """
+        The `optional` :class:`~tokenizers.processors.PostProcessor` in use by the Tokenizer
+        """
+        pass
+
+    @property
+    def pre_tokenizer(self):
+        """
+        The `optional` :class:`~tokenizers.pre_tokenizers.PreTokenizer` in use by the Tokenizer
+        """
+        pass
+
+    def save(self, path, pretty=True):
+        """
+        Save the :class:`~tokenizers.Tokenizer` to the file at the given path.
+
+        Args:
+            path (:obj:`str`):
+                A path to a file in which to save the serialized tokenizer.
+
+            pretty (:obj:`bool`, defaults to :obj:`True`):
+                Whether the JSON file should be pretty formatted.
+        """
+        pass
+
+    def to_str(self, pretty=False):
+        """
+        Gets a serialized string representing this :class:`~tokenizers.Tokenizer`.
+
+        Args:
+            pretty (:obj:`bool`, defaults to :obj:`False`):
+                Whether the JSON string should be pretty formatted.
+
+        Returns:
+            :obj:`str`: A string representing the serialized Tokenizer
+        """
+        pass
+
+    def token_to_id(self, token):
+        """
+        Convert the given token to its corresponding id if it exists
+
+        Args:
+            token (:obj:`str`):
+                The token to convert
+
+        Returns:
+            :obj:`Optional[int]`: An optional id, :obj:`None` if out of vocabulary
+        """
+        pass
+
+    def train(self, files, trainer=None):
+        """
+        Train the Tokenizer using the given files.
+
+        Reads the files line by line, while keeping all the whitespace, even new lines.
+        If you want to train from data store in-memory, you can check
+        :meth:`~tokenizers.Tokenizer.train_from_iterator`
+
+        Args:
+            files (:obj:`List[str]`):
+                A list of path to the files that we should use for training
+
+            trainer (:obj:`~tokenizers.trainers.Trainer`, `optional`):
+                An optional trainer that should be used to train our Model
+        """
+        pass
+
+    def train_from_iterator(self, iterator, trainer=None, length=None):
+        """
+        Train the Tokenizer using the provided iterator.
+
+        You can provide anything that is a Python Iterator
+
+            * A list of sequences :obj:`List[str]`
+            * A generator that yields :obj:`str` or :obj:`List[str]`
+            * A Numpy array of strings
+            * ...
+
+        Args:
+            iterator (:obj:`Iterator`):
+                Any iterator over strings or list of strings
+
+            trainer (:obj:`~tokenizers.trainers.Trainer`, `optional`):
+                An optional trainer that should be used to train our Model
+
+            length (:obj:`int`, `optional`):
+                The total number of sequences in the iterator. This is used to
+                provide meaningful progress tracking
+        """
+        pass
+
+    @property
+    def truncation(self):
+        """
+        Get the currently set truncation parameters
+
+        `Cannot set, use` :meth:`~tokenizers.Tokenizer.enable_truncation` `instead`
+
+        Returns:
+            (:obj:`dict`, `optional`):
+                A dict with the current truncation parameters if truncation is enabled
+        """
+        pass

wemm/lib/python3.10/site-packages/tokenizers/decoders/__init__.pyi

@@ -0,0 +1,271 @@
+# Generated content DO NOT EDIT
+class Decoder:
+    """
+    Base class for all decoders
+
+    This class is not supposed to be instantiated directly. Instead, any implementation of
+    a Decoder will return an instance of this class when instantiated.
+    """
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class BPEDecoder(Decoder):
+    """
+    BPEDecoder Decoder
+
+    Args:
+        suffix (:obj:`str`, `optional`, defaults to :obj:`</w>`):
+            The suffix that was used to caracterize an end-of-word. This suffix will
+            be replaced by whitespaces during the decoding
+    """
+    def __init__(self, suffix="</w>"):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class ByteFallback(Decoder):
+    """
+    ByteFallback Decoder
+    ByteFallback is a simple trick which converts tokens looking like `<0x61>`
+    to pure bytes, and attempts to make them into a string. If the tokens
+    cannot be decoded you will get � instead for each inconvertable byte token
+
+    """
+    def __init__(self):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class ByteLevel(Decoder):
+    """
+    ByteLevel Decoder
+
+    This decoder is to be used in tandem with the :class:`~tokenizers.pre_tokenizers.ByteLevel`
+    :class:`~tokenizers.pre_tokenizers.PreTokenizer`.
+    """
+    def __init__(self):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class CTC(Decoder):
+    """
+    CTC Decoder
+
+    Args:
+        pad_token (:obj:`str`, `optional`, defaults to :obj:`<pad>`):
+            The pad token used by CTC to delimit a new token.
+        word_delimiter_token (:obj:`str`, `optional`, defaults to :obj:`|`):
+            The word delimiter token. It will be replaced by a <space>
+        cleanup (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to cleanup some tokenization artifacts.
+            Mainly spaces before punctuation, and some abbreviated english forms.
+    """
+    def __init__(self, pad_token="<pad>", word_delimiter_token="|", cleanup=True):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class Fuse(Decoder):
+    """
+    Fuse Decoder
+    Fuse simply fuses every token into a single string.
+    This is the last step of decoding, this decoder exists only if
+    there is need to add other decoders *after* the fusion
+    """
+    def __init__(self):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class Metaspace(Decoder):
+    """
+    Metaspace Decoder
+
+    Args:
+        replacement (:obj:`str`, `optional`, defaults to :obj:`▁`):
+            The replacement character. Must be exactly one character. By default we
+            use the `▁` (U+2581) meta symbol (Same as in SentencePiece).
+
+        prepend_scheme (:obj:`str`, `optional`, defaults to :obj:`"always"`):
+            Whether to add a space to the first word if there isn't already one. This
+            lets us treat `hello` exactly like `say hello`.
+            Choices: "always", "never", "first". First means the space is only added on the first
+            token (relevant when special tokens are used or other pre_tokenizer are used).
+    """
+    def __init__(self, replacement="▁", prepend_scheme="always", split=True):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class Replace(Decoder):
+    """
+    Replace Decoder
+
+    This decoder is to be used in tandem with the :class:`~tokenizers.pre_tokenizers.Replace`
+    :class:`~tokenizers.pre_tokenizers.PreTokenizer`.
+    """
+    def __init__(self, pattern, content):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class Sequence(Decoder):
+    """
+    Sequence Decoder
+
+    Args:
+        decoders (:obj:`List[Decoder]`)
+            The decoders that need to be chained
+    """
+    def __init__(self, decoders):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class Strip(Decoder):
+    """
+    Strip normalizer
+    Strips n left characters of each token, or n right characters of each token
+    """
+    def __init__(self, content, left=0, right=0):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass
+
+class WordPiece(Decoder):
+    """
+    WordPiece Decoder
+
+    Args:
+        prefix (:obj:`str`, `optional`, defaults to :obj:`##`):
+            The prefix to use for subwords that are not a beginning-of-word
+
+        cleanup (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to cleanup some tokenization artifacts. Mainly spaces before punctuation,
+            and some abbreviated english forms.
+    """
+    def __init__(self, prefix="##", cleanup=True):
+        pass
+
+    def decode(self, tokens):
+        """
+        Decode the given list of tokens to a final string
+
+        Args:
+            tokens (:obj:`List[str]`):
+                The list of tokens to decode
+
+        Returns:
+            :obj:`str`: The decoded string
+        """
+        pass

wemm/lib/python3.10/site-packages/tokenizers/implementations/byte_level_bpe.py

@@ -0,0 +1,122 @@
+from typing import Dict, Iterator, List, Optional, Tuple, Union
+
+from tokenizers import AddedToken, Tokenizer, decoders, pre_tokenizers, processors, trainers
+from tokenizers.models import BPE
+from tokenizers.normalizers import Lowercase, Sequence, unicode_normalizer_from_str
+
+from .base_tokenizer import BaseTokenizer
+
+
+class ByteLevelBPETokenizer(BaseTokenizer):
+    """ByteLevelBPETokenizer
+
+    Represents a Byte-level BPE as introduced by OpenAI with their GPT-2 model
+    """
+
+    def __init__(
+        self,
+        vocab: Optional[Union[str, Dict[str, int]]] = None,
+        merges: Optional[Union[str, Dict[Tuple[int, int], Tuple[int, int]]]] = None,
+        add_prefix_space: bool = False,
+        lowercase: bool = False,
+        dropout: Optional[float] = None,
+        unicode_normalizer: Optional[str] = None,
+        continuing_subword_prefix: Optional[str] = None,
+        end_of_word_suffix: Optional[str] = None,
+        trim_offsets: bool = False,
+    ):
+        if vocab is not None and merges is not None:
+            tokenizer = Tokenizer(
+                BPE(
+                    vocab,
+                    merges,
+                    dropout=dropout,
+                    continuing_subword_prefix=continuing_subword_prefix or "",
+                    end_of_word_suffix=end_of_word_suffix or "",
+                )
+            )
+        else:
+            tokenizer = Tokenizer(BPE())
+
+        # Check for Unicode normalization first (before everything else)
+        normalizers = []
+
+        if unicode_normalizer:
+            normalizers += [unicode_normalizer_from_str(unicode_normalizer)]
+
+        if lowercase:
+            normalizers += [Lowercase()]
+
+        # Create the normalizer structure
+        if len(normalizers) > 0:
+            if len(normalizers) > 1:
+                tokenizer.normalizer = Sequence(normalizers)
+            else:
+                tokenizer.normalizer = normalizers[0]
+
+        tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space)
+        tokenizer.decoder = decoders.ByteLevel()
+        tokenizer.post_processor = processors.ByteLevel(trim_offsets=trim_offsets)
+
+        parameters = {
+            "model": "ByteLevelBPE",
+            "add_prefix_space": add_prefix_space,
+            "lowercase": lowercase,
+            "dropout": dropout,
+            "unicode_normalizer": unicode_normalizer,
+            "continuing_subword_prefix": continuing_subword_prefix,
+            "end_of_word_suffix": end_of_word_suffix,
+            "trim_offsets": trim_offsets,
+        }
+
+        super().__init__(tokenizer, parameters)
+
+    @staticmethod
+    def from_file(vocab_filename: str, merges_filename: str, **kwargs):
+        vocab, merges = BPE.read_file(vocab_filename, merges_filename)
+        return ByteLevelBPETokenizer(vocab, merges, **kwargs)
+
+    def train(
+        self,
+        files: Union[str, List[str]],
+        vocab_size: int = 30000,
+        min_frequency: int = 2,
+        show_progress: bool = True,
+        special_tokens: List[Union[str, AddedToken]] = [],
+    ):
+        """Train the model using the given files"""
+
+        trainer = trainers.BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=min_frequency,
+            show_progress=show_progress,
+            special_tokens=special_tokens,
+            initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
+        )
+        if isinstance(files, str):
+            files = [files]
+        self._tokenizer.train(files, trainer=trainer)
+
+    def train_from_iterator(
+        self,
+        iterator: Union[Iterator[str], Iterator[Iterator[str]]],
+        vocab_size: int = 30000,
+        min_frequency: int = 2,
+        show_progress: bool = True,
+        special_tokens: List[Union[str, AddedToken]] = [],
+        length: Optional[int] = None,
+    ):
+        """Train the model using the given iterator"""
+
+        trainer = trainers.BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=min_frequency,
+            show_progress=show_progress,
+            special_tokens=special_tokens,
+            initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
+        )
+        self._tokenizer.train_from_iterator(
+            iterator,
+            trainer=trainer,
+            length=length,
+        )

