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@@ -764,3 +764,7 @@ phi4/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_heuristic.so.9 filte
 phi4/lib/python3.10/site-packages/nvidia/cudnn/lib/libcudnn_ops.so.9 filter=lfs diff=lfs merge=lfs -text
 phi4/lib/python3.10/site-packages/nvidia/cuda_cupti/lib/libnvperf_target.so filter=lfs diff=lfs merge=lfs -text
 phi4/lib/python3.10/site-packages/nvidia/cuda_cupti/lib/libcupti.so.12 filter=lfs diff=lfs merge=lfs -text
+openflamingo/lib/python3.10/site-packages/fontTools/__pycache__/agl.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+openflamingo/lib/python3.10/site-packages/regex/_regex.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+phi4/bin/python filter=lfs diff=lfs merge=lfs -text
+phi4/lib/python3.10/site-packages/nvidia/cuda_cupti/lib/libpcsamplingutil.so filter=lfs diff=lfs merge=lfs -text

openflamingo/lib/python3.10/site-packages/blib2to3/Grammar.txt

@@ -0,0 +1,261 @@
+# Grammar for 2to3. This grammar supports Python 2.x and 3.x.
+
+# NOTE WELL: You should also follow all the steps listed at
+# https://devguide.python.org/grammar/
+
+# Start symbols for the grammar:
+#	file_input is a module or sequence of commands read from an input file;
+#	single_input is a single interactive statement;
+#	eval_input is the input for the eval() and input() functions.
+# NB: compound_stmt in single_input is followed by extra NEWLINE!
+file_input: (NEWLINE | stmt)* ENDMARKER
+single_input: NEWLINE | simple_stmt | compound_stmt NEWLINE
+eval_input: testlist NEWLINE* ENDMARKER
+
+typevar: NAME [':' expr] ['=' expr]
+paramspec: '**' NAME ['=' expr]
+typevartuple: '*' NAME ['=' (expr|star_expr)]
+typeparam: typevar | paramspec | typevartuple
+typeparams: '[' typeparam (',' typeparam)* [','] ']'
+
+decorator: '@' namedexpr_test NEWLINE
+decorators: decorator+
+decorated: decorators (classdef | funcdef | async_funcdef)
+async_funcdef: ASYNC funcdef
+funcdef: 'def' NAME [typeparams] parameters ['->' test] ':' suite
+parameters: '(' [typedargslist] ')'
+
+# The following definition for typedarglist is equivalent to this set of rules:
+#
+#     arguments = argument (',' argument)*
+#     argument = tfpdef ['=' test]
+#     kwargs = '**' tname [',']
+#     args = '*' [tname_star]
+#     kwonly_kwargs = (',' argument)* [',' [kwargs]]
+#     args_kwonly_kwargs = args kwonly_kwargs | kwargs
+#     poskeyword_args_kwonly_kwargs = arguments [',' [args_kwonly_kwargs]]
+#     typedargslist_no_posonly  = poskeyword_args_kwonly_kwargs | args_kwonly_kwargs
+#     typedarglist = arguments ',' '/' [',' [typedargslist_no_posonly]])|(typedargslist_no_posonly)"
+#
+# It needs to be fully expanded to allow our LL(1) parser to work on it.
+
+typedargslist: tfpdef ['=' test] (',' tfpdef ['=' test])* ',' '/' [
+                     ',' [((tfpdef ['=' test] ',')* ('*' [tname_star] (',' tname ['=' test])*
+                            [',' ['**' tname [',']]] | '**' tname [','])
+                     | tfpdef ['=' test] (',' tfpdef ['=' test])* [','])]
+                ] | ((tfpdef ['=' test] ',')* ('*' [tname_star] (',' tname ['=' test])*
+                     [',' ['**' tname [',']]] | '**' tname [','])
+                     | tfpdef ['=' test] (',' tfpdef ['=' test])* [','])
+
+tname: NAME [':' test]
+tname_star: NAME [':' (test|star_expr)]
+tfpdef: tname | '(' tfplist ')'
+tfplist: tfpdef (',' tfpdef)* [',']
+
+# The following definition for varargslist is equivalent to this set of rules:
+#
+#     arguments = argument (',' argument )*
+#     argument = vfpdef ['=' test]
+#     kwargs = '**' vname [',']
+#     args = '*' [vname]
+#     kwonly_kwargs = (',' argument )* [',' [kwargs]]
+#     args_kwonly_kwargs = args kwonly_kwargs | kwargs
+#     poskeyword_args_kwonly_kwargs = arguments [',' [args_kwonly_kwargs]]
+#     vararglist_no_posonly = poskeyword_args_kwonly_kwargs | args_kwonly_kwargs
+#     varargslist = arguments ',' '/' [','[(vararglist_no_posonly)]] | (vararglist_no_posonly)
+#
+# It needs to be fully expanded to allow our LL(1) parser to work on it.
+
+varargslist: vfpdef ['=' test ](',' vfpdef ['=' test])* ',' '/' [',' [
+                     ((vfpdef ['=' test] ',')* ('*' [vname] (',' vname ['=' test])*
+                            [',' ['**' vname [',']]] | '**' vname [','])
+                            | vfpdef ['=' test] (',' vfpdef ['=' test])* [','])
+                     ]] | ((vfpdef ['=' test] ',')*
+                     ('*' [vname] (',' vname ['=' test])*  [',' ['**' vname [',']]]| '**' vname [','])
+                     | vfpdef ['=' test] (',' vfpdef ['=' test])* [','])
+
+vname: NAME
+vfpdef: vname | '(' vfplist ')'
+vfplist: vfpdef (',' vfpdef)* [',']
+
+stmt: simple_stmt | compound_stmt
+simple_stmt: small_stmt (';' small_stmt)* [';'] NEWLINE
+small_stmt: (type_stmt | expr_stmt | del_stmt | pass_stmt | flow_stmt |
+             import_stmt | global_stmt | assert_stmt)
+expr_stmt: testlist_star_expr (annassign | augassign (yield_expr|testlist) |
+                     ('=' (yield_expr|testlist_star_expr))*)
+annassign: ':' test ['=' (yield_expr|testlist_star_expr)]
+testlist_star_expr: (test|star_expr) (',' (test|star_expr))* [',']
+augassign: ('+=' | '-=' | '*=' | '@=' | '/=' | '%=' | '&=' | '|=' | '^=' |
+            '<<=' | '>>=' | '**=' | '//=')
+# For normal and annotated assignments, additional restrictions enforced by the interpreter
+del_stmt: 'del' exprlist
+pass_stmt: 'pass'
+flow_stmt: break_stmt | continue_stmt | return_stmt | raise_stmt | yield_stmt
+break_stmt: 'break'
+continue_stmt: 'continue'
+return_stmt: 'return' [testlist_star_expr]
+yield_stmt: yield_expr
+raise_stmt: 'raise' [test ['from' test | ',' test [',' test]]]
+import_stmt: import_name | import_from
+import_name: 'import' dotted_as_names
+import_from: ('from' ('.'* dotted_name | '.'+)
+              'import' ('*' | '(' import_as_names ')' | import_as_names))
+import_as_name: NAME ['as' NAME]
+dotted_as_name: dotted_name ['as' NAME]
+import_as_names: import_as_name (',' import_as_name)* [',']
+dotted_as_names: dotted_as_name (',' dotted_as_name)*
+dotted_name: NAME ('.' NAME)*
+global_stmt: ('global' | 'nonlocal') NAME (',' NAME)*
+assert_stmt: 'assert' test [',' test]
+type_stmt: "type" NAME [typeparams] '=' test
+
+compound_stmt: if_stmt | while_stmt | for_stmt | try_stmt | with_stmt | funcdef | classdef | decorated | async_stmt | match_stmt
+async_stmt: ASYNC (funcdef | with_stmt | for_stmt)
+if_stmt: 'if' namedexpr_test ':' suite ('elif' namedexpr_test ':' suite)* ['else' ':' suite]
+while_stmt: 'while' namedexpr_test ':' suite ['else' ':' suite]
+for_stmt: 'for' exprlist 'in' testlist_star_expr ':' suite ['else' ':' suite]
+try_stmt: ('try' ':' suite
+           ((except_clause ':' suite)+
+	    ['else' ':' suite]
+	    ['finally' ':' suite] |
+	   'finally' ':' suite))
+with_stmt: 'with' asexpr_test (',' asexpr_test)*  ':' suite
+
+# NB compile.c makes sure that the default except clause is last
+except_clause: 'except' ['*'] [test [(',' | 'as') test]]
+suite: simple_stmt | NEWLINE INDENT stmt+ DEDENT
+
+# Backward compatibility cruft to support:
+# [ x for x in lambda: True, lambda: False if x() ]
+# even while also allowing:
+# lambda x: 5 if x else 2
+# (But not a mix of the two)
+testlist_safe: old_test [(',' old_test)+ [',']]
+old_test: or_test | old_lambdef
+old_lambdef: 'lambda' [varargslist] ':' old_test
+
+namedexpr_test: asexpr_test [':=' asexpr_test]
+
+# This is actually not a real rule, though since the parser is very
+# limited in terms of the strategy about match/case rules, we are inserting
+# a virtual case (<expr> as <expr>) as a valid expression. Unless a better
+# approach is thought, the only side effect of this seem to be just allowing
+# more stuff to be parser (which would fail on the ast).
+asexpr_test: test ['as' test]
+
+test: or_test ['if' or_test 'else' test] | lambdef
+or_test: and_test ('or' and_test)*
+and_test: not_test ('and' not_test)*
+not_test: 'not' not_test | comparison
+comparison: expr (comp_op expr)*
+comp_op: '<'|'>'|'=='|'>='|'<='|'<>'|'!='|'in'|'not' 'in'|'is'|'is' 'not'
+star_expr: '*' expr
+expr: xor_expr ('|' xor_expr)*
+xor_expr: and_expr ('^' and_expr)*
+and_expr: shift_expr ('&' shift_expr)*
+shift_expr: arith_expr (('<<'|'>>') arith_expr)*
+arith_expr: term (('+'|'-') term)*
+term: factor (('*'|'@'|'/'|'%'|'//') factor)*
+factor: ('+'|'-'|'~') factor | power
+power: [AWAIT] atom trailer* ['**' factor]
+atom: ('(' [yield_expr|testlist_gexp] ')' |
+       '[' [listmaker] ']' |
+       '{' [dictsetmaker] '}' |
+       '`' testlist1 '`' |
+       NAME | NUMBER | (STRING | fstring)+ | '.' '.' '.')
+listmaker: (namedexpr_test|star_expr) ( old_comp_for | (',' (namedexpr_test|star_expr))* [','] )
+testlist_gexp: (namedexpr_test|star_expr) ( old_comp_for | (',' (namedexpr_test|star_expr))* [','] )
+lambdef: 'lambda' [varargslist] ':' test
+trailer: '(' [arglist] ')' | '[' subscriptlist ']' | '.' NAME
+subscriptlist: (subscript|star_expr) (',' (subscript|star_expr))* [',']
+subscript: test [':=' test] | [test] ':' [test] [sliceop]
+sliceop: ':' [test]
+exprlist: (expr|star_expr) (',' (expr|star_expr))* [',']
+testlist: test (',' test)* [',']
+dictsetmaker: ( ((test ':' asexpr_test | '**' expr)
+                 (comp_for | (',' (test ':' asexpr_test | '**' expr))* [','])) |
+                ((test [':=' test] | star_expr)
+		 (comp_for | (',' (test [':=' test] | star_expr))* [','])) )
+
+classdef: 'class' NAME [typeparams] ['(' [arglist] ')'] ':' suite
+
+arglist: argument (',' argument)* [',']
+
+# "test '=' test" is really "keyword '=' test", but we have no such token.
+# These need to be in a single rule to avoid grammar that is ambiguous
+# to our LL(1) parser. Even though 'test' includes '*expr' in star_expr,
+# we explicitly match '*' here, too, to give it proper precedence.
+# Illegal combinations and orderings are blocked in ast.c:
+# multiple (test comp_for) arguments are blocked; keyword unpackings
+# that precede iterable unpackings are blocked; etc.
+argument: ( test [comp_for] |
+            test ':=' test [comp_for] |
+            test 'as' test |
+            test '=' asexpr_test |
+	    '**' test |
+            '*' test )
+
+comp_iter: comp_for | comp_if
+comp_for: [ASYNC] 'for' exprlist 'in' or_test [comp_iter]
+comp_if: 'if' old_test [comp_iter]
+
+# As noted above, testlist_safe extends the syntax allowed in list
+# comprehensions and generators. We can't use it indiscriminately in all
+# derivations using a comp_for-like pattern because the testlist_safe derivation
+# contains comma which clashes with trailing comma in arglist.
+#
+# This was an issue because the parser would not follow the correct derivation
+# when parsing syntactically valid Python code. Since testlist_safe was created
+# specifically to handle list comprehensions and generator expressions enclosed
+# with parentheses, it's safe to only use it in those. That avoids the issue; we
+# can parse code like set(x for x in [],).
+#
+# The syntax supported by this set of rules is not a valid Python 3 syntax,
+# hence the prefix "old".
+#
+# See https://bugs.python.org/issue27494
+old_comp_iter: old_comp_for | old_comp_if
+old_comp_for: [ASYNC] 'for' exprlist 'in' testlist_safe [old_comp_iter]
+old_comp_if: 'if' old_test [old_comp_iter]
+
+testlist1: test (',' test)*
+
+# not used in grammar, but may appear in "node" passed from Parser to Compiler
+encoding_decl: NAME
+
+yield_expr: 'yield' [yield_arg]
+yield_arg: 'from' test | testlist_star_expr
+
+
+# 3.10 match statement definition
+
+# PS: normally the grammar is much much more restricted, but
+# at this moment for not trying to bother much with encoding the
+# exact same DSL in a LL(1) parser, we will just accept an expression
+# and let the ast.parse() step of the safe mode to reject invalid
+# grammar.
+
+# The reason why it is more restricted is that, patterns are some
+# sort of a DSL (more advanced than our LHS on assignments, but
+# still in a very limited python subset). They are not really
+# expressions, but who cares. If we can parse them, that is enough
+# to reformat them.
+
+match_stmt: "match" subject_expr ':' NEWLINE INDENT case_block+ DEDENT
+
+# This is more permissive than the actual version. For example it
+# accepts `match *something:`, even though single-item starred expressions
+# are forbidden.
+subject_expr: (namedexpr_test|star_expr) (',' (namedexpr_test|star_expr))* [',']
+
+# cases
+case_block: "case" patterns [guard] ':' suite
+guard: 'if' namedexpr_test
+patterns: pattern (',' pattern)* [',']
+pattern: (expr|star_expr) ['as' expr]
+
+fstring: FSTRING_START fstring_middle* FSTRING_END
+fstring_middle: fstring_replacement_field | FSTRING_MIDDLE
+fstring_replacement_field: '{' (yield_expr | testlist_star_expr) ['='] [ "!" NAME ] [ ':' fstring_format_spec* ] '}'
+fstring_format_spec: FSTRING_MIDDLE | fstring_replacement_field