wemm/lib/python3.10/site-packages/tokenizers/implementations/char_level_bpe.py

@@ -0,0 +1,150 @@
+from typing import Dict, Iterator, List, Optional, Tuple, Union
+
+from .. import AddedToken, Tokenizer, decoders, pre_tokenizers, trainers
+from ..models import BPE
+from ..normalizers import BertNormalizer, Lowercase, Sequence, unicode_normalizer_from_str
+from .base_tokenizer import BaseTokenizer
+
+
+class CharBPETokenizer(BaseTokenizer):
+    """Original BPE Tokenizer
+
+    Represents the BPE algorithm, as introduced by Rico Sennrich
+    (https://arxiv.org/abs/1508.07909)
+
+    The defaults settings corresponds to OpenAI GPT BPE tokenizers and differs from the original
+    Sennrich subword-nmt implementation by the following options that you can deactivate:
+        - adding a normalizer to clean up the text (deactivate with `bert_normalizer=False`) by:
+            * removing any control characters and replacing all whitespaces by the classic one.
+            * handle chinese chars by putting spaces around them.
+            * strip all accents.
+        - spitting on punctuation in addition to whitespaces (deactivate it with
+          `split_on_whitespace_only=True`)
+    """
+
+    def __init__(
+        self,
+        vocab: Optional[Union[str, Dict[str, int]]] = None,
+        merges: Optional[Union[str, Dict[Tuple[int, int], Tuple[int, int]]]] = None,
+        unk_token: Union[str, AddedToken] = "<unk>",
+        suffix: str = "</w>",
+        dropout: Optional[float] = None,
+        lowercase: bool = False,
+        unicode_normalizer: Optional[str] = None,
+        bert_normalizer: bool = True,
+        split_on_whitespace_only: bool = False,
+    ):
+        if vocab is not None and merges is not None:
+            tokenizer = Tokenizer(
+                BPE(
+                    vocab,
+                    merges,
+                    dropout=dropout,
+                    unk_token=str(unk_token),
+                    end_of_word_suffix=suffix,
+                )
+            )
+        else:
+            tokenizer = Tokenizer(BPE(unk_token=str(unk_token), dropout=dropout, end_of_word_suffix=suffix))
+
+        if tokenizer.token_to_id(str(unk_token)) is not None:
+            tokenizer.add_special_tokens([str(unk_token)])
+
+        # Check for Unicode normalization first (before everything else)
+        normalizers = []
+
+        if unicode_normalizer:
+            normalizers += [unicode_normalizer_from_str(unicode_normalizer)]
+
+        if bert_normalizer:
+            normalizers += [BertNormalizer(lowercase=False)]
+
+        if lowercase:
+            normalizers += [Lowercase()]
+
+        # Create the normalizer structure
+        if len(normalizers) > 0:
+            if len(normalizers) > 1:
+                tokenizer.normalizer = Sequence(normalizers)
+            else:
+                tokenizer.normalizer = normalizers[0]
+
+        if split_on_whitespace_only:
+            tokenizer.pre_tokenizer = pre_tokenizers.WhitespaceSplit()
+        else:
+            tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
+
+        tokenizer.decoder = decoders.BPEDecoder(suffix=suffix)
+
+        parameters = {
+            "model": "BPE",
+            "unk_token": unk_token,
+            "suffix": suffix,
+            "dropout": dropout,
+            "lowercase": lowercase,
+            "unicode_normalizer": unicode_normalizer,
+            "bert_normalizer": bert_normalizer,
+            "split_on_whitespace_only": split_on_whitespace_only,
+        }
+
+        super().__init__(tokenizer, parameters)
+
+    @staticmethod
+    def from_file(vocab_filename: str, merges_filename: str, **kwargs):
+        vocab, merges = BPE.read_file(vocab_filename, merges_filename)
+        return CharBPETokenizer(vocab, merges, **kwargs)
+
+    def train(
+        self,
+        files: Union[str, List[str]],
+        vocab_size: int = 30000,
+        min_frequency: int = 2,
+        special_tokens: List[Union[str, AddedToken]] = ["<unk>"],
+        limit_alphabet: int = 1000,
+        initial_alphabet: List[str] = [],
+        suffix: Optional[str] = "</w>",
+        show_progress: bool = True,
+    ):
+        """Train the model using the given files"""
+
+        trainer = trainers.BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=min_frequency,
+            special_tokens=special_tokens,
+            limit_alphabet=limit_alphabet,
+            initial_alphabet=initial_alphabet,
+            end_of_word_suffix=suffix,
+            show_progress=show_progress,
+        )
+        if isinstance(files, str):
+            files = [files]
+        self._tokenizer.train(files, trainer=trainer)
+
+    def train_from_iterator(
+        self,
+        iterator: Union[Iterator[str], Iterator[Iterator[str]]],
+        vocab_size: int = 30000,
+        min_frequency: int = 2,
+        special_tokens: List[Union[str, AddedToken]] = ["<unk>"],
+        limit_alphabet: int = 1000,
+        initial_alphabet: List[str] = [],
+        suffix: Optional[str] = "</w>",
+        show_progress: bool = True,
+        length: Optional[int] = None,
+    ):
+        """Train the model using the given iterator"""
+
+        trainer = trainers.BpeTrainer(
+            vocab_size=vocab_size,
+            min_frequency=min_frequency,
+            special_tokens=special_tokens,
+            limit_alphabet=limit_alphabet,
+            initial_alphabet=initial_alphabet,
+            end_of_word_suffix=suffix,
+            show_progress=show_progress,
+        )
+        self._tokenizer.train_from_iterator(
+            iterator,
+            trainer=trainer,
+            length=length,
+        )

wemm/lib/python3.10/site-packages/tokenizers/implementations/sentencepiece_unigram.py

@@ -0,0 +1,196 @@
+import json
+import os
+from typing import Iterator, List, Optional, Union, Tuple
+
+from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
+from tokenizers.models import Unigram
+
+from .base_tokenizer import BaseTokenizer
+
+
+class SentencePieceUnigramTokenizer(BaseTokenizer):
+    """SentencePiece Unigram Tokenizer
+
+    Represents the Unigram algorithm, with the pretokenization used by SentencePiece
+    """
+
+    def __init__(
+        self,
+        vocab: Optional[List[Tuple[str, float]]] = None,
+        replacement: str = "▁",
+        add_prefix_space: bool = True,
+    ):
+        if vocab is not None:
+            # Let Unigram(..) fail if only one of them is None
+            tokenizer = Tokenizer(Unigram(vocab))
+        else:
+            tokenizer = Tokenizer(Unigram())
+
+        tokenizer.normalizer = normalizers.Sequence(
+            [normalizers.Nmt(), normalizers.NFKC(), normalizers.Replace(Regex(" {2,}"), " ")]
+        )
+        prepend_scheme = "always" if add_prefix_space else "never"
+        tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement=replacement, prepend_scheme=prepend_scheme)
+        tokenizer.decoder = decoders.Metaspace(replacement=replacement, prepend_scheme=prepend_scheme)
+
+        parameters = {
+            "model": "SentencePieceUnigram",
+            "replacement": replacement,
+            "add_prefix_space": add_prefix_space,
+        }
+
+        super().__init__(tokenizer, parameters)
+
+    def train(
+        self,
+        files: Union[str, List[str]],
+        vocab_size: int = 8000,
+        show_progress: bool = True,
+        special_tokens: Optional[List[Union[str, AddedToken]]] = None,
+        initial_alphabet: Optional[List[str]] = None,
+        unk_token: Optional[str] = None,
+    ):
+        """
+        Train the model using the given files
+
+        Args:
+            files (:obj:`List[str]`):
+                A list of path to the files that we should use for training
+            vocab_size (:obj:`int`):
+                The size of the final vocabulary, including all tokens and alphabet.
+            show_progress (:obj:`bool`):
+                Whether to show progress bars while training.
+            special_tokens (:obj:`List[Union[str, AddedToken]]`, `optional`):
+                A list of special tokens the model should know of.
+            initial_alphabet (:obj:`List[str]`, `optional`):
+                A list of characters to include in the initial alphabet, even
+                if not seen in the training dataset.
+                If the strings contain more than one character, only the first one
+                is kept.
+            unk_token (:obj:`str`, `optional`):
+                The unknown token to be used by the model.
+        """
+
+        if special_tokens is None:
+            special_tokens = []
+
+        if initial_alphabet is None:
+            initial_alphabet = []
+
+        trainer = trainers.UnigramTrainer(
+            vocab_size=vocab_size,
+            special_tokens=special_tokens,
+            show_progress=show_progress,
+            initial_alphabet=initial_alphabet,
+            unk_token=unk_token,
+        )
+
+        if isinstance(files, str):
+            files = [files]
+        self._tokenizer.train(files, trainer=trainer)
+
+    def train_from_iterator(
+        self,
+        iterator: Union[Iterator[str], Iterator[Iterator[str]]],
+        vocab_size: int = 8000,
+        show_progress: bool = True,
+        special_tokens: Optional[List[Union[str, AddedToken]]] = None,
+        initial_alphabet: Optional[List[str]] = None,
+        unk_token: Optional[str] = None,
+        length: Optional[int] = None,
+    ):
+        """
+        Train the model using the given iterator
+
+        Args:
+            iterator (:obj:`Union[Iterator[str], Iterator[Iterator[str]]]`):
+                Any iterator over strings or list of strings
+            vocab_size (:obj:`int`):
+                The size of the final vocabulary, including all tokens and alphabet.
+            show_progress (:obj:`bool`):
+                Whether to show progress bars while training.
+            special_tokens (:obj:`List[Union[str, AddedToken]]`, `optional`):
+                A list of special tokens the model should know of.
+            initial_alphabet (:obj:`List[str]`, `optional`):
+                A list of characters to include in the initial alphabet, even
+                if not seen in the training dataset.
+                If the strings contain more than one character, only the first one
+                is kept.
+            unk_token (:obj:`str`, `optional`):
+                The unknown token to be used by the model.
+            length (:obj:`int`, `optional`):
+                The total number of sequences in the iterator. This is used to
+                provide meaningful progress tracking
+        """
+
+        if special_tokens is None:
+            special_tokens = []
+
+        if initial_alphabet is None:
+            initial_alphabet = []
+
+        trainer = trainers.UnigramTrainer(
+            vocab_size=vocab_size,
+            special_tokens=special_tokens,
+            show_progress=show_progress,
+            initial_alphabet=initial_alphabet,
+            unk_token=unk_token,
+        )
+
+        self._tokenizer.train_from_iterator(
+            iterator,
+            trainer=trainer,
+            length=length,
+        )
+
+    @staticmethod
+    def from_spm(filename: str):
+        try:
+            import sys
+
+            sys.path.append(".")
+
+            import sentencepiece_model_pb2 as model
+        except Exception:
+            raise Exception(
+                "You don't seem to have the required protobuf file, in order to use this function you need to run `pip install protobuf` and `wget https://raw.githubusercontent.com/google/sentencepiece/master/python/src/sentencepiece/sentencepiece_model_pb2.py` for us to be able to read the intrinsics of your spm_file. `pip install sentencepiece` is not required."
+            )
+
+        m = model.ModelProto()
+        m.ParseFromString(open(filename, "rb").read())
+
+        precompiled_charsmap = m.normalizer_spec.precompiled_charsmap
+        vocab = [(piece.piece, piece.score) for piece in m.pieces]
+        unk_id = m.trainer_spec.unk_id
+        model_type = m.trainer_spec.model_type
+        byte_fallback = m.trainer_spec.byte_fallback
+        if model_type != 1:
+            raise Exception(
+                "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
+            )
+
+        replacement = "▁"
+        add_prefix_space = True
+
+        tokenizer = Tokenizer(Unigram(vocab, unk_id, byte_fallback))
+
+        if precompiled_charsmap:
+            tokenizer.normalizer = normalizers.Sequence(
+                [
+                    normalizers.Precompiled(precompiled_charsmap),
+                    normalizers.Replace(Regex(" {2,}"), " "),
+                ]
+            )
+        else:
+            tokenizer.normalizer = normalizers.Sequence([normalizers.Replace(Regex(" {2,}"), " ")])
+        prepend_scheme = "always" if add_prefix_space else "never"
+        tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement=replacement, prepend_scheme=prepend_scheme)
+        tokenizer.decoder = decoders.Metaspace(replacement=replacement, prepend_scheme=prepend_scheme)
+
+        parameters = {
+            "model": "SentencePieceUnigram",
+        }
+
+        obj = BaseTokenizer.__new__(SentencePieceUnigramTokenizer, tokenizer, parameters)
+        BaseTokenizer.__init__(obj, tokenizer, parameters)
+        return obj

wemm/lib/python3.10/site-packages/tokenizers/models/__init__.py

@@ -0,0 +1,8 @@
+# Generated content DO NOT EDIT
+from .. import models
+
+Model = models.Model
+BPE = models.BPE
+Unigram = models.Unigram
+WordLevel = models.WordLevel
+WordPiece = models.WordPiece

wemm/lib/python3.10/site-packages/tokenizers/normalizers/__init__.pyi

@@ -0,0 +1,595 @@
+# Generated content DO NOT EDIT
+class Normalizer:
+    """
+    Base class for all normalizers
+
+    This class is not supposed to be instantiated directly. Instead, any implementation of a
+    Normalizer will return an instance of this class when instantiated.
+    """
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class BertNormalizer(Normalizer):
+    """
+    BertNormalizer
+
+    Takes care of normalizing raw text before giving it to a Bert model.
+    This includes cleaning the text, handling accents, chinese chars and lowercasing
+
+    Args:
+        clean_text (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to clean the text, by removing any control characters
+            and replacing all whitespaces by the classic one.
+
+        handle_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to handle chinese chars by putting spaces around them.
+
+        strip_accents (:obj:`bool`, `optional`):
+            Whether to strip all accents. If this option is not specified (ie == None),
+            then it will be determined by the value for `lowercase` (as in the original Bert).
+
+        lowercase (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to lowercase.
+    """
+    def __init__(self, clean_text=True, handle_chinese_chars=True, strip_accents=None, lowercase=True):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class Lowercase(Normalizer):
+    """
+    Lowercase Normalizer
+    """
+    def __init__(self):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class NFC(Normalizer):
+    """
+    NFC Unicode Normalizer
+    """
+    def __init__(self):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class NFD(Normalizer):
+    """
+    NFD Unicode Normalizer
+    """
+    def __init__(self):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class NFKC(Normalizer):
+    """
+    NFKC Unicode Normalizer
+    """
+    def __init__(self):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class NFKD(Normalizer):
+    """
+    NFKD Unicode Normalizer
+    """
+    def __init__(self):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class Nmt(Normalizer):
+    """
+    Nmt normalizer
+    """
+    def __init__(self):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class Precompiled(Normalizer):
+    """
+    Precompiled normalizer
+    Don't use manually it is used for compatiblity for SentencePiece.
+    """
+    def __init__(self, precompiled_charsmap):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class Prepend(Normalizer):
+    """
+    Prepend normalizer
+    """
+    def __init__(self, prepend):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class Replace(Normalizer):
+    """
+    Replace normalizer
+    """
+    def __init__(self, pattern, content):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class Sequence(Normalizer):
+    """
+    Allows concatenating multiple other Normalizer as a Sequence.
+    All the normalizers run in sequence in the given order
+
+    Args:
+        normalizers (:obj:`List[Normalizer]`):
+            A list of Normalizer to be run as a sequence
+    """
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class Strip(Normalizer):
+    """
+    Strip normalizer
+    """
+    def __init__(self, left=True, right=True):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass
+
+class StripAccents(Normalizer):
+    """
+    StripAccents normalizer
+    """
+    def __init__(self):
+        pass
+
+    def normalize(self, normalized):
+        """
+        Normalize a :class:`~tokenizers.NormalizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.NormalizedString` to
+        keep track of the alignment information. If you just want to see the result
+        of the normalization on a raw string, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize_str`
+
+        Args:
+            normalized (:class:`~tokenizers.NormalizedString`):
+                The normalized string on which to apply this
+                :class:`~tokenizers.normalizers.Normalizer`
+        """
+        pass
+
+    def normalize_str(self, sequence):
+        """
+        Normalize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.normalizers.Normalizer` but it does not keep track of the alignment
+        information. If you need to get/convert offsets, you can use
+        :meth:`~tokenizers.normalizers.Normalizer.normalize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to normalize
+
+        Returns:
+            :obj:`str`: A string after normalization
+        """
+        pass

wemm/lib/python3.10/site-packages/tokenizers/pre_tokenizers/__init__.pyi

@@ -0,0 +1,607 @@
+# Generated content DO NOT EDIT
+class PreTokenizer:
+    """
+    Base class for all pre-tokenizers
+
+    This class is not supposed to be instantiated directly. Instead, any implementation of a
+    PreTokenizer will return an instance of this class when instantiated.
+    """
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class BertPreTokenizer(PreTokenizer):
+    """
+    BertPreTokenizer
+
+    This pre-tokenizer splits tokens on spaces, and also on punctuation.
+    Each occurence of a punctuation character will be treated separately.
+    """
+    def __init__(self):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class ByteLevel(PreTokenizer):
+    """
+    ByteLevel PreTokenizer
+
+    This pre-tokenizer takes care of replacing all bytes of the given string
+    with a corresponding representation, as well as splitting into words.
+
+    Args:
+        add_prefix_space (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to add a space to the first word if there isn't already one. This
+            lets us treat `hello` exactly like `say hello`.
+        use_regex (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Set this to :obj:`False` to prevent this `pre_tokenizer` from using
+            the GPT2 specific regexp for spliting on whitespace.
+    """
+    def __init__(self, add_prefix_space=True, use_regex=True):
+        pass
+
+    @staticmethod
+    def alphabet():
+        """
+        Returns the alphabet used by this PreTokenizer.
+
+        Since the ByteLevel works as its name suggests, at the byte level, it
+        encodes each byte value to a unique visible character. This means that there is a
+        total of 256 different characters composing this alphabet.
+
+        Returns:
+            :obj:`List[str]`: A list of characters that compose the alphabet
+        """
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class CharDelimiterSplit(PreTokenizer):
+    """
+    This pre-tokenizer simply splits on the provided char. Works like `.split(delimiter)`
+
+    Args:
+        delimiter: str:
+            The delimiter char that will be used to split input
+    """
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class Digits(PreTokenizer):
+    """
+    This pre-tokenizer simply splits using the digits in separate tokens
+
+    Args:
+        individual_digits (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            If set to True, digits will each be separated as follows::
+
+                "Call 123 please" -> "Call ", "1", "2", "3", " please"
+
+            If set to False, digits will grouped as follows::
+
+                "Call 123 please" -> "Call ", "123", " please"
+    """
+    def __init__(self, individual_digits=False):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class Metaspace(PreTokenizer):
+    """
+    Metaspace pre-tokenizer
+
+    This pre-tokenizer replaces any whitespace by the provided replacement character.
+    It then tries to split on these spaces.
+
+    Args:
+        replacement (:obj:`str`, `optional`, defaults to :obj:`▁`):
+            The replacement character. Must be exactly one character. By default we
+            use the `▁` (U+2581) meta symbol (Same as in SentencePiece).
+
+        prepend_scheme (:obj:`str`, `optional`, defaults to :obj:`"always"`):
+            Whether to add a space to the first word if there isn't already one. This
+            lets us treat `hello` exactly like `say hello`.
+            Choices: "always", "never", "first". First means the space is only added on the first
+            token (relevant when special tokens are used or other pre_tokenizer are used).
+
+    """
+    def __init__(self, replacement="_", prepend_scheme="always", split=True):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class Punctuation(PreTokenizer):
+    """
+    This pre-tokenizer simply splits on punctuation as individual characters.
+
+    Args:
+        behavior (:class:`~tokenizers.SplitDelimiterBehavior`):
+            The behavior to use when splitting.
+            Choices: "removed", "isolated" (default), "merged_with_previous", "merged_with_next",
+            "contiguous"
+    """
+    def __init__(self, behavior="isolated"):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class Sequence(PreTokenizer):
+    """
+    This pre-tokenizer composes other pre_tokenizers and applies them in sequence
+    """
+    def __init__(self, pretokenizers):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class Split(PreTokenizer):
+    """
+    Split PreTokenizer
+
+    This versatile pre-tokenizer splits using the provided pattern and
+    according to the provided behavior. The pattern can be inverted by
+    making use of the invert flag.
+
+    Args:
+        pattern (:obj:`str` or :class:`~tokenizers.Regex`):
+            A pattern used to split the string. Usually a string or a a regex built with `tokenizers.Regex`
+
+        behavior (:class:`~tokenizers.SplitDelimiterBehavior`):
+            The behavior to use when splitting.
+            Choices: "removed", "isolated", "merged_with_previous", "merged_with_next",
+            "contiguous"
+
+        invert (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether to invert the pattern.
+    """
+    def __init__(self, pattern, behavior, invert=False):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class UnicodeScripts(PreTokenizer):
+    """
+    This pre-tokenizer splits on characters that belong to different language family
+    It roughly follows https://github.com/google/sentencepiece/blob/master/data/Scripts.txt
+    Actually Hiragana and Katakana are fused with Han, and 0x30FC is Han too.
+    This mimicks SentencePiece Unigram implementation.
+    """
+    def __init__(self):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class Whitespace(PreTokenizer):
+    """
+    This pre-tokenizer simply splits using the following regex: `\w+|[^\w\s]+`
+    """
+    def __init__(self):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass
+
+class WhitespaceSplit(PreTokenizer):
+    """
+    This pre-tokenizer simply splits on the whitespace. Works like `.split()`
+    """
+    def __init__(self):
+        pass
+
+    def pre_tokenize(self, pretok):
+        """
+        Pre-tokenize a :class:`~tokenizers.PyPreTokenizedString` in-place
+
+        This method allows to modify a :class:`~tokenizers.PreTokenizedString` to
+        keep track of the pre-tokenization, and leverage the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you just want to see the result of
+        the pre-tokenization of a raw string, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize_str`
+
+        Args:
+            pretok (:class:`~tokenizers.PreTokenizedString):
+                The pre-tokenized string on which to apply this
+                :class:`~tokenizers.pre_tokenizers.PreTokenizer`
+        """
+        pass
+
+    def pre_tokenize_str(self, sequence):
+        """
+        Pre tokenize the given string
+
+        This method provides a way to visualize the effect of a
+        :class:`~tokenizers.pre_tokenizers.PreTokenizer` but it does not keep track of the
+        alignment, nor does it provide all the capabilities of the
+        :class:`~tokenizers.PreTokenizedString`. If you need some of these, you can use
+        :meth:`~tokenizers.pre_tokenizers.PreTokenizer.pre_tokenize`
+
+        Args:
+            sequence (:obj:`str`):
+                A string to pre-tokeize
+
+        Returns:
+            :obj:`List[Tuple[str, Offsets]]`:
+                A list of tuple with the pre-tokenized parts and their offsets
+        """
+        pass

wemm/lib/python3.10/site-packages/tokenizers/processors/__init__.py

@@ -0,0 +1,9 @@
+# Generated content DO NOT EDIT
+from .. import processors
+
+PostProcessor = processors.PostProcessor
+BertProcessing = processors.BertProcessing
+ByteLevel = processors.ByteLevel
+RobertaProcessing = processors.RobertaProcessing
+Sequence = processors.Sequence
+TemplateProcessing = processors.TemplateProcessing