openflamingo/lib/python3.10/site-packages/blib2to3/PatternGrammar.txt

@@ -0,0 +1,28 @@
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+# A grammar to describe tree matching patterns.
+# Not shown here:
+# - 'TOKEN' stands for any token (leaf node)
+# - 'any' stands for any node (leaf or interior)
+# With 'any' we can still specify the sub-structure.
+
+# The start symbol is 'Matcher'.
+
+Matcher: Alternatives ENDMARKER
+
+Alternatives: Alternative ('|' Alternative)*
+
+Alternative: (Unit | NegatedUnit)+
+
+Unit: [NAME '='] ( STRING [Repeater]
+                 | NAME [Details] [Repeater]
+                 | '(' Alternatives ')' [Repeater]
+                 | '[' Alternatives ']'
+		 )
+
+NegatedUnit: 'not' (STRING | NAME [Details] | '(' Alternatives ')')
+
+Repeater: '*' | '+' | '{' NUMBER [',' NUMBER] '}'
+
+Details: '<' Alternatives '>'

openflamingo/lib/python3.10/site-packages/blib2to3/__init__.py

@@ -0,0 +1 @@
+# empty

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/__init__.py

@@ -0,0 +1,4 @@
+# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""The pgen2 package."""

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/conv.py

@@ -0,0 +1,256 @@
+# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+# mypy: ignore-errors
+
+"""Convert graminit.[ch] spit out by pgen to Python code.
+
+Pgen is the Python parser generator.  It is useful to quickly create a
+parser from a grammar file in Python's grammar notation.  But I don't
+want my parsers to be written in C (yet), so I'm translating the
+parsing tables to Python data structures and writing a Python parse
+engine.
+
+Note that the token numbers are constants determined by the standard
+Python tokenizer.  The standard token module defines these numbers and
+their names (the names are not used much).  The token numbers are
+hardcoded into the Python tokenizer and into pgen.  A Python
+implementation of the Python tokenizer is also available, in the
+standard tokenize module.
+
+On the other hand, symbol numbers (representing the grammar's
+non-terminals) are assigned by pgen based on the actual grammar
+input.
+
+Note: this module is pretty much obsolete; the pgen module generates
+equivalent grammar tables directly from the Grammar.txt input file
+without having to invoke the Python pgen C program.
+
+"""
+
+# Python imports
+import re
+
+# Local imports
+from pgen2 import grammar, token
+
+
+class Converter(grammar.Grammar):
+    """Grammar subclass that reads classic pgen output files.
+
+    The run() method reads the tables as produced by the pgen parser
+    generator, typically contained in two C files, graminit.h and
+    graminit.c.  The other methods are for internal use only.
+
+    See the base class for more documentation.
+
+    """
+
+    def run(self, graminit_h, graminit_c):
+        """Load the grammar tables from the text files written by pgen."""
+        self.parse_graminit_h(graminit_h)
+        self.parse_graminit_c(graminit_c)
+        self.finish_off()
+
+    def parse_graminit_h(self, filename):
+        """Parse the .h file written by pgen.  (Internal)
+
+        This file is a sequence of #define statements defining the
+        nonterminals of the grammar as numbers.  We build two tables
+        mapping the numbers to names and back.
+
+        """
+        try:
+            f = open(filename)
+        except OSError as err:
+            print(f"Can't open {filename}: {err}")
+            return False
+        self.symbol2number = {}
+        self.number2symbol = {}
+        lineno = 0
+        for line in f:
+            lineno += 1
+            mo = re.match(r"^#define\s+(\w+)\s+(\d+)$", line)
+            if not mo and line.strip():
+                print(f"{filename}({lineno}): can't parse {line.strip()}")
+            else:
+                symbol, number = mo.groups()
+                number = int(number)
+                assert symbol not in self.symbol2number
+                assert number not in self.number2symbol
+                self.symbol2number[symbol] = number
+                self.number2symbol[number] = symbol
+        return True
+
+    def parse_graminit_c(self, filename):
+        """Parse the .c file written by pgen.  (Internal)
+
+        The file looks as follows.  The first two lines are always this:
+
+        #include "pgenheaders.h"
+        #include "grammar.h"
+
+        After that come four blocks:
+
+        1) one or more state definitions
+        2) a table defining dfas
+        3) a table defining labels
+        4) a struct defining the grammar
+
+        A state definition has the following form:
+        - one or more arc arrays, each of the form:
+          static arc arcs_<n>_<m>[<k>] = {
+                  {<i>, <j>},
+                  ...
+          };
+        - followed by a state array, of the form:
+          static state states_<s>[<t>] = {
+                  {<k>, arcs_<n>_<m>},
+                  ...
+          };
+
+        """
+        try:
+            f = open(filename)
+        except OSError as err:
+            print(f"Can't open {filename}: {err}")
+            return False
+        # The code below essentially uses f's iterator-ness!
+        lineno = 0
+
+        # Expect the two #include lines
+        lineno, line = lineno + 1, next(f)
+        assert line == '#include "pgenheaders.h"\n', (lineno, line)
+        lineno, line = lineno + 1, next(f)
+        assert line == '#include "grammar.h"\n', (lineno, line)
+
+        # Parse the state definitions
+        lineno, line = lineno + 1, next(f)
+        allarcs = {}
+        states = []
+        while line.startswith("static arc "):
+            while line.startswith("static arc "):
+                mo = re.match(r"static arc arcs_(\d+)_(\d+)\[(\d+)\] = {$", line)
+                assert mo, (lineno, line)
+                n, m, k = list(map(int, mo.groups()))
+                arcs = []
+                for _ in range(k):
+                    lineno, line = lineno + 1, next(f)
+                    mo = re.match(r"\s+{(\d+), (\d+)},$", line)
+                    assert mo, (lineno, line)
+                    i, j = list(map(int, mo.groups()))
+                    arcs.append((i, j))
+                lineno, line = lineno + 1, next(f)
+                assert line == "};\n", (lineno, line)
+                allarcs[(n, m)] = arcs
+                lineno, line = lineno + 1, next(f)
+            mo = re.match(r"static state states_(\d+)\[(\d+)\] = {$", line)
+            assert mo, (lineno, line)
+            s, t = list(map(int, mo.groups()))
+            assert s == len(states), (lineno, line)
+            state = []
+            for _ in range(t):
+                lineno, line = lineno + 1, next(f)
+                mo = re.match(r"\s+{(\d+), arcs_(\d+)_(\d+)},$", line)
+                assert mo, (lineno, line)
+                k, n, m = list(map(int, mo.groups()))
+                arcs = allarcs[n, m]
+                assert k == len(arcs), (lineno, line)
+                state.append(arcs)
+            states.append(state)
+            lineno, line = lineno + 1, next(f)
+            assert line == "};\n", (lineno, line)
+            lineno, line = lineno + 1, next(f)
+        self.states = states
+
+        # Parse the dfas
+        dfas = {}
+        mo = re.match(r"static dfa dfas\[(\d+)\] = {$", line)
+        assert mo, (lineno, line)
+        ndfas = int(mo.group(1))
+        for i in range(ndfas):
+            lineno, line = lineno + 1, next(f)
+            mo = re.match(r'\s+{(\d+), "(\w+)", (\d+), (\d+), states_(\d+),$', line)
+            assert mo, (lineno, line)
+            symbol = mo.group(2)
+            number, x, y, z = list(map(int, mo.group(1, 3, 4, 5)))
+            assert self.symbol2number[symbol] == number, (lineno, line)
+            assert self.number2symbol[number] == symbol, (lineno, line)
+            assert x == 0, (lineno, line)
+            state = states[z]
+            assert y == len(state), (lineno, line)
+            lineno, line = lineno + 1, next(f)
+            mo = re.match(r'\s+("(?:\\\d\d\d)*")},$', line)
+            assert mo, (lineno, line)
+            first = {}
+            rawbitset = eval(mo.group(1))
+            for i, c in enumerate(rawbitset):
+                byte = ord(c)
+                for j in range(8):
+                    if byte & (1 << j):
+                        first[i * 8 + j] = 1
+            dfas[number] = (state, first)
+        lineno, line = lineno + 1, next(f)
+        assert line == "};\n", (lineno, line)
+        self.dfas = dfas
+
+        # Parse the labels
+        labels = []
+        lineno, line = lineno + 1, next(f)
+        mo = re.match(r"static label labels\[(\d+)\] = {$", line)
+        assert mo, (lineno, line)
+        nlabels = int(mo.group(1))
+        for i in range(nlabels):
+            lineno, line = lineno + 1, next(f)
+            mo = re.match(r'\s+{(\d+), (0|"\w+")},$', line)
+            assert mo, (lineno, line)
+            x, y = mo.groups()
+            x = int(x)
+            if y == "0":
+                y = None
+            else:
+                y = eval(y)
+            labels.append((x, y))
+        lineno, line = lineno + 1, next(f)
+        assert line == "};\n", (lineno, line)
+        self.labels = labels
+
+        # Parse the grammar struct
+        lineno, line = lineno + 1, next(f)
+        assert line == "grammar _PyParser_Grammar = {\n", (lineno, line)
+        lineno, line = lineno + 1, next(f)
+        mo = re.match(r"\s+(\d+),$", line)
+        assert mo, (lineno, line)
+        ndfas = int(mo.group(1))
+        assert ndfas == len(self.dfas)
+        lineno, line = lineno + 1, next(f)
+        assert line == "\tdfas,\n", (lineno, line)
+        lineno, line = lineno + 1, next(f)
+        mo = re.match(r"\s+{(\d+), labels},$", line)
+        assert mo, (lineno, line)
+        nlabels = int(mo.group(1))
+        assert nlabels == len(self.labels), (lineno, line)
+        lineno, line = lineno + 1, next(f)
+        mo = re.match(r"\s+(\d+)$", line)
+        assert mo, (lineno, line)
+        start = int(mo.group(1))
+        assert start in self.number2symbol, (lineno, line)
+        self.start = start
+        lineno, line = lineno + 1, next(f)
+        assert line == "};\n", (lineno, line)
+        try:
+            lineno, line = lineno + 1, next(f)
+        except StopIteration:
+            pass
+        else:
+            assert 0, (lineno, line)
+
+    def finish_off(self):
+        """Create additional useful structures.  (Internal)."""
+        self.keywords = {}  # map from keyword strings to arc labels
+        self.tokens = {}  # map from numeric token values to arc labels
+        for ilabel, (type, value) in enumerate(self.labels):
+            if type == token.NAME and value is not None:
+                self.keywords[value] = ilabel
+            elif value is None:
+                self.tokens[type] = ilabel