wemm/lib/python3.10/site-packages/tokenizers/processors/__init__.pyi

@@ -0,0 +1,342 @@
+# Generated content DO NOT EDIT
+class PostProcessor:
+    """
+    Base class for all post-processors
+
+    This class is not supposed to be instantiated directly. Instead, any implementation of
+    a PostProcessor will return an instance of this class when instantiated.
+    """
+    def num_special_tokens_to_add(self, is_pair):
+        """
+        Return the number of special tokens that would be added for single/pair sentences.
+
+        Args:
+            is_pair (:obj:`bool`):
+                Whether the input would be a pair of sequences
+
+        Returns:
+            :obj:`int`: The number of tokens to add
+        """
+        pass
+
+    def process(self, encoding, pair=None, add_special_tokens=True):
+        """
+        Post-process the given encodings, generating the final one
+
+        Args:
+            encoding (:class:`~tokenizers.Encoding`):
+                The encoding for the first sequence
+
+            pair (:class:`~tokenizers.Encoding`, `optional`):
+                The encoding for the pair sequence
+
+            add_special_tokens (:obj:`bool`):
+                Whether to add the special tokens
+
+        Return:
+            :class:`~tokenizers.Encoding`: The final encoding
+        """
+        pass
+
+class BertProcessing(PostProcessor):
+    """
+    This post-processor takes care of adding the special tokens needed by
+    a Bert model:
+
+        - a SEP token
+        - a CLS token
+
+    Args:
+        sep (:obj:`Tuple[str, int]`):
+            A tuple with the string representation of the SEP token, and its id
+
+        cls (:obj:`Tuple[str, int]`):
+            A tuple with the string representation of the CLS token, and its id
+    """
+    def __init__(self, sep, cls):
+        pass
+
+    def num_special_tokens_to_add(self, is_pair):
+        """
+        Return the number of special tokens that would be added for single/pair sentences.
+
+        Args:
+            is_pair (:obj:`bool`):
+                Whether the input would be a pair of sequences
+
+        Returns:
+            :obj:`int`: The number of tokens to add
+        """
+        pass
+
+    def process(self, encoding, pair=None, add_special_tokens=True):
+        """
+        Post-process the given encodings, generating the final one
+
+        Args:
+            encoding (:class:`~tokenizers.Encoding`):
+                The encoding for the first sequence
+
+            pair (:class:`~tokenizers.Encoding`, `optional`):
+                The encoding for the pair sequence
+
+            add_special_tokens (:obj:`bool`):
+                Whether to add the special tokens
+
+        Return:
+            :class:`~tokenizers.Encoding`: The final encoding
+        """
+        pass
+
+class ByteLevel(PostProcessor):
+    """
+    This post-processor takes care of trimming the offsets.
+
+    By default, the ByteLevel BPE might include whitespaces in the produced tokens. If you don't
+    want the offsets to include these whitespaces, then this PostProcessor must be used.
+
+    Args:
+        trim_offsets (:obj:`bool`):
+            Whether to trim the whitespaces from the produced offsets.
+    """
+    def __init__(self, trim_offsets=True):
+        pass
+
+    def num_special_tokens_to_add(self, is_pair):
+        """
+        Return the number of special tokens that would be added for single/pair sentences.
+
+        Args:
+            is_pair (:obj:`bool`):
+                Whether the input would be a pair of sequences
+
+        Returns:
+            :obj:`int`: The number of tokens to add
+        """
+        pass
+
+    def process(self, encoding, pair=None, add_special_tokens=True):
+        """
+        Post-process the given encodings, generating the final one
+
+        Args:
+            encoding (:class:`~tokenizers.Encoding`):
+                The encoding for the first sequence
+
+            pair (:class:`~tokenizers.Encoding`, `optional`):
+                The encoding for the pair sequence
+
+            add_special_tokens (:obj:`bool`):
+                Whether to add the special tokens
+
+        Return:
+            :class:`~tokenizers.Encoding`: The final encoding
+        """
+        pass
+
+class RobertaProcessing(PostProcessor):
+    """
+    This post-processor takes care of adding the special tokens needed by
+    a Roberta model:
+
+        - a SEP token
+        - a CLS token
+
+    It also takes care of trimming the offsets.
+    By default, the ByteLevel BPE might include whitespaces in the produced tokens. If you don't
+    want the offsets to include these whitespaces, then this PostProcessor should be initialized
+    with :obj:`trim_offsets=True`
+
+    Args:
+        sep (:obj:`Tuple[str, int]`):
+            A tuple with the string representation of the SEP token, and its id
+
+        cls (:obj:`Tuple[str, int]`):
+            A tuple with the string representation of the CLS token, and its id
+
+        trim_offsets (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether to trim the whitespaces from the produced offsets.
+
+        add_prefix_space (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether the add_prefix_space option was enabled during pre-tokenization. This
+            is relevant because it defines the way the offsets are trimmed out.
+    """
+    def __init__(self, sep, cls, trim_offsets=True, add_prefix_space=True):
+        pass
+
+    def num_special_tokens_to_add(self, is_pair):
+        """
+        Return the number of special tokens that would be added for single/pair sentences.
+
+        Args:
+            is_pair (:obj:`bool`):
+                Whether the input would be a pair of sequences
+
+        Returns:
+            :obj:`int`: The number of tokens to add
+        """
+        pass
+
+    def process(self, encoding, pair=None, add_special_tokens=True):
+        """
+        Post-process the given encodings, generating the final one
+
+        Args:
+            encoding (:class:`~tokenizers.Encoding`):
+                The encoding for the first sequence
+
+            pair (:class:`~tokenizers.Encoding`, `optional`):
+                The encoding for the pair sequence
+
+            add_special_tokens (:obj:`bool`):
+                Whether to add the special tokens
+
+        Return:
+            :class:`~tokenizers.Encoding`: The final encoding
+        """
+        pass
+
+class Sequence(PostProcessor):
+    """
+    Sequence Processor
+
+    Args:
+        processors (:obj:`List[PostProcessor]`)
+            The processors that need to be chained
+    """
+    def __init__(self, processors):
+        pass
+
+    def num_special_tokens_to_add(self, is_pair):
+        """
+        Return the number of special tokens that would be added for single/pair sentences.
+
+        Args:
+            is_pair (:obj:`bool`):
+                Whether the input would be a pair of sequences
+
+        Returns:
+            :obj:`int`: The number of tokens to add
+        """
+        pass
+
+    def process(self, encoding, pair=None, add_special_tokens=True):
+        """
+        Post-process the given encodings, generating the final one
+
+        Args:
+            encoding (:class:`~tokenizers.Encoding`):
+                The encoding for the first sequence
+
+            pair (:class:`~tokenizers.Encoding`, `optional`):
+                The encoding for the pair sequence
+
+            add_special_tokens (:obj:`bool`):
+                Whether to add the special tokens
+
+        Return:
+            :class:`~tokenizers.Encoding`: The final encoding
+        """
+        pass
+
+class TemplateProcessing(PostProcessor):
+    """
+    Provides a way to specify templates in order to add the special tokens to each
+    input sequence as relevant.
+
+    Let's take :obj:`BERT` tokenizer as an example. It uses two special tokens, used to
+    delimitate each sequence. :obj:`[CLS]` is always used at the beginning of the first
+    sequence, and :obj:`[SEP]` is added at the end of both the first, and the pair
+    sequences. The final result looks like this:
+
+        - Single sequence: :obj:`[CLS] Hello there [SEP]`
+        - Pair sequences: :obj:`[CLS] My name is Anthony [SEP] What is my name? [SEP]`
+
+    With the type ids as following::
+
+        [CLS]   ...   [SEP]   ...   [SEP]
+          0      0      0      1      1
+
+    You can achieve such behavior using a TemplateProcessing::
+
+        TemplateProcessing(
+            single="[CLS] $0 [SEP]",
+            pair="[CLS] $A [SEP] $B:1 [SEP]:1",
+            special_tokens=[("[CLS]", 1), ("[SEP]", 0)],
+        )
+
+    In this example, each input sequence is identified using a ``$`` construct. This identifier
+    lets us specify each input sequence, and the type_id to use. When nothing is specified,
+    it uses the default values. Here are the different ways to specify it:
+
+        - Specifying the sequence, with default ``type_id == 0``: ``$A`` or ``$B``
+        - Specifying the `type_id` with default ``sequence == A``: ``$0``, ``$1``, ``$2``, ...
+        - Specifying both: ``$A:0``, ``$B:1``, ...
+
+    The same construct is used for special tokens: ``<identifier>(:<type_id>)?``.
+
+    **Warning**: You must ensure that you are giving the correct tokens/ids as these
+    will be added to the Encoding without any further check. If the given ids correspond
+    to something totally different in a `Tokenizer` using this `PostProcessor`, it
+    might lead to unexpected results.
+
+    Args:
+        single (:obj:`Template`):
+            The template used for single sequences
+
+        pair (:obj:`Template`):
+            The template used when both sequences are specified
+
+        special_tokens (:obj:`Tokens`):
+            The list of special tokens used in each sequences
+
+    Types:
+
+        Template (:obj:`str` or :obj:`List`):
+            - If a :obj:`str` is provided, the whitespace is used as delimiter between tokens
+            - If a :obj:`List[str]` is provided, a list of tokens
+
+        Tokens (:obj:`List[Union[Tuple[int, str], Tuple[str, int], dict]]`):
+            - A :obj:`Tuple` with both a token and its associated ID, in any order
+            - A :obj:`dict` with the following keys:
+                - "id": :obj:`str` => The special token id, as specified in the Template
+                - "ids": :obj:`List[int]` => The associated IDs
+                - "tokens": :obj:`List[str]` => The associated tokens
+
+             The given dict expects the provided :obj:`ids` and :obj:`tokens` lists to have
+             the same length.
+    """
+    def __init__(self, single, pair, special_tokens):
+        pass
+
+    def num_special_tokens_to_add(self, is_pair):
+        """
+        Return the number of special tokens that would be added for single/pair sentences.
+
+        Args:
+            is_pair (:obj:`bool`):
+                Whether the input would be a pair of sequences
+
+        Returns:
+            :obj:`int`: The number of tokens to add
+        """
+        pass
+
+    def process(self, encoding, pair=None, add_special_tokens=True):
+        """
+        Post-process the given encodings, generating the final one
+
+        Args:
+            encoding (:class:`~tokenizers.Encoding`):
+                The encoding for the first sequence
+
+            pair (:class:`~tokenizers.Encoding`, `optional`):
+                The encoding for the pair sequence
+
+            add_special_tokens (:obj:`bool`):
+                Whether to add the special tokens
+
+        Return:
+            :class:`~tokenizers.Encoding`: The final encoding
+        """
+        pass

wemm/lib/python3.10/site-packages/tokenizers/tokenizers.cpython-310-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e4732f10ce97c9f5e903774dcb953c0f1518f40b27e287992075311835cefce
+size 11815960