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/driver.py

@@ -0,0 +1,318 @@
+# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+# Modifications:
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Parser driver.
+
+This provides a high-level interface to parse a file into a syntax tree.
+
+"""
+
+__author__ = "Guido van Rossum <guido@python.org>"
+
+__all__ = ["Driver", "load_grammar"]
+
+# Python imports
+import io
+import logging
+import os
+import pkgutil
+import sys
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from logging import Logger
+from typing import IO, Any, Iterable, Iterator, Optional, Union, cast
+
+from blib2to3.pgen2.grammar import Grammar
+from blib2to3.pgen2.tokenize import GoodTokenInfo
+from blib2to3.pytree import NL
+
+# Pgen imports
+from . import grammar, parse, pgen, token, tokenize
+
+Path = Union[str, "os.PathLike[str]"]
+
+
+@dataclass
+class ReleaseRange:
+    start: int
+    end: Optional[int] = None
+    tokens: list[Any] = field(default_factory=list)
+
+    def lock(self) -> None:
+        total_eaten = len(self.tokens)
+        self.end = self.start + total_eaten
+
+
+class TokenProxy:
+    def __init__(self, generator: Any) -> None:
+        self._tokens = generator
+        self._counter = 0
+        self._release_ranges: list[ReleaseRange] = []
+
+    @contextmanager
+    def release(self) -> Iterator["TokenProxy"]:
+        release_range = ReleaseRange(self._counter)
+        self._release_ranges.append(release_range)
+        try:
+            yield self
+        finally:
+            # Lock the last release range to the final position that
+            # has been eaten.
+            release_range.lock()
+
+    def eat(self, point: int) -> Any:
+        eaten_tokens = self._release_ranges[-1].tokens
+        if point < len(eaten_tokens):
+            return eaten_tokens[point]
+        else:
+            while point >= len(eaten_tokens):
+                token = next(self._tokens)
+                eaten_tokens.append(token)
+            return token
+
+    def __iter__(self) -> "TokenProxy":
+        return self
+
+    def __next__(self) -> Any:
+        # If the current position is already compromised (looked up)
+        # return the eaten token, if not just go further on the given
+        # token producer.
+        for release_range in self._release_ranges:
+            assert release_range.end is not None
+
+            start, end = release_range.start, release_range.end
+            if start <= self._counter < end:
+                token = release_range.tokens[self._counter - start]
+                break
+        else:
+            token = next(self._tokens)
+        self._counter += 1
+        return token
+
+    def can_advance(self, to: int) -> bool:
+        # Try to eat, fail if it can't. The eat operation is cached
+        # so there won't be any additional cost of eating here
+        try:
+            self.eat(to)
+        except StopIteration:
+            return False
+        else:
+            return True
+
+
+class Driver:
+    def __init__(self, grammar: Grammar, logger: Optional[Logger] = None) -> None:
+        self.grammar = grammar
+        if logger is None:
+            logger = logging.getLogger(__name__)
+        self.logger = logger
+
+    def parse_tokens(self, tokens: Iterable[GoodTokenInfo], debug: bool = False) -> NL:
+        """Parse a series of tokens and return the syntax tree."""
+        # XXX Move the prefix computation into a wrapper around tokenize.
+        proxy = TokenProxy(tokens)
+
+        p = parse.Parser(self.grammar)
+        p.setup(proxy=proxy)
+
+        lineno = 1
+        column = 0
+        indent_columns: list[int] = []
+        type = value = start = end = line_text = None
+        prefix = ""
+
+        for quintuple in proxy:
+            type, value, start, end, line_text = quintuple
+            if start != (lineno, column):
+                assert (lineno, column) <= start, ((lineno, column), start)
+                s_lineno, s_column = start
+                if lineno < s_lineno:
+                    prefix += "\n" * (s_lineno - lineno)
+                    lineno = s_lineno
+                    column = 0
+                if column < s_column:
+                    prefix += line_text[column:s_column]
+                    column = s_column
+            if type in (tokenize.COMMENT, tokenize.NL):
+                prefix += value
+                lineno, column = end
+                if value.endswith("\n"):
+                    lineno += 1
+                    column = 0
+                continue
+            if type == token.OP:
+                type = grammar.opmap[value]
+            if debug:
+                assert type is not None
+                self.logger.debug(
+                    "%s %r (prefix=%r)", token.tok_name[type], value, prefix
+                )
+            if type == token.INDENT:
+                indent_columns.append(len(value))
+                _prefix = prefix + value
+                prefix = ""
+                value = ""
+            elif type == token.DEDENT:
+                _indent_col = indent_columns.pop()
+                prefix, _prefix = self._partially_consume_prefix(prefix, _indent_col)
+            if p.addtoken(cast(int, type), value, (prefix, start)):
+                if debug:
+                    self.logger.debug("Stop.")
+                break
+            prefix = ""
+            if type in {token.INDENT, token.DEDENT}:
+                prefix = _prefix
+            lineno, column = end
+            # FSTRING_MIDDLE is the only token that can end with a newline, and
+            # `end` will point to the next line. For that case, don't increment lineno.
+            if value.endswith("\n") and type != token.FSTRING_MIDDLE:
+                lineno += 1
+                column = 0
+        else:
+            # We never broke out -- EOF is too soon (how can this happen???)
+            assert start is not None
+            raise parse.ParseError("incomplete input", type, value, (prefix, start))
+        assert p.rootnode is not None
+        return p.rootnode
+
+    def parse_stream_raw(self, stream: IO[str], debug: bool = False) -> NL:
+        """Parse a stream and return the syntax tree."""
+        tokens = tokenize.generate_tokens(stream.readline, grammar=self.grammar)
+        return self.parse_tokens(tokens, debug)
+
+    def parse_stream(self, stream: IO[str], debug: bool = False) -> NL:
+        """Parse a stream and return the syntax tree."""
+        return self.parse_stream_raw(stream, debug)
+
+    def parse_file(
+        self, filename: Path, encoding: Optional[str] = None, debug: bool = False
+    ) -> NL:
+        """Parse a file and return the syntax tree."""
+        with open(filename, encoding=encoding) as stream:
+            return self.parse_stream(stream, debug)
+
+    def parse_string(self, text: str, debug: bool = False) -> NL:
+        """Parse a string and return the syntax tree."""
+        tokens = tokenize.generate_tokens(
+            io.StringIO(text).readline, grammar=self.grammar
+        )
+        return self.parse_tokens(tokens, debug)
+
+    def _partially_consume_prefix(self, prefix: str, column: int) -> tuple[str, str]:
+        lines: list[str] = []
+        current_line = ""
+        current_column = 0
+        wait_for_nl = False
+        for char in prefix:
+            current_line += char
+            if wait_for_nl:
+                if char == "\n":
+                    if current_line.strip() and current_column < column:
+                        res = "".join(lines)
+                        return res, prefix[len(res) :]
+
+                    lines.append(current_line)
+                    current_line = ""
+                    current_column = 0
+                    wait_for_nl = False
+            elif char in " \t":
+                current_column += 1
+            elif char == "\n":
+                # unexpected empty line
+                current_column = 0
+            elif char == "\f":
+                current_column = 0
+            else:
+                # indent is finished
+                wait_for_nl = True
+        return "".join(lines), current_line
+
+
+def _generate_pickle_name(gt: Path, cache_dir: Optional[Path] = None) -> str:
+    head, tail = os.path.splitext(gt)
+    if tail == ".txt":
+        tail = ""
+    name = head + tail + ".".join(map(str, sys.version_info)) + ".pickle"
+    if cache_dir:
+        return os.path.join(cache_dir, os.path.basename(name))
+    else:
+        return name
+
+
+def load_grammar(
+    gt: str = "Grammar.txt",
+    gp: Optional[str] = None,
+    save: bool = True,
+    force: bool = False,
+    logger: Optional[Logger] = None,
+) -> Grammar:
+    """Load the grammar (maybe from a pickle)."""
+    if logger is None:
+        logger = logging.getLogger(__name__)
+    gp = _generate_pickle_name(gt) if gp is None else gp
+    if force or not _newer(gp, gt):
+        g: grammar.Grammar = pgen.generate_grammar(gt)
+        if save:
+            try:
+                g.dump(gp)
+            except OSError:
+                # Ignore error, caching is not vital.
+                pass
+    else:
+        g = grammar.Grammar()
+        g.load(gp)
+    return g
+
+
+def _newer(a: str, b: str) -> bool:
+    """Inquire whether file a was written since file b."""
+    if not os.path.exists(a):
+        return False
+    if not os.path.exists(b):
+        return True
+    return os.path.getmtime(a) >= os.path.getmtime(b)
+
+
+def load_packaged_grammar(
+    package: str, grammar_source: str, cache_dir: Optional[Path] = None
+) -> grammar.Grammar:
+    """Normally, loads a pickled grammar by doing
+        pkgutil.get_data(package, pickled_grammar)
+    where *pickled_grammar* is computed from *grammar_source* by adding the
+    Python version and using a ``.pickle`` extension.
+
+    However, if *grammar_source* is an extant file, load_grammar(grammar_source)
+    is called instead. This facilitates using a packaged grammar file when needed
+    but preserves load_grammar's automatic regeneration behavior when possible.
+
+    """
+    if os.path.isfile(grammar_source):
+        gp = _generate_pickle_name(grammar_source, cache_dir) if cache_dir else None
+        return load_grammar(grammar_source, gp=gp)
+    pickled_name = _generate_pickle_name(os.path.basename(grammar_source), cache_dir)
+    data = pkgutil.get_data(package, pickled_name)
+    assert data is not None
+    g = grammar.Grammar()
+    g.loads(data)
+    return g
+
+
+def main(*args: str) -> bool:
+    """Main program, when run as a script: produce grammar pickle files.
+
+    Calls load_grammar for each argument, a path to a grammar text file.
+    """
+    if not args:
+        args = tuple(sys.argv[1:])
+    logging.basicConfig(level=logging.INFO, stream=sys.stdout, format="%(message)s")
+    for gt in args:
+        load_grammar(gt, save=True, force=True)
+    return True
+
+
+if __name__ == "__main__":
+    sys.exit(int(not main()))