wemm/lib/python3.10/site-packages/tokenizers/tools/__init__.py

@@ -0,0 +1 @@
+from .visualizer import Annotation, EncodingVisualizer

wemm/lib/python3.10/site-packages/tokenizers/tools/visualizer-styles.css

@@ -0,0 +1,170 @@
+.tokenized-text {
+    width:100%;
+    padding:2rem;
+    max-height: 400px;
+    overflow-y: auto;
+    box-sizing:border-box;
+    line-height:4rem; /* Lots of space between lines */
+    font-family: "Roboto Light", "Ubuntu Light", "Ubuntu", monospace;
+    box-shadow: 2px 2px 2px rgba(0,0,0,0.2);
+    background-color: rgba(0,0,0,0.01);
+    letter-spacing:2px; /* Give some extra separation between chars */
+}
+.non-token{
+    /* White space and other things the tokenizer ignores*/
+    white-space: pre;
+    letter-spacing:4px;
+    border-top:1px solid #A0A0A0; /* A gentle border on top and bottom makes tabs more ovious*/
+    border-bottom:1px solid #A0A0A0;
+    line-height: 1rem;
+    height: calc(100% - 2px);
+}
+
+.token {
+    white-space: pre;
+    position:relative;
+    color:black;
+    letter-spacing:2px;
+}
+
+.annotation{
+    white-space:nowrap; /* Important - ensures that annotations appears even if the annotated text wraps a line */
+    border-radius:4px;
+    position:relative;
+    width:fit-content;
+}
+.annotation:before {
+    /*The before holds the text and the after holds the background*/
+    z-index:1000; /* Make sure this is above the background */
+    content:attr(data-label); /* The annotations label is on a data attribute */
+    color:white;
+    position:absolute;
+    font-size:1rem;
+    text-align:center;
+    font-weight:bold;
+
+    top:1.75rem;
+    line-height:0;
+    left:0;
+    width:100%;
+    padding:0.5rem 0;
+    /* These make it so an annotation doesn't stretch beyond the annotated text if the label is longer*/
+    overflow: hidden;
+    white-space: nowrap;
+    text-overflow:ellipsis;
+}
+
+.annotation:after {
+    content:attr(data-label); /* The content defines the width of the annotation*/
+    position:absolute;
+    font-size:0.75rem;
+    text-align:center;
+    font-weight:bold;
+    text-overflow:ellipsis;
+    top:1.75rem;
+    line-height:0;
+    overflow: hidden;
+    white-space: nowrap;
+
+    left:0;
+    width:100%; /* 100% of the parent, which is the annotation whose width is the tokens inside it*/
+
+    padding:0.5rem 0;
+    /* Nast hack below:
+    We set the annotations color in code because we don't know the colors at css time.
+    But you can't pass a color as a data attribute to get it into the pseudo element (this thing)
+    So to get around that, annotations have the color set on them with a style attribute and then we
+    can get the color with currentColor.
+    Annotations wrap tokens and tokens set the color back to black
+     */
+    background-color: currentColor;
+}
+.annotation:hover::after, .annotation:hover::before{
+    /* When the user hovers over an annotation expand the label to display in full
+     */
+    min-width: fit-content;
+}
+
+.annotation:hover{
+    /* Emphasize the annotation start end with a border on hover*/
+    border-color: currentColor;
+    border: 2px solid;
+}
+.special-token:not(:empty){
+    /*
+    A none empty special token is like UNK (as opposed to CLS which has no representation in the text )
+     */
+    position:relative;
+}
+.special-token:empty::before{
+    /* Special tokens that don't have text are displayed as pseudo elements so we dont select them with the mouse*/
+    content:attr(data-stok);
+    background:#202020;
+    font-size:0.75rem;
+    color:white;
+    margin: 0 0.25rem;
+    padding: 0.25rem;
+    border-radius:4px
+}
+
+.special-token:not(:empty):before {
+    /* Special tokens that have text (UNK) are displayed above the actual text*/
+    content:attr(data-stok);
+    position:absolute;
+    bottom:1.75rem;
+    min-width:100%;
+    width:100%;
+    height:1rem;
+    line-height:1rem;
+    font-size:1rem;
+    text-align:center;
+    color:white;
+    font-weight:bold;
+    background:#202020;
+    border-radius:10%;
+}
+/*
+We want to alternate the color of tokens, but we can't use nth child because tokens might be broken up by annotations
+instead we apply even and odd class at generation time and color them that way
+ */
+.even-token{
+    background:#DCDCDC	;
+    border: 1px solid #DCDCDC;
+}
+.odd-token{
+    background:#A0A0A0;
+    border: 1px solid #A0A0A0;
+}
+.even-token.multi-token,.odd-token.multi-token{
+    background:  repeating-linear-gradient(
+    45deg,
+    transparent,
+    transparent 1px,
+    #ccc 1px,
+    #ccc 1px
+    ),
+    /* on "bottom" */
+    linear-gradient(
+    to bottom,
+    #FFB6C1,
+    #999
+    );
+}
+
+.multi-token:hover::after {
+    content:"This char has more than 1 token"; /* The content defines the width of the annotation*/
+    color:white;
+    background-color: black;
+    position:absolute;
+    font-size:0.75rem;
+    text-align:center;
+    font-weight:bold;
+    text-overflow:ellipsis;
+    top:1.75rem;
+    line-height:0;
+    overflow: hidden;
+    white-space: nowrap;
+    left:0;
+    width:fit-content; /* 100% of the parent, which is the annotation whose width is the tokens inside it*/
+    padding:0.5rem 0;
+}

wemm/lib/python3.10/site-packages/tokenizers/tools/visualizer.py

@@ -0,0 +1,403 @@
+import itertools
+import os
+import re
+from string import Template
+from typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple
+
+from tokenizers import Encoding, Tokenizer
+
+
+dirname = os.path.dirname(__file__)
+css_filename = os.path.join(dirname, "visualizer-styles.css")
+with open(css_filename) as f:
+    css = f.read()
+
+
+class Annotation:
+    start: int
+    end: int
+    label: int
+
+    def __init__(self, start: int, end: int, label: str):
+        self.start = start
+        self.end = end
+        self.label = label
+
+
+AnnotationList = List[Annotation]
+PartialIntList = List[Optional[int]]
+
+
+class CharStateKey(NamedTuple):
+    token_ix: Optional[int]
+    anno_ix: Optional[int]
+
+
+class CharState:
+    char_ix: Optional[int]
+
+    def __init__(self, char_ix):
+        self.char_ix = char_ix
+
+        self.anno_ix: Optional[int] = None
+        self.tokens: List[int] = []
+
+    @property
+    def token_ix(self):
+        return self.tokens[0] if len(self.tokens) > 0 else None
+
+    @property
+    def is_multitoken(self):
+        """
+        BPE tokenizers can output more than one token for a char
+        """
+        return len(self.tokens) > 1
+
+    def partition_key(self) -> CharStateKey:
+        return CharStateKey(
+            token_ix=self.token_ix,
+            anno_ix=self.anno_ix,
+        )
+
+
+class Aligned:
+    pass
+
+
+class EncodingVisualizer:
+    """
+    Build an EncodingVisualizer
+
+    Args:
+
+         tokenizer (:class:`~tokenizers.Tokenizer`):
+            A tokenizer instance
+
+         default_to_notebook (:obj:`bool`):
+            Whether to render html output in a notebook by default
+
+         annotation_converter (:obj:`Callable`, `optional`):
+            An optional (lambda) function that takes an annotation in any format and returns
+            an Annotation object
+    """
+
+    unk_token_regex = re.compile("(.{1}\b)?(unk|oov)(\b.{1})?", flags=re.IGNORECASE)
+
+    def __init__(
+        self,
+        tokenizer: Tokenizer,
+        default_to_notebook: bool = True,
+        annotation_converter: Optional[Callable[[Any], Annotation]] = None,
+    ):
+        if default_to_notebook:
+            try:
+                from IPython.core.display import HTML, display
+            except ImportError:
+                raise Exception(
+                    """We couldn't import IPython utils for html display.
+                        Are you running in a notebook?
+                        You can also pass `default_to_notebook=False` to get back raw HTML
+                    """
+                )
+
+        self.tokenizer = tokenizer
+        self.default_to_notebook = default_to_notebook
+        self.annotation_coverter = annotation_converter
+        pass
+
+    def __call__(
+        self,
+        text: str,
+        annotations: AnnotationList = [],
+        default_to_notebook: Optional[bool] = None,
+    ) -> Optional[str]:
+        """
+        Build a visualization of the given text
+
+        Args:
+            text (:obj:`str`):
+                The text to tokenize
+
+            annotations (:obj:`List[Annotation]`, `optional`):
+                An optional list of annotations of the text. The can either be an annotation class
+                or anything else if you instantiated the visualizer with a converter function
+
+            default_to_notebook (:obj:`bool`, `optional`, defaults to `False`):
+                If True, will render the html in a notebook. Otherwise returns an html string.
+
+        Returns:
+            The HTML string if default_to_notebook is False, otherwise (default) returns None and
+            renders the HTML in the notebook
+
+        """
+        final_default_to_notebook = self.default_to_notebook
+        if default_to_notebook is not None:
+            final_default_to_notebook = default_to_notebook
+        if final_default_to_notebook:
+            try:
+                from IPython.core.display import HTML, display
+            except ImportError:
+                raise Exception(
+                    """We couldn't import IPython utils for html display.
+                    Are you running in a notebook?"""
+                )
+        if self.annotation_coverter is not None:
+            annotations = list(map(self.annotation_coverter, annotations))
+        encoding = self.tokenizer.encode(text)
+        html = EncodingVisualizer.__make_html(text, encoding, annotations)
+        if final_default_to_notebook:
+            display(HTML(html))
+        else:
+            return html
+
+    @staticmethod
+    def calculate_label_colors(annotations: AnnotationList) -> Dict[str, str]:
+        """
+        Generates a color palette for all the labels in a given set of annotations
+
+        Args:
+          annotations (:obj:`Annotation`):
+            A list of annotations
+
+        Returns:
+            :obj:`dict`: A dictionary mapping labels to colors in HSL format
+        """
+        if len(annotations) == 0:
+            return {}
+        labels = set(map(lambda x: x.label, annotations))
+        num_labels = len(labels)
+        h_step = int(255 / num_labels)
+        if h_step < 20:
+            h_step = 20
+        s = 32
+        l = 64  # noqa: E741
+        h = 10
+        colors = {}
+
+        for label in sorted(labels):  # sort so we always get the same colors for a given set of labels
+            colors[label] = f"hsl({h},{s}%,{l}%"
+            h += h_step
+        return colors
+
+    @staticmethod
+    def consecutive_chars_to_html(
+        consecutive_chars_list: List[CharState],
+        text: str,
+        encoding: Encoding,
+    ):
+        """
+        Converts a list of "consecutive chars" into a single HTML element.
+        Chars are consecutive if they fall under the same word, token and annotation.
+        The CharState class is a named tuple with a "partition_key" method that makes it easy to
+        compare if two chars are consecutive.
+
+        Args:
+            consecutive_chars_list (:obj:`List[CharState]`):
+                A list of CharStates that have been grouped together
+
+            text (:obj:`str`):
+                The original text being processed
+
+            encoding (:class:`~tokenizers.Encoding`):
+                The encoding returned from the tokenizer
+
+        Returns:
+            :obj:`str`: The HTML span for a set of consecutive chars
+        """
+        first = consecutive_chars_list[0]
+        if first.char_ix is None:
+            # its a special token
+            stoken = encoding.tokens[first.token_ix]
+            # special tokens are represented as empty spans. We use the data attribute and css
+            # magic to display it
+            return f'<span class="special-token" data-stoken={stoken}></span>'
+        # We're not in a special token so this group has a start and end.
+        last = consecutive_chars_list[-1]
+        start = first.char_ix
+        end = last.char_ix + 1
+        span_text = text[start:end]
+        css_classes = []  # What css classes will we apply on the resulting span
+        data_items = {}  # What data attributes will we apply on the result span
+        if first.token_ix is not None:
+            # We can either be in a token or not (e.g. in white space)
+            css_classes.append("token")
+            if first.is_multitoken:
+                css_classes.append("multi-token")
+            if first.token_ix % 2:
+                # We use this to color alternating tokens.
+                # A token might be split by an annotation that ends in the middle of it, so this
+                # lets us visually indicate a consecutive token despite its possible splitting in
+                # the html markup
+                css_classes.append("odd-token")
+            else:
+                # Like above, but a different color so we can see the tokens alternate
+                css_classes.append("even-token")
+            if EncodingVisualizer.unk_token_regex.search(encoding.tokens[first.token_ix]) is not None:
+                # This is a special token that is in the text. probably UNK
+                css_classes.append("special-token")
+                # TODO is this the right name for the data attribute ?
+                data_items["stok"] = encoding.tokens[first.token_ix]
+        else:
+            # In this case we are looking at a group/single char that is not tokenized.
+            # e.g. white space
+            css_classes.append("non-token")
+        css = f'''class="{' '.join(css_classes)}"'''
+        data = ""
+        for key, val in data_items.items():
+            data += f' data-{key}="{val}"'
+        return f"<span {css} {data} >{span_text}</span>"
+
+    @staticmethod
+    def __make_html(text: str, encoding: Encoding, annotations: AnnotationList) -> str:
+        char_states = EncodingVisualizer.__make_char_states(text, encoding, annotations)
+        current_consecutive_chars = [char_states[0]]
+        prev_anno_ix = char_states[0].anno_ix
+        spans = []
+        label_colors_dict = EncodingVisualizer.calculate_label_colors(annotations)
+        cur_anno_ix = char_states[0].anno_ix
+        if cur_anno_ix is not None:
+            # If we started in an  annotation make a span for it
+            anno = annotations[cur_anno_ix]
+            label = anno.label
+            color = label_colors_dict[label]
+            spans.append(f'<span class="annotation" style="color:{color}" data-label="{label}">')
+
+        for cs in char_states[1:]:
+            cur_anno_ix = cs.anno_ix
+            if cur_anno_ix != prev_anno_ix:
+                # If we've transitioned in or out of an annotation
+                spans.append(
+                    # Create a span from the current consecutive characters
+                    EncodingVisualizer.consecutive_chars_to_html(
+                        current_consecutive_chars,
+                        text=text,
+                        encoding=encoding,
+                    )
+                )
+                current_consecutive_chars = [cs]
+
+                if prev_anno_ix is not None:
+                    # if we transitioned out of an annotation close it's span
+                    spans.append("</span>")
+                if cur_anno_ix is not None:
+                    # If we entered a new annotation make a span for it
+                    anno = annotations[cur_anno_ix]
+                    label = anno.label
+                    color = label_colors_dict[label]
+                    spans.append(f'<span class="annotation" style="color:{color}" data-label="{label}">')
+            prev_anno_ix = cur_anno_ix
+
+            if cs.partition_key() == current_consecutive_chars[0].partition_key():
+                # If the current charchter is in the same "group" as the previous one
+                current_consecutive_chars.append(cs)
+            else:
+                # Otherwise we make a span for the previous group
+                spans.append(
+                    EncodingVisualizer.consecutive_chars_to_html(
+                        current_consecutive_chars,
+                        text=text,
+                        encoding=encoding,
+                    )
+                )
+                # An reset the consecutive_char_list to form a new group
+                current_consecutive_chars = [cs]
+        # All that's left is to fill out the final span
+        # TODO I think there is an edge case here where an annotation's span might not close
+        spans.append(
+            EncodingVisualizer.consecutive_chars_to_html(
+                current_consecutive_chars,
+                text=text,
+                encoding=encoding,
+            )
+        )
+        res = HTMLBody(spans)  # Send the list of spans to the body of our html
+        return res
+
+    @staticmethod
+    def __make_anno_map(text: str, annotations: AnnotationList) -> PartialIntList:
+        """
+        Args:
+            text (:obj:`str`):
+                The raw text we want to align to
+
+            annotations (:obj:`AnnotationList`):
+                A (possibly empty) list of annotations
+
+        Returns:
+            A list of  length len(text) whose entry at index i is None if there is no annotation on
+            charachter i or k, the index of the annotation that covers index i where k is with
+            respect to the list of annotations
+        """
+        annotation_map = [None] * len(text)
+        for anno_ix, a in enumerate(annotations):
+            for i in range(a.start, a.end):
+                annotation_map[i] = anno_ix
+        return annotation_map
+
+    @staticmethod
+    def __make_char_states(text: str, encoding: Encoding, annotations: AnnotationList) -> List[CharState]:
+        """
+        For each character in the original text, we emit a tuple representing it's "state":
+
+            * which token_ix it corresponds to
+            * which word_ix it corresponds to
+            * which annotation_ix it corresponds to
+
+        Args:
+            text (:obj:`str`):
+                The raw text we want to align to
+
+            annotations (:obj:`List[Annotation]`):
+                A (possibly empty) list of annotations
+
+            encoding: (:class:`~tokenizers.Encoding`):
+                The encoding returned from the tokenizer
+
+        Returns:
+            :obj:`List[CharState]`: A list of CharStates, indicating for each char in the text what
+            it's state is
+        """
+        annotation_map = EncodingVisualizer.__make_anno_map(text, annotations)
+        # Todo make this a dataclass or named tuple
+        char_states: List[CharState] = [CharState(char_ix) for char_ix in range(len(text))]
+        for token_ix, token in enumerate(encoding.tokens):
+            offsets = encoding.token_to_chars(token_ix)
+            if offsets is not None:
+                start, end = offsets
+                for i in range(start, end):
+                    char_states[i].tokens.append(token_ix)
+        for char_ix, anno_ix in enumerate(annotation_map):
+            char_states[char_ix].anno_ix = anno_ix
+
+        return char_states
+
+
+def HTMLBody(children: List[str], css_styles=css) -> str:
+    """
+    Generates the full html with css from a list of html spans
+
+    Args:
+        children (:obj:`List[str]`):
+            A list of strings, assumed to be html elements
+
+        css_styles (:obj:`str`, `optional`):
+            Optional alternative implementation of the css
+
+    Returns:
+        :obj:`str`: An HTML string with style markup
+    """
+    children_text = "".join(children)
+    return f"""
+    <html>
+        <head>
+            <style>
+                {css_styles}
+            </style>
+        </head>
+        <body>
+            <div class="tokenized-text" dir=auto>
+            {children_text}
+            </div>
+        </body>
+    </html>
+    """