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/grammar.py

@@ -0,0 +1,228 @@
+# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""This module defines the data structures used to represent a grammar.
+
+These are a bit arcane because they are derived from the data
+structures used by Python's 'pgen' parser generator.
+
+There's also a table here mapping operators to their names in the
+token module; the Python tokenize module reports all operators as the
+fallback token code OP, but the parser needs the actual token code.
+
+"""
+
+# Python imports
+import os
+import pickle
+import tempfile
+from typing import Any, Optional, TypeVar, Union
+
+# Local imports
+from . import token
+
+_P = TypeVar("_P", bound="Grammar")
+Label = tuple[int, Optional[str]]
+DFA = list[list[tuple[int, int]]]
+DFAS = tuple[DFA, dict[int, int]]
+Path = Union[str, "os.PathLike[str]"]
+
+
+class Grammar:
+    """Pgen parsing tables conversion class.
+
+    Once initialized, this class supplies the grammar tables for the
+    parsing engine implemented by parse.py.  The parsing engine
+    accesses the instance variables directly.  The class here does not
+    provide initialization of the tables; several subclasses exist to
+    do this (see the conv and pgen modules).
+
+    The load() method reads the tables from a pickle file, which is
+    much faster than the other ways offered by subclasses.  The pickle
+    file is written by calling dump() (after loading the grammar
+    tables using a subclass).  The report() method prints a readable
+    representation of the tables to stdout, for debugging.
+
+    The instance variables are as follows:
+
+    symbol2number -- a dict mapping symbol names to numbers.  Symbol
+                     numbers are always 256 or higher, to distinguish
+                     them from token numbers, which are between 0 and
+                     255 (inclusive).
+
+    number2symbol -- a dict mapping numbers to symbol names;
+                     these two are each other's inverse.
+
+    states        -- a list of DFAs, where each DFA is a list of
+                     states, each state is a list of arcs, and each
+                     arc is a (i, j) pair where i is a label and j is
+                     a state number.  The DFA number is the index into
+                     this list.  (This name is slightly confusing.)
+                     Final states are represented by a special arc of
+                     the form (0, j) where j is its own state number.
+
+    dfas          -- a dict mapping symbol numbers to (DFA, first)
+                     pairs, where DFA is an item from the states list
+                     above, and first is a set of tokens that can
+                     begin this grammar rule (represented by a dict
+                     whose values are always 1).
+
+    labels        -- a list of (x, y) pairs where x is either a token
+                     number or a symbol number, and y is either None
+                     or a string; the strings are keywords.  The label
+                     number is the index in this list; label numbers
+                     are used to mark state transitions (arcs) in the
+                     DFAs.
+
+    start         -- the number of the grammar's start symbol.
+
+    keywords      -- a dict mapping keyword strings to arc labels.
+
+    tokens        -- a dict mapping token numbers to arc labels.
+
+    """
+
+    def __init__(self) -> None:
+        self.symbol2number: dict[str, int] = {}
+        self.number2symbol: dict[int, str] = {}
+        self.states: list[DFA] = []
+        self.dfas: dict[int, DFAS] = {}
+        self.labels: list[Label] = [(0, "EMPTY")]
+        self.keywords: dict[str, int] = {}
+        self.soft_keywords: dict[str, int] = {}
+        self.tokens: dict[int, int] = {}
+        self.symbol2label: dict[str, int] = {}
+        self.version: tuple[int, int] = (0, 0)
+        self.start = 256
+        # Python 3.7+ parses async as a keyword, not an identifier
+        self.async_keywords = False
+
+    def dump(self, filename: Path) -> None:
+        """Dump the grammar tables to a pickle file."""
+
+        # mypyc generates objects that don't have a __dict__, but they
+        # do have __getstate__ methods that will return an equivalent
+        # dictionary
+        if hasattr(self, "__dict__"):
+            d = self.__dict__
+        else:
+            d = self.__getstate__()  # type: ignore
+
+        with tempfile.NamedTemporaryFile(
+            dir=os.path.dirname(filename), delete=False
+        ) as f:
+            pickle.dump(d, f, pickle.HIGHEST_PROTOCOL)
+        os.replace(f.name, filename)
+
+    def _update(self, attrs: dict[str, Any]) -> None:
+        for k, v in attrs.items():
+            setattr(self, k, v)
+
+    def load(self, filename: Path) -> None:
+        """Load the grammar tables from a pickle file."""
+        with open(filename, "rb") as f:
+            d = pickle.load(f)
+        self._update(d)
+
+    def loads(self, pkl: bytes) -> None:
+        """Load the grammar tables from a pickle bytes object."""
+        self._update(pickle.loads(pkl))
+
+    def copy(self: _P) -> _P:
+        """
+        Copy the grammar.
+        """
+        new = self.__class__()
+        for dict_attr in (
+            "symbol2number",
+            "number2symbol",
+            "dfas",
+            "keywords",
+            "soft_keywords",
+            "tokens",
+            "symbol2label",
+        ):
+            setattr(new, dict_attr, getattr(self, dict_attr).copy())
+        new.labels = self.labels[:]
+        new.states = self.states[:]
+        new.start = self.start
+        new.version = self.version
+        new.async_keywords = self.async_keywords
+        return new
+
+    def report(self) -> None:
+        """Dump the grammar tables to standard output, for debugging."""
+        from pprint import pprint
+
+        print("s2n")
+        pprint(self.symbol2number)
+        print("n2s")
+        pprint(self.number2symbol)
+        print("states")
+        pprint(self.states)
+        print("dfas")
+        pprint(self.dfas)
+        print("labels")
+        pprint(self.labels)
+        print("start", self.start)
+
+
+# Map from operator to number (since tokenize doesn't do this)
+
+opmap_raw = """
+( LPAR
+) RPAR
+[ LSQB
+] RSQB
+: COLON
+, COMMA
+; SEMI
++ PLUS
+- MINUS
+* STAR
+/ SLASH
+| VBAR
+& AMPER
+< LESS
+> GREATER
+= EQUAL
+. DOT
+% PERCENT
+` BACKQUOTE
+{ LBRACE
+} RBRACE
+@ AT
+@= ATEQUAL
+== EQEQUAL
+!= NOTEQUAL
+<> NOTEQUAL
+<= LESSEQUAL
+>= GREATEREQUAL
+~ TILDE
+^ CIRCUMFLEX
+<< LEFTSHIFT
+>> RIGHTSHIFT
+** DOUBLESTAR
++= PLUSEQUAL
+-= MINEQUAL
+*= STAREQUAL
+/= SLASHEQUAL
+%= PERCENTEQUAL
+&= AMPEREQUAL
+|= VBAREQUAL
+^= CIRCUMFLEXEQUAL
+<<= LEFTSHIFTEQUAL
+>>= RIGHTSHIFTEQUAL
+**= DOUBLESTAREQUAL
+// DOUBLESLASH
+//= DOUBLESLASHEQUAL
+-> RARROW
+:= COLONEQUAL
+! BANG
+"""
+
+opmap = {}
+for line in opmap_raw.splitlines():
+    if line:
+        op, name = line.split()
+        opmap[op] = getattr(token, name)

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/literals.py

@@ -0,0 +1,66 @@
+# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Safely evaluate Python string literals without using eval()."""
+
+import re
+from typing import Match
+
+simple_escapes: dict[str, str] = {
+    "a": "\a",
+    "b": "\b",
+    "f": "\f",
+    "n": "\n",
+    "r": "\r",
+    "t": "\t",
+    "v": "\v",
+    "'": "'",
+    '"': '"',
+    "\\": "\\",
+}
+
+
+def escape(m: Match[str]) -> str:
+    all, tail = m.group(0, 1)
+    assert all.startswith("\\")
+    esc = simple_escapes.get(tail)
+    if esc is not None:
+        return esc
+    if tail.startswith("x"):
+        hexes = tail[1:]
+        if len(hexes) < 2:
+            raise ValueError("invalid hex string escape ('\\%s')" % tail)
+        try:
+            i = int(hexes, 16)
+        except ValueError:
+            raise ValueError("invalid hex string escape ('\\%s')" % tail) from None
+    else:
+        try:
+            i = int(tail, 8)
+        except ValueError:
+            raise ValueError("invalid octal string escape ('\\%s')" % tail) from None
+    return chr(i)
+
+
+def evalString(s: str) -> str:
+    assert s.startswith("'") or s.startswith('"'), repr(s[:1])
+    q = s[0]
+    if s[:3] == q * 3:
+        q = q * 3
+    assert s.endswith(q), repr(s[-len(q) :])
+    assert len(s) >= 2 * len(q)
+    s = s[len(q) : -len(q)]
+    return re.sub(r"\\(\'|\"|\\|[abfnrtv]|x.{0,2}|[0-7]{1,3})", escape, s)
+
+
+def test() -> None:
+    for i in range(256):
+        c = chr(i)
+        s = repr(c)
+        e = evalString(s)
+        if e != c:
+            print(i, c, s, e)
+
+
+if __name__ == "__main__":
+    test()

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/parse.py

@@ -0,0 +1,399 @@
+# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Parser engine for the grammar tables generated by pgen.
+
+The grammar table must be loaded first.
+
+See Parser/parser.c in the Python distribution for additional info on
+how this parsing engine works.
+
+"""
+from contextlib import contextmanager
+from typing import TYPE_CHECKING, Any, Callable, Iterator, Optional, Union, cast
+
+from blib2to3.pgen2.grammar import Grammar
+from blib2to3.pytree import NL, Context, Leaf, Node, RawNode, convert
+
+# Local imports
+from . import grammar, token, tokenize
+
+if TYPE_CHECKING:
+    from blib2to3.pgen2.driver import TokenProxy
+
+
+Results = dict[str, NL]
+Convert = Callable[[Grammar, RawNode], Union[Node, Leaf]]
+DFA = list[list[tuple[int, int]]]
+DFAS = tuple[DFA, dict[int, int]]
+
+
+def lam_sub(grammar: Grammar, node: RawNode) -> NL:
+    assert node[3] is not None
+    return Node(type=node[0], children=node[3], context=node[2])
+
+
+# A placeholder node, used when parser is backtracking.
+DUMMY_NODE = (-1, None, None, None)
+
+
+def stack_copy(
+    stack: list[tuple[DFAS, int, RawNode]],
+) -> list[tuple[DFAS, int, RawNode]]:
+    """Nodeless stack copy."""
+    return [(dfa, label, DUMMY_NODE) for dfa, label, _ in stack]
+
+
+class Recorder:
+    def __init__(self, parser: "Parser", ilabels: list[int], context: Context) -> None:
+        self.parser = parser
+        self._ilabels = ilabels
+        self.context = context  # not really matter
+
+        self._dead_ilabels: set[int] = set()
+        self._start_point = self.parser.stack
+        self._points = {ilabel: stack_copy(self._start_point) for ilabel in ilabels}
+
+    @property
+    def ilabels(self) -> set[int]:
+        return self._dead_ilabels.symmetric_difference(self._ilabels)
+
+    @contextmanager
+    def switch_to(self, ilabel: int) -> Iterator[None]:
+        with self.backtrack():
+            self.parser.stack = self._points[ilabel]
+            try:
+                yield
+            except ParseError:
+                self._dead_ilabels.add(ilabel)
+            finally:
+                self.parser.stack = self._start_point
+
+    @contextmanager
+    def backtrack(self) -> Iterator[None]:
+        """
+        Use the node-level invariant ones for basic parsing operations (push/pop/shift).
+        These still will operate on the stack; but they won't create any new nodes, or
+        modify the contents of any other existing nodes.
+
+        This saves us a ton of time when we are backtracking, since we
+        want to restore to the initial state as quick as possible, which
+        can only be done by having as little mutatations as possible.
+        """
+        is_backtracking = self.parser.is_backtracking
+        try:
+            self.parser.is_backtracking = True
+            yield
+        finally:
+            self.parser.is_backtracking = is_backtracking
+
+    def add_token(self, tok_type: int, tok_val: str, raw: bool = False) -> None:
+        func: Callable[..., Any]
+        if raw:
+            func = self.parser._addtoken
+        else:
+            func = self.parser.addtoken
+
+        for ilabel in self.ilabels:
+            with self.switch_to(ilabel):
+                args = [tok_type, tok_val, self.context]
+                if raw:
+                    args.insert(0, ilabel)
+                func(*args)
+
+    def determine_route(
+        self, value: Optional[str] = None, force: bool = False
+    ) -> Optional[int]:
+        alive_ilabels = self.ilabels
+        if len(alive_ilabels) == 0:
+            *_, most_successful_ilabel = self._dead_ilabels
+            raise ParseError("bad input", most_successful_ilabel, value, self.context)
+
+        ilabel, *rest = alive_ilabels
+        if force or not rest:
+            return ilabel
+        else:
+            return None
+
+
+class ParseError(Exception):
+    """Exception to signal the parser is stuck."""
+
+    def __init__(
+        self, msg: str, type: Optional[int], value: Optional[str], context: Context
+    ) -> None:
+        Exception.__init__(
+            self, f"{msg}: type={type!r}, value={value!r}, context={context!r}"
+        )
+        self.msg = msg
+        self.type = type
+        self.value = value
+        self.context = context
+
+
+class Parser:
+    """Parser engine.
+
+    The proper usage sequence is:
+
+    p = Parser(grammar, [converter])  # create instance
+    p.setup([start])                  # prepare for parsing
+    <for each input token>:
+        if p.addtoken(...):           # parse a token; may raise ParseError
+            break
+    root = p.rootnode                 # root of abstract syntax tree
+
+    A Parser instance may be reused by calling setup() repeatedly.
+
+    A Parser instance contains state pertaining to the current token
+    sequence, and should not be used concurrently by different threads
+    to parse separate token sequences.
+
+    See driver.py for how to get input tokens by tokenizing a file or
+    string.
+
+    Parsing is complete when addtoken() returns True; the root of the
+    abstract syntax tree can then be retrieved from the rootnode
+    instance variable.  When a syntax error occurs, addtoken() raises
+    the ParseError exception.  There is no error recovery; the parser
+    cannot be used after a syntax error was reported (but it can be
+    reinitialized by calling setup()).
+
+    """
+
+    def __init__(self, grammar: Grammar, convert: Optional[Convert] = None) -> None:
+        """Constructor.
+
+        The grammar argument is a grammar.Grammar instance; see the
+        grammar module for more information.
+
+        The parser is not ready yet for parsing; you must call the
+        setup() method to get it started.
+
+        The optional convert argument is a function mapping concrete
+        syntax tree nodes to abstract syntax tree nodes.  If not
+        given, no conversion is done and the syntax tree produced is
+        the concrete syntax tree.  If given, it must be a function of
+        two arguments, the first being the grammar (a grammar.Grammar
+        instance), and the second being the concrete syntax tree node
+        to be converted.  The syntax tree is converted from the bottom
+        up.
+
+        **post-note: the convert argument is ignored since for Black's
+        usage, convert will always be blib2to3.pytree.convert. Allowing
+        this to be dynamic hurts mypyc's ability to use early binding.
+        These docs are left for historical and informational value.
+
+        A concrete syntax tree node is a (type, value, context, nodes)
+        tuple, where type is the node type (a token or symbol number),
+        value is None for symbols and a string for tokens, context is
+        None or an opaque value used for error reporting (typically a
+        (lineno, offset) pair), and nodes is a list of children for
+        symbols, and None for tokens.
+
+        An abstract syntax tree node may be anything; this is entirely
+        up to the converter function.
+
+        """
+        self.grammar = grammar
+        # See note in docstring above. TL;DR this is ignored.
+        self.convert = convert or lam_sub
+        self.is_backtracking = False
+        self.last_token: Optional[int] = None
+
+    def setup(self, proxy: "TokenProxy", start: Optional[int] = None) -> None:
+        """Prepare for parsing.
+
+        This *must* be called before starting to parse.
+
+        The optional argument is an alternative start symbol; it
+        defaults to the grammar's start symbol.
+
+        You can use a Parser instance to parse any number of programs;
+        each time you call setup() the parser is reset to an initial
+        state determined by the (implicit or explicit) start symbol.
+
+        """
+        if start is None:
+            start = self.grammar.start
+        # Each stack entry is a tuple: (dfa, state, node).
+        # A node is a tuple: (type, value, context, children),
+        # where children is a list of nodes or None, and context may be None.
+        newnode: RawNode = (start, None, None, [])
+        stackentry = (self.grammar.dfas[start], 0, newnode)
+        self.stack: list[tuple[DFAS, int, RawNode]] = [stackentry]
+        self.rootnode: Optional[NL] = None
+        self.used_names: set[str] = set()
+        self.proxy = proxy
+        self.last_token = None
+
+    def addtoken(self, type: int, value: str, context: Context) -> bool:
+        """Add a token; return True iff this is the end of the program."""
+        # Map from token to label
+        ilabels = self.classify(type, value, context)
+        assert len(ilabels) >= 1
+
+        # If we have only one state to advance, we'll directly
+        # take it as is.
+        if len(ilabels) == 1:
+            [ilabel] = ilabels
+            return self._addtoken(ilabel, type, value, context)
+
+        # If there are multiple states which we can advance (only
+        # happen under soft-keywords), then we will try all of them
+        # in parallel and as soon as one state can reach further than
+        # the rest, we'll choose that one. This is a pretty hacky
+        # and hopefully temporary algorithm.
+        #
+        # For a more detailed explanation, check out this post:
+        # https://tree.science/what-the-backtracking.html
+
+        with self.proxy.release() as proxy:
+            counter, force = 0, False
+            recorder = Recorder(self, ilabels, context)
+            recorder.add_token(type, value, raw=True)
+
+            next_token_value = value
+            while recorder.determine_route(next_token_value) is None:
+                if not proxy.can_advance(counter):
+                    force = True
+                    break
+
+                next_token_type, next_token_value, *_ = proxy.eat(counter)
+                if next_token_type in (tokenize.COMMENT, tokenize.NL):
+                    counter += 1
+                    continue
+
+                if next_token_type == tokenize.OP:
+                    next_token_type = grammar.opmap[next_token_value]
+
+                recorder.add_token(next_token_type, next_token_value)
+                counter += 1
+
+            ilabel = cast(int, recorder.determine_route(next_token_value, force=force))
+            assert ilabel is not None
+
+        return self._addtoken(ilabel, type, value, context)
+
+    def _addtoken(self, ilabel: int, type: int, value: str, context: Context) -> bool:
+        # Loop until the token is shifted; may raise exceptions
+        while True:
+            dfa, state, node = self.stack[-1]
+            states, first = dfa
+            arcs = states[state]
+            # Look for a state with this label
+            for i, newstate in arcs:
+                t = self.grammar.labels[i][0]
+                if t >= 256:
+                    # See if it's a symbol and if we're in its first set
+                    itsdfa = self.grammar.dfas[t]
+                    itsstates, itsfirst = itsdfa
+                    if ilabel in itsfirst:
+                        # Push a symbol
+                        self.push(t, itsdfa, newstate, context)
+                        break  # To continue the outer while loop
+
+                elif ilabel == i:
+                    # Look it up in the list of labels
+                    # Shift a token; we're done with it
+                    self.shift(type, value, newstate, context)
+                    # Pop while we are in an accept-only state
+                    state = newstate
+                    while states[state] == [(0, state)]:
+                        self.pop()
+                        if not self.stack:
+                            # Done parsing!
+                            return True
+                        dfa, state, node = self.stack[-1]
+                        states, first = dfa
+                    # Done with this token
+                    self.last_token = type
+                    return False
+
+            else:
+                if (0, state) in arcs:
+                    # An accepting state, pop it and try something else
+                    self.pop()
+                    if not self.stack:
+                        # Done parsing, but another token is input
+                        raise ParseError("too much input", type, value, context)
+                else:
+                    # No success finding a transition
+                    raise ParseError("bad input", type, value, context)
+
+    def classify(self, type: int, value: str, context: Context) -> list[int]:
+        """Turn a token into a label.  (Internal)
+
+        Depending on whether the value is a soft-keyword or not,
+        this function may return multiple labels to choose from."""
+        if type == token.NAME:
+            # Keep a listing of all used names
+            self.used_names.add(value)
+            # Check for reserved words
+            if value in self.grammar.keywords:
+                return [self.grammar.keywords[value]]
+            elif value in self.grammar.soft_keywords:
+                assert type in self.grammar.tokens
+                # Current soft keywords (match, case, type) can only appear at the
+                # beginning of a statement. So as a shortcut, don't try to treat them
+                # like keywords in any other context.
+                # ('_' is also a soft keyword in the real grammar, but for our grammar
+                # it's just an expression, so we don't need to treat it specially.)
+                if self.last_token not in (
+                    None,
+                    token.INDENT,
+                    token.DEDENT,
+                    token.NEWLINE,
+                    token.SEMI,
+                    token.COLON,
+                ):
+                    return [self.grammar.tokens[type]]
+                return [
+                    self.grammar.tokens[type],
+                    self.grammar.soft_keywords[value],
+                ]
+
+        ilabel = self.grammar.tokens.get(type)
+        if ilabel is None:
+            raise ParseError("bad token", type, value, context)
+        return [ilabel]
+
+    def shift(self, type: int, value: str, newstate: int, context: Context) -> None:
+        """Shift a token.  (Internal)"""
+        if self.is_backtracking:
+            dfa, state, _ = self.stack[-1]
+            self.stack[-1] = (dfa, newstate, DUMMY_NODE)
+        else:
+            dfa, state, node = self.stack[-1]
+            rawnode: RawNode = (type, value, context, None)
+            newnode = convert(self.grammar, rawnode)
+            assert node[-1] is not None
+            node[-1].append(newnode)
+            self.stack[-1] = (dfa, newstate, node)
+
+    def push(self, type: int, newdfa: DFAS, newstate: int, context: Context) -> None:
+        """Push a nonterminal.  (Internal)"""
+        if self.is_backtracking:
+            dfa, state, _ = self.stack[-1]
+            self.stack[-1] = (dfa, newstate, DUMMY_NODE)
+            self.stack.append((newdfa, 0, DUMMY_NODE))
+        else:
+            dfa, state, node = self.stack[-1]
+            newnode: RawNode = (type, None, context, [])
+            self.stack[-1] = (dfa, newstate, node)
+            self.stack.append((newdfa, 0, newnode))
+
+    def pop(self) -> None:
+        """Pop a nonterminal.  (Internal)"""
+        if self.is_backtracking:
+            self.stack.pop()
+        else:
+            popdfa, popstate, popnode = self.stack.pop()
+            newnode = convert(self.grammar, popnode)
+            if self.stack:
+                dfa, state, node = self.stack[-1]
+                assert node[-1] is not None
+                node[-1].append(newnode)
+            else:
+                self.rootnode = newnode
+                self.rootnode.used_names = self.used_names