wemm/lib/python3.10/site-packages/tokenizers/trainers/__init__.py

@@ -0,0 +1,8 @@
+# Generated content DO NOT EDIT
+from .. import trainers
+
+Trainer = trainers.Trainer
+BpeTrainer = trainers.BpeTrainer
+UnigramTrainer = trainers.UnigramTrainer
+WordLevelTrainer = trainers.WordLevelTrainer
+WordPieceTrainer = trainers.WordPieceTrainer

wemm/lib/python3.10/site-packages/tokenizers/trainers/__init__.pyi

@@ -0,0 +1,156 @@
+# Generated content DO NOT EDIT
+class Trainer:
+    """
+    Base class for all trainers
+
+    This class is not supposed to be instantiated directly. Instead, any implementation of a
+    Trainer will return an instance of this class when instantiated.
+    """
+
+class BpeTrainer(Trainer):
+    """
+    Trainer capable of training a BPE model
+
+    Args:
+        vocab_size (:obj:`int`, `optional`):
+            The size of the final vocabulary, including all tokens and alphabet.
+
+        min_frequency (:obj:`int`, `optional`):
+            The minimum frequency a pair should have in order to be merged.
+
+        show_progress (:obj:`bool`, `optional`):
+            Whether to show progress bars while training.
+
+        special_tokens (:obj:`List[Union[str, AddedToken]]`, `optional`):
+            A list of special tokens the model should know of.
+
+        limit_alphabet (:obj:`int`, `optional`):
+            The maximum different characters to keep in the alphabet.
+
+        initial_alphabet (:obj:`List[str]`, `optional`):
+            A list of characters to include in the initial alphabet, even
+            if not seen in the training dataset.
+            If the strings contain more than one character, only the first one
+            is kept.
+
+        continuing_subword_prefix (:obj:`str`, `optional`):
+            A prefix to be used for every subword that is not a beginning-of-word.
+
+        end_of_word_suffix (:obj:`str`, `optional`):
+            A suffix to be used for every subword that is a end-of-word.
+
+        max_token_length (:obj:`int`, `optional`):
+            Prevents creating tokens longer than the specified size.
+            This can help with reducing polluting your vocabulary with
+            highly repetitive tokens like `======` for wikipedia
+
+    """
+
+class UnigramTrainer(Trainer):
+    """
+    Trainer capable of training a Unigram model
+
+    Args:
+        vocab_size (:obj:`int`):
+            The size of the final vocabulary, including all tokens and alphabet.
+
+        show_progress (:obj:`bool`):
+            Whether to show progress bars while training.
+
+        special_tokens (:obj:`List[Union[str, AddedToken]]`):
+            A list of special tokens the model should know of.
+
+        initial_alphabet (:obj:`List[str]`):
+            A list of characters to include in the initial alphabet, even
+            if not seen in the training dataset.
+            If the strings contain more than one character, only the first one
+            is kept.
+
+        shrinking_factor (:obj:`float`):
+            The shrinking factor used at each step of the training to prune the
+            vocabulary.
+
+        unk_token (:obj:`str`):
+            The token used for out-of-vocabulary tokens.
+
+        max_piece_length (:obj:`int`):
+            The maximum length of a given token.
+
+        n_sub_iterations (:obj:`int`):
+            The number of iterations of the EM algorithm to perform before
+            pruning the vocabulary.
+    """
+    def __init__(
+        self,
+        vocab_size=8000,
+        show_progress=True,
+        special_tokens=[],
+        shrinking_factor=0.75,
+        unk_token=None,
+        max_piece_length=16,
+        n_sub_iterations=2,
+    ):
+        pass
+
+class WordLevelTrainer(Trainer):
+    """
+    Trainer capable of training a WorldLevel model
+
+    Args:
+        vocab_size (:obj:`int`, `optional`):
+            The size of the final vocabulary, including all tokens and alphabet.
+
+        min_frequency (:obj:`int`, `optional`):
+            The minimum frequency a pair should have in order to be merged.
+
+        show_progress (:obj:`bool`, `optional`):
+            Whether to show progress bars while training.
+
+        special_tokens (:obj:`List[Union[str, AddedToken]]`):
+            A list of special tokens the model should know of.
+    """
+
+class WordPieceTrainer(Trainer):
+    """
+    Trainer capable of training a WordPiece model
+
+    Args:
+        vocab_size (:obj:`int`, `optional`):
+            The size of the final vocabulary, including all tokens and alphabet.
+
+        min_frequency (:obj:`int`, `optional`):
+            The minimum frequency a pair should have in order to be merged.
+
+        show_progress (:obj:`bool`, `optional`):
+            Whether to show progress bars while training.
+
+        special_tokens (:obj:`List[Union[str, AddedToken]]`, `optional`):
+            A list of special tokens the model should know of.
+
+        limit_alphabet (:obj:`int`, `optional`):
+            The maximum different characters to keep in the alphabet.
+
+        initial_alphabet (:obj:`List[str]`, `optional`):
+            A list of characters to include in the initial alphabet, even
+            if not seen in the training dataset.
+            If the strings contain more than one character, only the first one
+            is kept.
+
+        continuing_subword_prefix (:obj:`str`, `optional`):
+            A prefix to be used for every subword that is not a beginning-of-word.
+
+        end_of_word_suffix (:obj:`str`, `optional`):
+            A suffix to be used for every subword that is a end-of-word.
+    """
+    def __init__(
+        self,
+        vocab_size=30000,
+        min_frequency=0,
+        show_progress=True,
+        special_tokens=[],
+        limit_alphabet=None,
+        initial_alphabet=[],
+        continuing_subword_prefix="##",
+        end_of_word_suffix=None,
+    ):
+        pass

wemm/lib/python3.10/site-packages/torchvision/__init__.py

@@ -0,0 +1,114 @@
+import os
+import warnings
+from modulefinder import Module
+
+import torch
+from torchvision import datasets, io, models, ops, transforms, utils
+
+from .extension import _HAS_OPS
+
+try:
+    from .version import __version__  # noqa: F401
+except ImportError:
+    pass
+
+
+# Check if torchvision is being imported within the root folder
+if not _HAS_OPS and os.path.dirname(os.path.realpath(__file__)) == os.path.join(
+    os.path.realpath(os.getcwd()), "torchvision"
+):
+    message = (
+        "You are importing torchvision within its own root folder ({}). "
+        "This is not expected to work and may give errors. Please exit the "
+        "torchvision project source and relaunch your python interpreter."
+    )
+    warnings.warn(message.format(os.getcwd()))
+
+_image_backend = "PIL"
+
+_video_backend = "pyav"
+
+
+def set_image_backend(backend):
+    """
+    Specifies the package used to load images.
+
+    Args:
+        backend (string): Name of the image backend. one of {'PIL', 'accimage'}.
+            The :mod:`accimage` package uses the Intel IPP library. It is
+            generally faster than PIL, but does not support as many operations.
+    """
+    global _image_backend
+    if backend not in ["PIL", "accimage"]:
+        raise ValueError(f"Invalid backend '{backend}'. Options are 'PIL' and 'accimage'")
+    _image_backend = backend
+
+
+def get_image_backend():
+    """
+    Gets the name of the package used to load images
+    """
+    return _image_backend
+
+
+def set_video_backend(backend):
+    """
+    Specifies the package used to decode videos.
+
+    Args:
+        backend (string): Name of the video backend. one of {'pyav', 'video_reader'}.
+            The :mod:`pyav` package uses the 3rd party PyAv library. It is a Pythonic
+            binding for the FFmpeg libraries.
+            The :mod:`video_reader` package includes a native C++ implementation on
+            top of FFMPEG libraries, and a python API of TorchScript custom operator.
+            It generally decodes faster than :mod:`pyav`, but is perhaps less robust.
+
+    .. note::
+        Building with FFMPEG is disabled by default in the latest `main`. If you want to use the 'video_reader'
+        backend, please compile torchvision from source.
+    """
+    global _video_backend
+    if backend not in ["pyav", "video_reader", "cuda"]:
+        raise ValueError("Invalid video backend '%s'. Options are 'pyav', 'video_reader' and 'cuda'" % backend)
+    if backend == "video_reader" and not io._HAS_VIDEO_OPT:
+        # TODO: better messages
+        message = "video_reader video backend is not available. Please compile torchvision from source and try again"
+        raise RuntimeError(message)
+    elif backend == "cuda" and not io._HAS_GPU_VIDEO_DECODER:
+        # TODO: better messages
+        message = "cuda video backend is not available."
+        raise RuntimeError(message)
+    else:
+        _video_backend = backend
+
+
+def get_video_backend():
+    """
+    Returns the currently active video backend used to decode videos.
+
+    Returns:
+        str: Name of the video backend. one of {'pyav', 'video_reader'}.
+    """
+
+    return _video_backend
+
+
+def _is_tracing():
+    return torch._C._get_tracing_state()
+
+
+_WARN_ABOUT_BETA_TRANSFORMS = True
+_BETA_TRANSFORMS_WARNING = (
+    "The torchvision.datapoints and torchvision.transforms.v2 namespaces are still Beta. "
+    "While we do not expect major breaking changes, some APIs may still change "
+    "according to user feedback. Please submit any feedback you may have in "
+    "this issue: https://github.com/pytorch/vision/issues/6753, and you can also "
+    "check out https://github.com/pytorch/vision/issues/7319 to learn more about "
+    "the APIs that we suspect might involve future changes. "
+    "You can silence this warning by calling torchvision.disable_beta_transforms_warning()."
+)
+
+
+def disable_beta_transforms_warning():
+    global _WARN_ABOUT_BETA_TRANSFORMS
+    _WARN_ABOUT_BETA_TRANSFORMS = False