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/pgen.py

@@ -0,0 +1,417 @@
+# Copyright 2004-2005 Elemental Security, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+import os
+from typing import IO, Any, Iterator, NoReturn, Optional, Sequence, Union
+
+from blib2to3.pgen2 import grammar, token, tokenize
+from blib2to3.pgen2.tokenize import GoodTokenInfo
+
+Path = Union[str, "os.PathLike[str]"]
+
+
+class PgenGrammar(grammar.Grammar):
+    pass
+
+
+class ParserGenerator:
+    filename: Path
+    stream: IO[str]
+    generator: Iterator[GoodTokenInfo]
+    first: dict[str, Optional[dict[str, int]]]
+
+    def __init__(self, filename: Path, stream: Optional[IO[str]] = None) -> None:
+        close_stream = None
+        if stream is None:
+            stream = open(filename, encoding="utf-8")
+            close_stream = stream.close
+        self.filename = filename
+        self.stream = stream
+        self.generator = tokenize.generate_tokens(stream.readline)
+        self.gettoken()  # Initialize lookahead
+        self.dfas, self.startsymbol = self.parse()
+        if close_stream is not None:
+            close_stream()
+        self.first = {}  # map from symbol name to set of tokens
+        self.addfirstsets()
+
+    def make_grammar(self) -> PgenGrammar:
+        c = PgenGrammar()
+        names = list(self.dfas.keys())
+        names.sort()
+        names.remove(self.startsymbol)
+        names.insert(0, self.startsymbol)
+        for name in names:
+            i = 256 + len(c.symbol2number)
+            c.symbol2number[name] = i
+            c.number2symbol[i] = name
+        for name in names:
+            dfa = self.dfas[name]
+            states = []
+            for state in dfa:
+                arcs = []
+                for label, next in sorted(state.arcs.items()):
+                    arcs.append((self.make_label(c, label), dfa.index(next)))
+                if state.isfinal:
+                    arcs.append((0, dfa.index(state)))
+                states.append(arcs)
+            c.states.append(states)
+            c.dfas[c.symbol2number[name]] = (states, self.make_first(c, name))
+        c.start = c.symbol2number[self.startsymbol]
+        return c
+
+    def make_first(self, c: PgenGrammar, name: str) -> dict[int, int]:
+        rawfirst = self.first[name]
+        assert rawfirst is not None
+        first = {}
+        for label in sorted(rawfirst):
+            ilabel = self.make_label(c, label)
+            ##assert ilabel not in first # XXX failed on <> ... !=
+            first[ilabel] = 1
+        return first
+
+    def make_label(self, c: PgenGrammar, label: str) -> int:
+        # XXX Maybe this should be a method on a subclass of converter?
+        ilabel = len(c.labels)
+        if label[0].isalpha():
+            # Either a symbol name or a named token
+            if label in c.symbol2number:
+                # A symbol name (a non-terminal)
+                if label in c.symbol2label:
+                    return c.symbol2label[label]
+                else:
+                    c.labels.append((c.symbol2number[label], None))
+                    c.symbol2label[label] = ilabel
+                    return ilabel
+            else:
+                # A named token (NAME, NUMBER, STRING)
+                itoken = getattr(token, label, None)
+                assert isinstance(itoken, int), label
+                assert itoken in token.tok_name, label
+                if itoken in c.tokens:
+                    return c.tokens[itoken]
+                else:
+                    c.labels.append((itoken, None))
+                    c.tokens[itoken] = ilabel
+                    return ilabel
+        else:
+            # Either a keyword or an operator
+            assert label[0] in ('"', "'"), label
+            value = eval(label)
+            if value[0].isalpha():
+                if label[0] == '"':
+                    keywords = c.soft_keywords
+                else:
+                    keywords = c.keywords
+
+                # A keyword
+                if value in keywords:
+                    return keywords[value]
+                else:
+                    c.labels.append((token.NAME, value))
+                    keywords[value] = ilabel
+                    return ilabel
+            else:
+                # An operator (any non-numeric token)
+                itoken = grammar.opmap[value]  # Fails if unknown token
+                if itoken in c.tokens:
+                    return c.tokens[itoken]
+                else:
+                    c.labels.append((itoken, None))
+                    c.tokens[itoken] = ilabel
+                    return ilabel
+
+    def addfirstsets(self) -> None:
+        names = list(self.dfas.keys())
+        names.sort()
+        for name in names:
+            if name not in self.first:
+                self.calcfirst(name)
+            # print name, self.first[name].keys()
+
+    def calcfirst(self, name: str) -> None:
+        dfa = self.dfas[name]
+        self.first[name] = None  # dummy to detect left recursion
+        state = dfa[0]
+        totalset: dict[str, int] = {}
+        overlapcheck = {}
+        for label in state.arcs:
+            if label in self.dfas:
+                if label in self.first:
+                    fset = self.first[label]
+                    if fset is None:
+                        raise ValueError("recursion for rule %r" % name)
+                else:
+                    self.calcfirst(label)
+                    fset = self.first[label]
+                    assert fset is not None
+                totalset.update(fset)
+                overlapcheck[label] = fset
+            else:
+                totalset[label] = 1
+                overlapcheck[label] = {label: 1}
+        inverse: dict[str, str] = {}
+        for label, itsfirst in overlapcheck.items():
+            for symbol in itsfirst:
+                if symbol in inverse:
+                    raise ValueError(
+                        "rule %s is ambiguous; %s is in the first sets of %s as well"
+                        " as %s" % (name, symbol, label, inverse[symbol])
+                    )
+                inverse[symbol] = label
+        self.first[name] = totalset
+
+    def parse(self) -> tuple[dict[str, list["DFAState"]], str]:
+        dfas = {}
+        startsymbol: Optional[str] = None
+        # MSTART: (NEWLINE | RULE)* ENDMARKER
+        while self.type != token.ENDMARKER:
+            while self.type == token.NEWLINE:
+                self.gettoken()
+            # RULE: NAME ':' RHS NEWLINE
+            name = self.expect(token.NAME)
+            self.expect(token.OP, ":")
+            a, z = self.parse_rhs()
+            self.expect(token.NEWLINE)
+            # self.dump_nfa(name, a, z)
+            dfa = self.make_dfa(a, z)
+            # self.dump_dfa(name, dfa)
+            # oldlen = len(dfa)
+            self.simplify_dfa(dfa)
+            # newlen = len(dfa)
+            dfas[name] = dfa
+            # print name, oldlen, newlen
+            if startsymbol is None:
+                startsymbol = name
+        assert startsymbol is not None
+        return dfas, startsymbol
+
+    def make_dfa(self, start: "NFAState", finish: "NFAState") -> list["DFAState"]:
+        # To turn an NFA into a DFA, we define the states of the DFA
+        # to correspond to *sets* of states of the NFA.  Then do some
+        # state reduction.  Let's represent sets as dicts with 1 for
+        # values.
+        assert isinstance(start, NFAState)
+        assert isinstance(finish, NFAState)
+
+        def closure(state: NFAState) -> dict[NFAState, int]:
+            base: dict[NFAState, int] = {}
+            addclosure(state, base)
+            return base
+
+        def addclosure(state: NFAState, base: dict[NFAState, int]) -> None:
+            assert isinstance(state, NFAState)
+            if state in base:
+                return
+            base[state] = 1
+            for label, next in state.arcs:
+                if label is None:
+                    addclosure(next, base)
+
+        states = [DFAState(closure(start), finish)]
+        for state in states:  # NB states grows while we're iterating
+            arcs: dict[str, dict[NFAState, int]] = {}
+            for nfastate in state.nfaset:
+                for label, next in nfastate.arcs:
+                    if label is not None:
+                        addclosure(next, arcs.setdefault(label, {}))
+            for label, nfaset in sorted(arcs.items()):
+                for st in states:
+                    if st.nfaset == nfaset:
+                        break
+                else:
+                    st = DFAState(nfaset, finish)
+                    states.append(st)
+                state.addarc(st, label)
+        return states  # List of DFAState instances; first one is start
+
+    def dump_nfa(self, name: str, start: "NFAState", finish: "NFAState") -> None:
+        print("Dump of NFA for", name)
+        todo = [start]
+        for i, state in enumerate(todo):
+            print("  State", i, state is finish and "(final)" or "")
+            for label, next in state.arcs:
+                if next in todo:
+                    j = todo.index(next)
+                else:
+                    j = len(todo)
+                    todo.append(next)
+                if label is None:
+                    print("    -> %d" % j)
+                else:
+                    print("    %s -> %d" % (label, j))
+
+    def dump_dfa(self, name: str, dfa: Sequence["DFAState"]) -> None:
+        print("Dump of DFA for", name)
+        for i, state in enumerate(dfa):
+            print("  State", i, state.isfinal and "(final)" or "")
+            for label, next in sorted(state.arcs.items()):
+                print("    %s -> %d" % (label, dfa.index(next)))
+
+    def simplify_dfa(self, dfa: list["DFAState"]) -> None:
+        # This is not theoretically optimal, but works well enough.
+        # Algorithm: repeatedly look for two states that have the same
+        # set of arcs (same labels pointing to the same nodes) and
+        # unify them, until things stop changing.
+
+        # dfa is a list of DFAState instances
+        changes = True
+        while changes:
+            changes = False
+            for i, state_i in enumerate(dfa):
+                for j in range(i + 1, len(dfa)):
+                    state_j = dfa[j]
+                    if state_i == state_j:
+                        # print "  unify", i, j
+                        del dfa[j]
+                        for state in dfa:
+                            state.unifystate(state_j, state_i)
+                        changes = True
+                        break
+
+    def parse_rhs(self) -> tuple["NFAState", "NFAState"]:
+        # RHS: ALT ('|' ALT)*
+        a, z = self.parse_alt()
+        if self.value != "|":
+            return a, z
+        else:
+            aa = NFAState()
+            zz = NFAState()
+            aa.addarc(a)
+            z.addarc(zz)
+            while self.value == "|":
+                self.gettoken()
+                a, z = self.parse_alt()
+                aa.addarc(a)
+                z.addarc(zz)
+            return aa, zz
+
+    def parse_alt(self) -> tuple["NFAState", "NFAState"]:
+        # ALT: ITEM+
+        a, b = self.parse_item()
+        while self.value in ("(", "[") or self.type in (token.NAME, token.STRING):
+            c, d = self.parse_item()
+            b.addarc(c)
+            b = d
+        return a, b
+
+    def parse_item(self) -> tuple["NFAState", "NFAState"]:
+        # ITEM: '[' RHS ']' | ATOM ['+' | '*']
+        if self.value == "[":
+            self.gettoken()
+            a, z = self.parse_rhs()
+            self.expect(token.OP, "]")
+            a.addarc(z)
+            return a, z
+        else:
+            a, z = self.parse_atom()
+            value = self.value
+            if value not in ("+", "*"):
+                return a, z
+            self.gettoken()
+            z.addarc(a)
+            if value == "+":
+                return a, z
+            else:
+                return a, a
+
+    def parse_atom(self) -> tuple["NFAState", "NFAState"]:
+        # ATOM: '(' RHS ')' | NAME | STRING
+        if self.value == "(":
+            self.gettoken()
+            a, z = self.parse_rhs()
+            self.expect(token.OP, ")")
+            return a, z
+        elif self.type in (token.NAME, token.STRING):
+            a = NFAState()
+            z = NFAState()
+            a.addarc(z, self.value)
+            self.gettoken()
+            return a, z
+        else:
+            self.raise_error(
+                "expected (...) or NAME or STRING, got %s/%s", self.type, self.value
+            )
+            raise AssertionError
+
+    def expect(self, type: int, value: Optional[Any] = None) -> str:
+        if self.type != type or (value is not None and self.value != value):
+            self.raise_error(
+                "expected %s/%s, got %s/%s", type, value, self.type, self.value
+            )
+        value = self.value
+        self.gettoken()
+        return value
+
+    def gettoken(self) -> None:
+        tup = next(self.generator)
+        while tup[0] in (tokenize.COMMENT, tokenize.NL):
+            tup = next(self.generator)
+        self.type, self.value, self.begin, self.end, self.line = tup
+        # print token.tok_name[self.type], repr(self.value)
+
+    def raise_error(self, msg: str, *args: Any) -> NoReturn:
+        if args:
+            try:
+                msg = msg % args
+            except Exception:
+                msg = " ".join([msg] + list(map(str, args)))
+        raise SyntaxError(msg, (self.filename, self.end[0], self.end[1], self.line))
+
+
+class NFAState:
+    arcs: list[tuple[Optional[str], "NFAState"]]
+
+    def __init__(self) -> None:
+        self.arcs = []  # list of (label, NFAState) pairs
+
+    def addarc(self, next: "NFAState", label: Optional[str] = None) -> None:
+        assert label is None or isinstance(label, str)
+        assert isinstance(next, NFAState)
+        self.arcs.append((label, next))
+
+
+class DFAState:
+    nfaset: dict[NFAState, Any]
+    isfinal: bool
+    arcs: dict[str, "DFAState"]
+
+    def __init__(self, nfaset: dict[NFAState, Any], final: NFAState) -> None:
+        assert isinstance(nfaset, dict)
+        assert isinstance(next(iter(nfaset)), NFAState)
+        assert isinstance(final, NFAState)
+        self.nfaset = nfaset
+        self.isfinal = final in nfaset
+        self.arcs = {}  # map from label to DFAState
+
+    def addarc(self, next: "DFAState", label: str) -> None:
+        assert isinstance(label, str)
+        assert label not in self.arcs
+        assert isinstance(next, DFAState)
+        self.arcs[label] = next
+
+    def unifystate(self, old: "DFAState", new: "DFAState") -> None:
+        for label, next in self.arcs.items():
+            if next is old:
+                self.arcs[label] = new
+
+    def __eq__(self, other: Any) -> bool:
+        # Equality test -- ignore the nfaset instance variable
+        assert isinstance(other, DFAState)
+        if self.isfinal != other.isfinal:
+            return False
+        # Can't just return self.arcs == other.arcs, because that
+        # would invoke this method recursively, with cycles...
+        if len(self.arcs) != len(other.arcs):
+            return False
+        for label, next in self.arcs.items():
+            if next is not other.arcs.get(label):
+                return False
+        return True
+
+    __hash__: Any = None  # For Py3 compatibility.
+
+
+def generate_grammar(filename: Path = "Grammar.txt") -> PgenGrammar:
+    p = ParserGenerator(filename)
+    return p.make_grammar()