wemm/lib/python3.10/site-packages/torchvision/_internally_replaced_utils.py

@@ -0,0 +1,58 @@
+import importlib.machinery
+import os
+
+from torch.hub import _get_torch_home
+
+
+_HOME = os.path.join(_get_torch_home(), "datasets", "vision")
+_USE_SHARDED_DATASETS = False
+
+
+def _download_file_from_remote_location(fpath: str, url: str) -> None:
+    pass
+
+
+def _is_remote_location_available() -> bool:
+    return False
+
+
+try:
+    from torch.hub import load_state_dict_from_url  # noqa: 401
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url  # noqa: 401
+
+
+def _get_extension_path(lib_name):
+
+    lib_dir = os.path.dirname(__file__)
+    if os.name == "nt":
+        # Register the main torchvision library location on the default DLL path
+        import ctypes
+        import sys
+
+        kernel32 = ctypes.WinDLL("kernel32.dll", use_last_error=True)
+        with_load_library_flags = hasattr(kernel32, "AddDllDirectory")
+        prev_error_mode = kernel32.SetErrorMode(0x0001)
+
+        if with_load_library_flags:
+            kernel32.AddDllDirectory.restype = ctypes.c_void_p
+
+        if sys.version_info >= (3, 8):
+            os.add_dll_directory(lib_dir)
+        elif with_load_library_flags:
+            res = kernel32.AddDllDirectory(lib_dir)
+            if res is None:
+                err = ctypes.WinError(ctypes.get_last_error())
+                err.strerror += f' Error adding "{lib_dir}" to the DLL directories.'
+                raise err
+
+        kernel32.SetErrorMode(prev_error_mode)
+
+    loader_details = (importlib.machinery.ExtensionFileLoader, importlib.machinery.EXTENSION_SUFFIXES)
+
+    extfinder = importlib.machinery.FileFinder(lib_dir, loader_details)
+    ext_specs = extfinder.find_spec(lib_name)
+    if ext_specs is None:
+        raise ImportError
+
+    return ext_specs.origin

wemm/lib/python3.10/site-packages/torchvision/_utils.py

@@ -0,0 +1,32 @@
+import enum
+from typing import Sequence, Type, TypeVar
+
+T = TypeVar("T", bound=enum.Enum)
+
+
+class StrEnumMeta(enum.EnumMeta):
+    auto = enum.auto
+
+    def from_str(self: Type[T], member: str) -> T:  # type: ignore[misc]
+        try:
+            return self[member]
+        except KeyError:
+            # TODO: use `add_suggestion` from torchvision.prototype.utils._internal to improve the error message as
+            #  soon as it is migrated.
+            raise ValueError(f"Unknown value '{member}' for {self.__name__}.") from None
+
+
+class StrEnum(enum.Enum, metaclass=StrEnumMeta):
+    pass
+
+
+def sequence_to_str(seq: Sequence, separate_last: str = "") -> str:
+    if not seq:
+        return ""
+    if len(seq) == 1:
+        return f"'{seq[0]}'"
+
+    head = "'" + "', '".join([str(item) for item in seq[:-1]]) + "'"
+    tail = f"{'' if separate_last and len(seq) == 2 else ','} {separate_last}'{seq[-1]}'"
+
+    return head + tail

wemm/lib/python3.10/site-packages/torchvision/datapoints/__init__.py

@@ -0,0 +1,12 @@
+from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
+
+from ._bounding_box import BoundingBox, BoundingBoxFormat
+from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
+from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
+from ._mask import Mask
+from ._video import _TensorVideoType, _TensorVideoTypeJIT, _VideoType, _VideoTypeJIT, Video
+
+if _WARN_ABOUT_BETA_TRANSFORMS:
+    import warnings
+
+    warnings.warn(_BETA_TRANSFORMS_WARNING)

wemm/lib/python3.10/site-packages/torchvision/datapoints/_datapoint.py

@@ -0,0 +1,259 @@
+from __future__ import annotations
+
+from types import ModuleType
+from typing import Any, Callable, List, Mapping, Optional, Sequence, Tuple, Type, TypeVar, Union
+
+import PIL.Image
+import torch
+from torch._C import DisableTorchFunctionSubclass
+from torch.types import _device, _dtype, _size
+from torchvision.transforms import InterpolationMode
+
+
+D = TypeVar("D", bound="Datapoint")
+_FillType = Union[int, float, Sequence[int], Sequence[float], None]
+_FillTypeJIT = Optional[List[float]]
+
+
+class Datapoint(torch.Tensor):
+    __F: Optional[ModuleType] = None
+
+    @staticmethod
+    def _to_tensor(
+        data: Any,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[Union[torch.device, str, int]] = None,
+        requires_grad: Optional[bool] = None,
+    ) -> torch.Tensor:
+        if requires_grad is None:
+            requires_grad = data.requires_grad if isinstance(data, torch.Tensor) else False
+        return torch.as_tensor(data, dtype=dtype, device=device).requires_grad_(requires_grad)
+
+    @classmethod
+    def wrap_like(cls: Type[D], other: D, tensor: torch.Tensor) -> D:
+        raise NotImplementedError
+
+    _NO_WRAPPING_EXCEPTIONS = {
+        torch.Tensor.clone: lambda cls, input, output: cls.wrap_like(input, output),
+        torch.Tensor.to: lambda cls, input, output: cls.wrap_like(input, output),
+        # We don't need to wrap the output of `Tensor.requires_grad_`, since it is an inplace operation and thus
+        # retains the type automatically
+        torch.Tensor.requires_grad_: lambda cls, input, output: output,
+    }
+
+    @classmethod
+    def __torch_function__(
+        cls,
+        func: Callable[..., torch.Tensor],
+        types: Tuple[Type[torch.Tensor], ...],
+        args: Sequence[Any] = (),
+        kwargs: Optional[Mapping[str, Any]] = None,
+    ) -> torch.Tensor:
+        """For general information about how the __torch_function__ protocol works,
+        see https://pytorch.org/docs/stable/notes/extending.html#extending-torch
+
+        TL;DR: Every time a PyTorch operator is called, it goes through the inputs and looks for the
+        ``__torch_function__`` method. If one is found, it is invoked with the operator as ``func`` as well as the
+        ``args`` and ``kwargs`` of the original call.
+
+        The default behavior of :class:`~torch.Tensor`'s is to retain a custom tensor type. For the :class:`Datapoint`
+        use case, this has two downsides:
+
+        1. Since some :class:`Datapoint`'s require metadata to be constructed, the default wrapping, i.e.
+           ``return cls(func(*args, **kwargs))``, will fail for them.
+        2. For most operations, there is no way of knowing if the input type is still valid for the output.
+
+        For these reasons, the automatic output wrapping is turned off for most operators. The only exceptions are
+        listed in :attr:`Datapoint._NO_WRAPPING_EXCEPTIONS`
+        """
+        # Since super().__torch_function__ has no hook to prevent the coercing of the output into the input type, we
+        # need to reimplement the functionality.
+
+        if not all(issubclass(cls, t) for t in types):
+            return NotImplemented
+
+        with DisableTorchFunctionSubclass():
+            output = func(*args, **kwargs or dict())
+
+            wrapper = cls._NO_WRAPPING_EXCEPTIONS.get(func)
+            # Apart from `func` needing to be an exception, we also require the primary operand, i.e. `args[0]`, to be
+            # an instance of the class that `__torch_function__` was invoked on. The __torch_function__ protocol will
+            # invoke this method on *all* types involved in the computation by walking the MRO upwards. For example,
+            # `torch.Tensor(...).to(datapoints.Image(...))` will invoke `datapoints.Image.__torch_function__` with
+            # `args = (torch.Tensor(), datapoints.Image())` first. Without this guard, the original `torch.Tensor` would
+            # be wrapped into a `datapoints.Image`.
+            if wrapper and isinstance(args[0], cls):
+                return wrapper(cls, args[0], output)
+
+            # Inplace `func`'s, canonically identified with a trailing underscore in their name like `.add_(...)`,
+            # will retain the input type. Thus, we need to unwrap here.
+            if isinstance(output, cls):
+                return output.as_subclass(torch.Tensor)
+
+            return output
+
+    def _make_repr(self, **kwargs: Any) -> str:
+        # This is a poor man's implementation of the proposal in https://github.com/pytorch/pytorch/issues/76532.
+        # If that ever gets implemented, remove this in favor of the solution on the `torch.Tensor` class.
+        extra_repr = ", ".join(f"{key}={value}" for key, value in kwargs.items())
+        return f"{super().__repr__()[:-1]}, {extra_repr})"
+
+    @property
+    def _F(self) -> ModuleType:
+        # This implements a lazy import of the functional to get around the cyclic import. This import is deferred
+        # until the first time we need reference to the functional module and it's shared across all instances of
+        # the class. This approach avoids the DataLoader issue described at
+        # https://github.com/pytorch/vision/pull/6476#discussion_r953588621
+        if Datapoint.__F is None:
+            from ..transforms.v2 import functional
+
+            Datapoint.__F = functional
+        return Datapoint.__F
+
+    # Add properties for common attributes like shape, dtype, device, ndim etc
+    # this way we return the result without passing into __torch_function__
+    @property
+    def shape(self) -> _size:  # type: ignore[override]
+        with DisableTorchFunctionSubclass():
+            return super().shape
+
+    @property
+    def ndim(self) -> int:  # type: ignore[override]
+        with DisableTorchFunctionSubclass():
+            return super().ndim
+
+    @property
+    def device(self, *args: Any, **kwargs: Any) -> _device:  # type: ignore[override]
+        with DisableTorchFunctionSubclass():
+            return super().device
+
+    @property
+    def dtype(self) -> _dtype:  # type: ignore[override]
+        with DisableTorchFunctionSubclass():
+            return super().dtype
+
+    def horizontal_flip(self) -> Datapoint:
+        return self
+
+    def vertical_flip(self) -> Datapoint:
+        return self
+
+    # TODO: We have to ignore override mypy error as there is torch.Tensor built-in deprecated op: Tensor.resize
+    # https://github.com/pytorch/pytorch/blob/e8727994eb7cdb2ab642749d6549bc497563aa06/torch/_tensor.py#L588-L593
+    def resize(  # type: ignore[override]
+        self,
+        size: List[int],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        max_size: Optional[int] = None,
+        antialias: Optional[Union[str, bool]] = "warn",
+    ) -> Datapoint:
+        return self
+
+    def crop(self, top: int, left: int, height: int, width: int) -> Datapoint:
+        return self
+
+    def center_crop(self, output_size: List[int]) -> Datapoint:
+        return self
+
+    def resized_crop(
+        self,
+        top: int,
+        left: int,
+        height: int,
+        width: int,
+        size: List[int],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        antialias: Optional[Union[str, bool]] = "warn",
+    ) -> Datapoint:
+        return self
+
+    def pad(
+        self,
+        padding: List[int],
+        fill: Optional[Union[int, float, List[float]]] = None,
+        padding_mode: str = "constant",
+    ) -> Datapoint:
+        return self
+
+    def rotate(
+        self,
+        angle: float,
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+        expand: bool = False,
+        center: Optional[List[float]] = None,
+        fill: _FillTypeJIT = None,
+    ) -> Datapoint:
+        return self
+
+    def affine(
+        self,
+        angle: Union[int, float],
+        translate: List[float],
+        scale: float,
+        shear: List[float],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+        fill: _FillTypeJIT = None,
+        center: Optional[List[float]] = None,
+    ) -> Datapoint:
+        return self
+
+    def perspective(
+        self,
+        startpoints: Optional[List[List[int]]],
+        endpoints: Optional[List[List[int]]],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        fill: _FillTypeJIT = None,
+        coefficients: Optional[List[float]] = None,
+    ) -> Datapoint:
+        return self
+
+    def elastic(
+        self,
+        displacement: torch.Tensor,
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        fill: _FillTypeJIT = None,
+    ) -> Datapoint:
+        return self
+
+    def rgb_to_grayscale(self, num_output_channels: int = 1) -> Datapoint:
+        return self
+
+    def adjust_brightness(self, brightness_factor: float) -> Datapoint:
+        return self
+
+    def adjust_saturation(self, saturation_factor: float) -> Datapoint:
+        return self
+
+    def adjust_contrast(self, contrast_factor: float) -> Datapoint:
+        return self
+
+    def adjust_sharpness(self, sharpness_factor: float) -> Datapoint:
+        return self
+
+    def adjust_hue(self, hue_factor: float) -> Datapoint:
+        return self
+
+    def adjust_gamma(self, gamma: float, gain: float = 1) -> Datapoint:
+        return self
+
+    def posterize(self, bits: int) -> Datapoint:
+        return self
+
+    def solarize(self, threshold: float) -> Datapoint:
+        return self
+
+    def autocontrast(self) -> Datapoint:
+        return self
+
+    def equalize(self) -> Datapoint:
+        return self
+
+    def invert(self) -> Datapoint:
+        return self
+
+    def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Datapoint:
+        return self
+
+
+_InputType = Union[torch.Tensor, PIL.Image.Image, Datapoint]
+_InputTypeJIT = torch.Tensor