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/token.py

@@ -0,0 +1,92 @@
+"""Token constants (from "token.h")."""
+
+from typing import Final
+
+#  Taken from Python (r53757) and modified to include some tokens
+#   originally monkeypatched in by pgen2.tokenize
+
+# --start constants--
+ENDMARKER: Final = 0
+NAME: Final = 1
+NUMBER: Final = 2
+STRING: Final = 3
+NEWLINE: Final = 4
+INDENT: Final = 5
+DEDENT: Final = 6
+LPAR: Final = 7
+RPAR: Final = 8
+LSQB: Final = 9
+RSQB: Final = 10
+COLON: Final = 11
+COMMA: Final = 12
+SEMI: Final = 13
+PLUS: Final = 14
+MINUS: Final = 15
+STAR: Final = 16
+SLASH: Final = 17
+VBAR: Final = 18
+AMPER: Final = 19
+LESS: Final = 20
+GREATER: Final = 21
+EQUAL: Final = 22
+DOT: Final = 23
+PERCENT: Final = 24
+BACKQUOTE: Final = 25
+LBRACE: Final = 26
+RBRACE: Final = 27
+EQEQUAL: Final = 28
+NOTEQUAL: Final = 29
+LESSEQUAL: Final = 30
+GREATEREQUAL: Final = 31
+TILDE: Final = 32
+CIRCUMFLEX: Final = 33
+LEFTSHIFT: Final = 34
+RIGHTSHIFT: Final = 35
+DOUBLESTAR: Final = 36
+PLUSEQUAL: Final = 37
+MINEQUAL: Final = 38
+STAREQUAL: Final = 39
+SLASHEQUAL: Final = 40
+PERCENTEQUAL: Final = 41
+AMPEREQUAL: Final = 42
+VBAREQUAL: Final = 43
+CIRCUMFLEXEQUAL: Final = 44
+LEFTSHIFTEQUAL: Final = 45
+RIGHTSHIFTEQUAL: Final = 46
+DOUBLESTAREQUAL: Final = 47
+DOUBLESLASH: Final = 48
+DOUBLESLASHEQUAL: Final = 49
+AT: Final = 50
+ATEQUAL: Final = 51
+OP: Final = 52
+COMMENT: Final = 53
+NL: Final = 54
+RARROW: Final = 55
+AWAIT: Final = 56
+ASYNC: Final = 57
+ERRORTOKEN: Final = 58
+COLONEQUAL: Final = 59
+FSTRING_START: Final = 60
+FSTRING_MIDDLE: Final = 61
+FSTRING_END: Final = 62
+BANG: Final = 63
+N_TOKENS: Final = 64
+NT_OFFSET: Final = 256
+# --end constants--
+
+tok_name: Final[dict[int, str]] = {}
+for _name, _value in list(globals().items()):
+    if type(_value) is int:
+        tok_name[_value] = _name
+
+
+def ISTERMINAL(x: int) -> bool:
+    return x < NT_OFFSET
+
+
+def ISNONTERMINAL(x: int) -> bool:
+    return x >= NT_OFFSET
+
+
+def ISEOF(x: int) -> bool:
+    return x == ENDMARKER