wemm/lib/python3.10/site-packages/torchvision/datapoints/_image.py

@@ -0,0 +1,260 @@
+from __future__ import annotations
+
+from typing import Any, List, Optional, Tuple, Union
+
+import PIL.Image
+import torch
+from torchvision.transforms.functional import InterpolationMode
+
+from ._datapoint import _FillTypeJIT, Datapoint
+
+
+class Image(Datapoint):
+    """[BETA] :class:`torch.Tensor` subclass for images.
+
+    Args:
+        data (tensor-like, PIL.Image.Image): Any data that can be turned into a tensor with :func:`torch.as_tensor` as
+            well as PIL images.
+        dtype (torch.dtype, optional): Desired data type of the bounding box. If omitted, will be inferred from
+            ``data``.
+        device (torch.device, optional): Desired device of the bounding box. If omitted and ``data`` is a
+            :class:`torch.Tensor`, the device is taken from it. Otherwise, the bounding box is constructed on the CPU.
+        requires_grad (bool, optional): Whether autograd should record operations on the bounding box. If omitted and
+            ``data`` is a :class:`torch.Tensor`, the value is taken from it. Otherwise, defaults to ``False``.
+    """
+
+    @classmethod
+    def _wrap(cls, tensor: torch.Tensor) -> Image:
+        image = tensor.as_subclass(cls)
+        return image
+
+    def __new__(
+        cls,
+        data: Any,
+        *,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[Union[torch.device, str, int]] = None,
+        requires_grad: Optional[bool] = None,
+    ) -> Image:
+        if isinstance(data, PIL.Image.Image):
+            from torchvision.transforms.v2 import functional as F
+
+            data = F.pil_to_tensor(data)
+
+        tensor = cls._to_tensor(data, dtype=dtype, device=device, requires_grad=requires_grad)
+        if tensor.ndim < 2:
+            raise ValueError
+        elif tensor.ndim == 2:
+            tensor = tensor.unsqueeze(0)
+
+        return cls._wrap(tensor)
+
+    @classmethod
+    def wrap_like(cls, other: Image, tensor: torch.Tensor) -> Image:
+        return cls._wrap(tensor)
+
+    def __repr__(self, *, tensor_contents: Any = None) -> str:  # type: ignore[override]
+        return self._make_repr()
+
+    @property
+    def spatial_size(self) -> Tuple[int, int]:
+        return tuple(self.shape[-2:])  # type: ignore[return-value]
+
+    @property
+    def num_channels(self) -> int:
+        return self.shape[-3]
+
+    def horizontal_flip(self) -> Image:
+        output = self._F.horizontal_flip_image_tensor(self.as_subclass(torch.Tensor))
+        return Image.wrap_like(self, output)
+
+    def vertical_flip(self) -> Image:
+        output = self._F.vertical_flip_image_tensor(self.as_subclass(torch.Tensor))
+        return Image.wrap_like(self, output)
+
+    def resize(  # type: ignore[override]
+        self,
+        size: List[int],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        max_size: Optional[int] = None,
+        antialias: Optional[Union[str, bool]] = "warn",
+    ) -> Image:
+        output = self._F.resize_image_tensor(
+            self.as_subclass(torch.Tensor), size, interpolation=interpolation, max_size=max_size, antialias=antialias
+        )
+        return Image.wrap_like(self, output)
+
+    def crop(self, top: int, left: int, height: int, width: int) -> Image:
+        output = self._F.crop_image_tensor(self.as_subclass(torch.Tensor), top, left, height, width)
+        return Image.wrap_like(self, output)
+
+    def center_crop(self, output_size: List[int]) -> Image:
+        output = self._F.center_crop_image_tensor(self.as_subclass(torch.Tensor), output_size=output_size)
+        return Image.wrap_like(self, output)
+
+    def resized_crop(
+        self,
+        top: int,
+        left: int,
+        height: int,
+        width: int,
+        size: List[int],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        antialias: Optional[Union[str, bool]] = "warn",
+    ) -> Image:
+        output = self._F.resized_crop_image_tensor(
+            self.as_subclass(torch.Tensor),
+            top,
+            left,
+            height,
+            width,
+            size=list(size),
+            interpolation=interpolation,
+            antialias=antialias,
+        )
+        return Image.wrap_like(self, output)
+
+    def pad(
+        self,
+        padding: List[int],
+        fill: Optional[Union[int, float, List[float]]] = None,
+        padding_mode: str = "constant",
+    ) -> Image:
+        output = self._F.pad_image_tensor(self.as_subclass(torch.Tensor), padding, fill=fill, padding_mode=padding_mode)
+        return Image.wrap_like(self, output)
+
+    def rotate(
+        self,
+        angle: float,
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+        expand: bool = False,
+        center: Optional[List[float]] = None,
+        fill: _FillTypeJIT = None,
+    ) -> Image:
+        output = self._F.rotate_image_tensor(
+            self.as_subclass(torch.Tensor), angle, interpolation=interpolation, expand=expand, fill=fill, center=center
+        )
+        return Image.wrap_like(self, output)
+
+    def affine(
+        self,
+        angle: Union[int, float],
+        translate: List[float],
+        scale: float,
+        shear: List[float],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST,
+        fill: _FillTypeJIT = None,
+        center: Optional[List[float]] = None,
+    ) -> Image:
+        output = self._F.affine_image_tensor(
+            self.as_subclass(torch.Tensor),
+            angle,
+            translate=translate,
+            scale=scale,
+            shear=shear,
+            interpolation=interpolation,
+            fill=fill,
+            center=center,
+        )
+        return Image.wrap_like(self, output)
+
+    def perspective(
+        self,
+        startpoints: Optional[List[List[int]]],
+        endpoints: Optional[List[List[int]]],
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        fill: _FillTypeJIT = None,
+        coefficients: Optional[List[float]] = None,
+    ) -> Image:
+        output = self._F.perspective_image_tensor(
+            self.as_subclass(torch.Tensor),
+            startpoints,
+            endpoints,
+            interpolation=interpolation,
+            fill=fill,
+            coefficients=coefficients,
+        )
+        return Image.wrap_like(self, output)
+
+    def elastic(
+        self,
+        displacement: torch.Tensor,
+        interpolation: Union[InterpolationMode, int] = InterpolationMode.BILINEAR,
+        fill: _FillTypeJIT = None,
+    ) -> Image:
+        output = self._F.elastic_image_tensor(
+            self.as_subclass(torch.Tensor), displacement, interpolation=interpolation, fill=fill
+        )
+        return Image.wrap_like(self, output)
+
+    def rgb_to_grayscale(self, num_output_channels: int = 1) -> Image:
+        output = self._F.rgb_to_grayscale_image_tensor(
+            self.as_subclass(torch.Tensor), num_output_channels=num_output_channels
+        )
+        return Image.wrap_like(self, output)
+
+    def adjust_brightness(self, brightness_factor: float) -> Image:
+        output = self._F.adjust_brightness_image_tensor(
+            self.as_subclass(torch.Tensor), brightness_factor=brightness_factor
+        )
+        return Image.wrap_like(self, output)
+
+    def adjust_saturation(self, saturation_factor: float) -> Image:
+        output = self._F.adjust_saturation_image_tensor(
+            self.as_subclass(torch.Tensor), saturation_factor=saturation_factor
+        )
+        return Image.wrap_like(self, output)
+
+    def adjust_contrast(self, contrast_factor: float) -> Image:
+        output = self._F.adjust_contrast_image_tensor(self.as_subclass(torch.Tensor), contrast_factor=contrast_factor)
+        return Image.wrap_like(self, output)
+
+    def adjust_sharpness(self, sharpness_factor: float) -> Image:
+        output = self._F.adjust_sharpness_image_tensor(
+            self.as_subclass(torch.Tensor), sharpness_factor=sharpness_factor
+        )
+        return Image.wrap_like(self, output)
+
+    def adjust_hue(self, hue_factor: float) -> Image:
+        output = self._F.adjust_hue_image_tensor(self.as_subclass(torch.Tensor), hue_factor=hue_factor)
+        return Image.wrap_like(self, output)
+
+    def adjust_gamma(self, gamma: float, gain: float = 1) -> Image:
+        output = self._F.adjust_gamma_image_tensor(self.as_subclass(torch.Tensor), gamma=gamma, gain=gain)
+        return Image.wrap_like(self, output)
+
+    def posterize(self, bits: int) -> Image:
+        output = self._F.posterize_image_tensor(self.as_subclass(torch.Tensor), bits=bits)
+        return Image.wrap_like(self, output)
+
+    def solarize(self, threshold: float) -> Image:
+        output = self._F.solarize_image_tensor(self.as_subclass(torch.Tensor), threshold=threshold)
+        return Image.wrap_like(self, output)
+
+    def autocontrast(self) -> Image:
+        output = self._F.autocontrast_image_tensor(self.as_subclass(torch.Tensor))
+        return Image.wrap_like(self, output)
+
+    def equalize(self) -> Image:
+        output = self._F.equalize_image_tensor(self.as_subclass(torch.Tensor))
+        return Image.wrap_like(self, output)
+
+    def invert(self) -> Image:
+        output = self._F.invert_image_tensor(self.as_subclass(torch.Tensor))
+        return Image.wrap_like(self, output)
+
+    def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Image:
+        output = self._F.gaussian_blur_image_tensor(
+            self.as_subclass(torch.Tensor), kernel_size=kernel_size, sigma=sigma
+        )
+        return Image.wrap_like(self, output)
+
+    def normalize(self, mean: List[float], std: List[float], inplace: bool = False) -> Image:
+        output = self._F.normalize_image_tensor(self.as_subclass(torch.Tensor), mean=mean, std=std, inplace=inplace)
+        return Image.wrap_like(self, output)
+
+
+_ImageType = Union[torch.Tensor, PIL.Image.Image, Image]
+_ImageTypeJIT = torch.Tensor
+_TensorImageType = Union[torch.Tensor, Image]
+_TensorImageTypeJIT = torch.Tensor