openflamingo/lib/python3.10/site-packages/blib2to3/pgen2/tokenize.py

@@ -0,0 +1,1112 @@
+# Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006 Python Software Foundation.
+# All rights reserved.
+
+# mypy: allow-untyped-defs, allow-untyped-calls
+
+"""Tokenization help for Python programs.
+
+generate_tokens(readline) is a generator that breaks a stream of
+text into Python tokens.  It accepts a readline-like method which is called
+repeatedly to get the next line of input (or "" for EOF).  It generates
+5-tuples with these members:
+
+    the token type (see token.py)
+    the token (a string)
+    the starting (row, column) indices of the token (a 2-tuple of ints)
+    the ending (row, column) indices of the token (a 2-tuple of ints)
+    the original line (string)
+
+It is designed to match the working of the Python tokenizer exactly, except
+that it produces COMMENT tokens for comments and gives type OP for all
+operators
+
+Older entry points
+    tokenize_loop(readline, tokeneater)
+    tokenize(readline, tokeneater=printtoken)
+are the same, except instead of generating tokens, tokeneater is a callback
+function to which the 5 fields described above are passed as 5 arguments,
+each time a new token is found."""
+
+import builtins
+import sys
+from typing import Callable, Final, Iterable, Iterator, Optional, Pattern, Union
+
+from blib2to3.pgen2.grammar import Grammar
+from blib2to3.pgen2.token import (
+    ASYNC,
+    AWAIT,
+    COMMENT,
+    DEDENT,
+    ENDMARKER,
+    ERRORTOKEN,
+    FSTRING_END,
+    FSTRING_MIDDLE,
+    FSTRING_START,
+    INDENT,
+    LBRACE,
+    NAME,
+    NEWLINE,
+    NL,
+    NUMBER,
+    OP,
+    RBRACE,
+    STRING,
+    tok_name,
+)
+
+__author__ = "Ka-Ping Yee <ping@lfw.org>"
+__credits__ = "GvR, ESR, Tim Peters, Thomas Wouters, Fred Drake, Skip Montanaro"
+
+import re
+from codecs import BOM_UTF8, lookup
+
+from . import token
+
+__all__ = [x for x in dir(token) if x[0] != "_"] + [
+    "tokenize",
+    "generate_tokens",
+    "untokenize",
+]
+del token
+
+
+def group(*choices: str) -> str:
+    return "(" + "|".join(choices) + ")"
+
+
+def any(*choices: str) -> str:
+    return group(*choices) + "*"
+
+
+def maybe(*choices: str) -> str:
+    return group(*choices) + "?"
+
+
+def _combinations(*l: str) -> set[str]:
+    return {x + y for x in l for y in l + ("",) if x.casefold() != y.casefold()}
+
+
+Whitespace = r"[ \f\t]*"
+Comment = r"#[^\r\n]*"
+Ignore = Whitespace + any(r"\\\r?\n" + Whitespace) + maybe(Comment)
+Name = (  # this is invalid but it's fine because Name comes after Number in all groups
+    r"[^\s#\(\)\[\]\{\}+\-*/!@$%^&=|;:'\",\.<>/?`~\\]+"
+)
+
+Binnumber = r"0[bB]_?[01]+(?:_[01]+)*"
+Hexnumber = r"0[xX]_?[\da-fA-F]+(?:_[\da-fA-F]+)*[lL]?"
+Octnumber = r"0[oO]?_?[0-7]+(?:_[0-7]+)*[lL]?"
+Decnumber = group(r"[1-9]\d*(?:_\d+)*[lL]?", "0[lL]?")
+Intnumber = group(Binnumber, Hexnumber, Octnumber, Decnumber)
+Exponent = r"[eE][-+]?\d+(?:_\d+)*"
+Pointfloat = group(r"\d+(?:_\d+)*\.(?:\d+(?:_\d+)*)?", r"\.\d+(?:_\d+)*") + maybe(
+    Exponent
+)
+Expfloat = r"\d+(?:_\d+)*" + Exponent
+Floatnumber = group(Pointfloat, Expfloat)
+Imagnumber = group(r"\d+(?:_\d+)*[jJ]", Floatnumber + r"[jJ]")
+Number = group(Imagnumber, Floatnumber, Intnumber)
+
+# Tail end of ' string.
+Single = r"(?:\\.|[^'\\])*'"
+# Tail end of " string.
+Double = r'(?:\\.|[^"\\])*"'
+# Tail end of ''' string.
+Single3 = r"(?:\\.|'(?!'')|[^'\\])*'''"
+# Tail end of """ string.
+Double3 = r'(?:\\.|"(?!"")|[^"\\])*"""'
+_litprefix = r"(?:[uUrRbB]|[rR][bB]|[bBuU][rR])?"
+_fstringlitprefix = r"(?:rF|FR|Fr|fr|RF|F|rf|f|Rf|fR)"
+Triple = group(
+    _litprefix + "'''",
+    _litprefix + '"""',
+    _fstringlitprefix + '"""',
+    _fstringlitprefix + "'''",
+)
+
+# beginning of a single quoted f-string. must not end with `{{` or `\N{`
+SingleLbrace = r"(?:\\N{|{{|\\'|[^\n'{])*(?<!\\N)({)(?!{)"
+DoubleLbrace = r'(?:\\N{|{{|\\"|[^\n"{])*(?<!\\N)({)(?!{)'
+
+# beginning of a triple quoted f-string. must not end with `{{` or `\N{`
+Single3Lbrace = r"(?:\\N{|{{|\\'|'(?!'')|[^'{])*(?<!\\N){(?!{)"
+Double3Lbrace = r'(?:\\N{|{{|\\"|"(?!"")|[^"{])*(?<!\\N){(?!{)'
+
+# ! format specifier inside an fstring brace, ensure it's not a `!=` token
+Bang = Whitespace + group("!") + r"(?!=)"
+bang = re.compile(Bang)
+Colon = Whitespace + group(":")
+colon = re.compile(Colon)
+
+FstringMiddleAfterColon = group(Whitespace + r".*?") + group("{", "}")
+fstring_middle_after_colon = re.compile(FstringMiddleAfterColon)
+
+# Because of leftmost-then-longest match semantics, be sure to put the
+# longest operators first (e.g., if = came before ==, == would get
+# recognized as two instances of =).
+Operator = group(
+    r"\*\*=?",
+    r">>=?",
+    r"<<=?",
+    r"<>",
+    r"!=",
+    r"//=?",
+    r"->",
+    r"[+\-*/%&@|^=<>:]=?",
+    r"~",
+)
+
+Bracket = "[][(){}]"
+Special = group(r"\r?\n", r"[:;.,`@]")
+Funny = group(Operator, Bracket, Special)
+
+_string_middle_single = r"(?:[^\n'\\]|\\.)*"
+_string_middle_double = r'(?:[^\n"\\]|\\.)*'
+
+# FSTRING_MIDDLE and LBRACE, must not end with a `{{` or `\N{`
+_fstring_middle_single = SingleLbrace
+_fstring_middle_double = DoubleLbrace
+
+# First (or only) line of ' or " string.
+ContStr = group(
+    _litprefix + "'" + _string_middle_single + group("'", r"\\\r?\n"),
+    _litprefix + '"' + _string_middle_double + group('"', r"\\\r?\n"),
+    group(_fstringlitprefix + "'") + _fstring_middle_single,
+    group(_fstringlitprefix + '"') + _fstring_middle_double,
+    group(_fstringlitprefix + "'") + _string_middle_single + group("'", r"\\\r?\n"),
+    group(_fstringlitprefix + '"') + _string_middle_double + group('"', r"\\\r?\n"),
+)
+PseudoExtras = group(r"\\\r?\n", Comment, Triple)
+PseudoToken = Whitespace + group(PseudoExtras, Number, Funny, ContStr, Name)
+
+pseudoprog: Final = re.compile(PseudoToken, re.UNICODE)
+
+singleprog = re.compile(Single)
+singleprog_plus_lbrace = re.compile(group(SingleLbrace, Single))
+doubleprog = re.compile(Double)
+doubleprog_plus_lbrace = re.compile(group(DoubleLbrace, Double))
+
+single3prog = re.compile(Single3)
+single3prog_plus_lbrace = re.compile(group(Single3Lbrace, Single3))
+double3prog = re.compile(Double3)
+double3prog_plus_lbrace = re.compile(group(Double3Lbrace, Double3))
+
+_strprefixes = _combinations("r", "R", "b", "B") | {"u", "U", "ur", "uR", "Ur", "UR"}
+_fstring_prefixes = _combinations("r", "R", "f", "F") - {"r", "R"}
+
+endprogs: Final = {
+    "'": singleprog,
+    '"': doubleprog,
+    "'''": single3prog,
+    '"""': double3prog,
+    **{f"{prefix}'": singleprog for prefix in _strprefixes},
+    **{f'{prefix}"': doubleprog for prefix in _strprefixes},
+    **{f"{prefix}'": singleprog_plus_lbrace for prefix in _fstring_prefixes},
+    **{f'{prefix}"': doubleprog_plus_lbrace for prefix in _fstring_prefixes},
+    **{f"{prefix}'''": single3prog for prefix in _strprefixes},
+    **{f'{prefix}"""': double3prog for prefix in _strprefixes},
+    **{f"{prefix}'''": single3prog_plus_lbrace for prefix in _fstring_prefixes},
+    **{f'{prefix}"""': double3prog_plus_lbrace for prefix in _fstring_prefixes},
+}
+
+triple_quoted: Final = (
+    {"'''", '"""'}
+    | {f"{prefix}'''" for prefix in _strprefixes | _fstring_prefixes}
+    | {f'{prefix}"""' for prefix in _strprefixes | _fstring_prefixes}
+)
+single_quoted: Final = (
+    {"'", '"'}
+    | {f"{prefix}'" for prefix in _strprefixes | _fstring_prefixes}
+    | {f'{prefix}"' for prefix in _strprefixes | _fstring_prefixes}
+)
+fstring_prefix: Final = (
+    {f"{prefix}'" for prefix in _fstring_prefixes}
+    | {f'{prefix}"' for prefix in _fstring_prefixes}
+    | {f"{prefix}'''" for prefix in _fstring_prefixes}
+    | {f'{prefix}"""' for prefix in _fstring_prefixes}
+)
+
+tabsize = 8
+
+
+class TokenError(Exception):
+    pass
+
+
+class StopTokenizing(Exception):
+    pass
+
+
+Coord = tuple[int, int]
+
+
+def printtoken(
+    type: int, token: str, srow_col: Coord, erow_col: Coord, line: str
+) -> None:  # for testing
+    (srow, scol) = srow_col
+    (erow, ecol) = erow_col
+    print(
+        "%d,%d-%d,%d:\t%s\t%s" % (srow, scol, erow, ecol, tok_name[type], repr(token))
+    )
+
+
+TokenEater = Callable[[int, str, Coord, Coord, str], None]
+
+
+def tokenize(readline: Callable[[], str], tokeneater: TokenEater = printtoken) -> None:
+    """
+    The tokenize() function accepts two parameters: one representing the
+    input stream, and one providing an output mechanism for tokenize().
+
+    The first parameter, readline, must be a callable object which provides
+    the same interface as the readline() method of built-in file objects.
+    Each call to the function should return one line of input as a string.
+
+    The second parameter, tokeneater, must also be a callable object. It is
+    called once for each token, with five arguments, corresponding to the
+    tuples generated by generate_tokens().
+    """
+    try:
+        tokenize_loop(readline, tokeneater)
+    except StopTokenizing:
+        pass
+
+
+# backwards compatible interface
+def tokenize_loop(readline: Callable[[], str], tokeneater: TokenEater) -> None:
+    for token_info in generate_tokens(readline):
+        tokeneater(*token_info)
+
+
+GoodTokenInfo = tuple[int, str, Coord, Coord, str]
+TokenInfo = Union[tuple[int, str], GoodTokenInfo]
+
+
+class Untokenizer:
+    tokens: list[str]
+    prev_row: int
+    prev_col: int
+
+    def __init__(self) -> None:
+        self.tokens = []
+        self.prev_row = 1
+        self.prev_col = 0
+
+    def add_whitespace(self, start: Coord) -> None:
+        row, col = start
+        assert row <= self.prev_row
+        col_offset = col - self.prev_col
+        if col_offset:
+            self.tokens.append(" " * col_offset)
+
+    def untokenize(self, iterable: Iterable[TokenInfo]) -> str:
+        for t in iterable:
+            if len(t) == 2:
+                self.compat(t, iterable)
+                break
+            tok_type, token, start, end, line = t
+            self.add_whitespace(start)
+            self.tokens.append(token)
+            self.prev_row, self.prev_col = end
+            if tok_type in (NEWLINE, NL):
+                self.prev_row += 1
+                self.prev_col = 0
+        return "".join(self.tokens)
+
+    def compat(self, token: tuple[int, str], iterable: Iterable[TokenInfo]) -> None:
+        startline = False
+        indents = []
+        toks_append = self.tokens.append
+        toknum, tokval = token
+        if toknum in (NAME, NUMBER):
+            tokval += " "
+        if toknum in (NEWLINE, NL):
+            startline = True
+        for tok in iterable:
+            toknum, tokval = tok[:2]
+
+            if toknum in (NAME, NUMBER, ASYNC, AWAIT):
+                tokval += " "
+
+            if toknum == INDENT:
+                indents.append(tokval)
+                continue
+            elif toknum == DEDENT:
+                indents.pop()
+                continue
+            elif toknum in (NEWLINE, NL):
+                startline = True
+            elif startline and indents:
+                toks_append(indents[-1])
+                startline = False
+            toks_append(tokval)
+
+
+cookie_re = re.compile(r"^[ \t\f]*#.*?coding[:=][ \t]*([-\w.]+)", re.ASCII)
+blank_re = re.compile(rb"^[ \t\f]*(?:[#\r\n]|$)", re.ASCII)
+
+
+def _get_normal_name(orig_enc: str) -> str:
+    """Imitates get_normal_name in tokenizer.c."""
+    # Only care about the first 12 characters.
+    enc = orig_enc[:12].lower().replace("_", "-")
+    if enc == "utf-8" or enc.startswith("utf-8-"):
+        return "utf-8"
+    if enc in ("latin-1", "iso-8859-1", "iso-latin-1") or enc.startswith(
+        ("latin-1-", "iso-8859-1-", "iso-latin-1-")
+    ):
+        return "iso-8859-1"
+    return orig_enc
+
+
+def detect_encoding(readline: Callable[[], bytes]) -> tuple[str, list[bytes]]:
+    """
+    The detect_encoding() function is used to detect the encoding that should
+    be used to decode a Python source file. It requires one argument, readline,
+    in the same way as the tokenize() generator.
+
+    It will call readline a maximum of twice, and return the encoding used
+    (as a string) and a list of any lines (left as bytes) it has read
+    in.
+
+    It detects the encoding from the presence of a utf-8 bom or an encoding
+    cookie as specified in pep-0263. If both a bom and a cookie are present, but
+    disagree, a SyntaxError will be raised. If the encoding cookie is an invalid
+    charset, raise a SyntaxError.  Note that if a utf-8 bom is found,
+    'utf-8-sig' is returned.
+
+    If no encoding is specified, then the default of 'utf-8' will be returned.
+    """
+    bom_found = False
+    encoding = None
+    default = "utf-8"
+
+    def read_or_stop() -> bytes:
+        try:
+            return readline()
+        except StopIteration:
+            return b""
+
+    def find_cookie(line: bytes) -> Optional[str]:
+        try:
+            line_string = line.decode("ascii")
+        except UnicodeDecodeError:
+            return None
+        match = cookie_re.match(line_string)
+        if not match:
+            return None
+        encoding = _get_normal_name(match.group(1))
+        try:
+            codec = lookup(encoding)
+        except LookupError:
+            # This behaviour mimics the Python interpreter
+            raise SyntaxError("unknown encoding: " + encoding)
+
+        if bom_found:
+            if codec.name != "utf-8":
+                # This behaviour mimics the Python interpreter
+                raise SyntaxError("encoding problem: utf-8")
+            encoding += "-sig"
+        return encoding
+
+    first = read_or_stop()
+    if first.startswith(BOM_UTF8):
+        bom_found = True
+        first = first[3:]
+        default = "utf-8-sig"
+    if not first:
+        return default, []
+
+    encoding = find_cookie(first)
+    if encoding:
+        return encoding, [first]
+    if not blank_re.match(first):
+        return default, [first]
+
+    second = read_or_stop()
+    if not second:
+        return default, [first]
+
+    encoding = find_cookie(second)
+    if encoding:
+        return encoding, [first, second]
+
+    return default, [first, second]
+
+
+def untokenize(iterable: Iterable[TokenInfo]) -> str:
+    """Transform tokens back into Python source code.
+
+    Each element returned by the iterable must be a token sequence
+    with at least two elements, a token number and token value.  If
+    only two tokens are passed, the resulting output is poor.
+
+    Round-trip invariant for full input:
+        Untokenized source will match input source exactly
+
+    Round-trip invariant for limited input:
+        # Output text will tokenize the back to the input
+        t1 = [tok[:2] for tok in generate_tokens(f.readline)]
+        newcode = untokenize(t1)
+        readline = iter(newcode.splitlines(1)).next
+        t2 = [tok[:2] for tokin generate_tokens(readline)]
+        assert t1 == t2
+    """
+    ut = Untokenizer()
+    return ut.untokenize(iterable)
+
+
+def is_fstring_start(token: str) -> bool:
+    return builtins.any(token.startswith(prefix) for prefix in fstring_prefix)
+
+
+def _split_fstring_start_and_middle(token: str) -> tuple[str, str]:
+    for prefix in fstring_prefix:
+        _, prefix, rest = token.partition(prefix)
+        if prefix != "":
+            return prefix, rest
+
+    raise ValueError(f"Token {token!r} is not a valid f-string start")
+
+
+STATE_NOT_FSTRING: Final = 0  # not in an f-string
+STATE_MIDDLE: Final = 1  # in the string portion of an f-string (outside braces)
+STATE_IN_BRACES: Final = 2  # between braces in an f-string
+# in the format specifier (between the colon and the closing brace)
+STATE_IN_COLON: Final = 3
+
+
+class FStringState:
+    """Keeps track of state around f-strings.
+
+    The tokenizer should call the appropriate method on this class when
+    it transitions to a different part of an f-string. This is needed
+    because the tokenization depends on knowing where exactly we are in
+    the f-string.
+
+    For example, consider the following f-string:
+
+        f"a{1:b{2}c}d"
+
+    The following is the tokenization of this string and the states
+    tracked by this class:
+
+        1,0-1,2:	FSTRING_START	'f"'  # [STATE_NOT_FSTRING, STATE_MIDDLE]
+        1,2-1,3:	FSTRING_MIDDLE	'a'
+        1,3-1,4:	LBRACE	'{'  # [STATE_NOT_FSTRING, STATE_IN_BRACES]
+        1,4-1,5:	NUMBER	'1'
+        1,5-1,6:	OP	':'  # [STATE_NOT_FSTRING, STATE_IN_COLON]
+        1,6-1,7:	FSTRING_MIDDLE	'b'
+        1,7-1,8:	LBRACE	'{'  # [STATE_NOT_FSTRING, STATE_IN_COLON, STATE_IN_BRACES]
+        1,8-1,9:	NUMBER	'2'
+        1,9-1,10:	RBRACE	'}'  # [STATE_NOT_FSTRING, STATE_IN_COLON]
+        1,10-1,11:	FSTRING_MIDDLE	'c'
+        1,11-1,12:	RBRACE	'}'  # [STATE_NOT_FSTRING, STATE_MIDDLE]
+        1,12-1,13:	FSTRING_MIDDLE	'd'
+        1,13-1,14:	FSTRING_END	'"'  # [STATE_NOT_FSTRING]
+        1,14-1,15:	NEWLINE	'\n'
+        2,0-2,0:	ENDMARKER	''
+
+    Notice that the nested braces in the format specifier are represented
+    by adding a STATE_IN_BRACES entry to the state stack. The stack is
+    also used if there are nested f-strings.
+
+    """
+
+    def __init__(self) -> None:
+        self.stack: list[int] = [STATE_NOT_FSTRING]
+
+    def is_in_fstring_expression(self) -> bool:
+        return self.stack[-1] not in (STATE_MIDDLE, STATE_NOT_FSTRING)
+
+    def current(self) -> int:
+        return self.stack[-1]
+
+    def enter_fstring(self) -> None:
+        self.stack.append(STATE_MIDDLE)
+
+    def leave_fstring(self) -> None:
+        state = self.stack.pop()
+        assert state == STATE_MIDDLE
+
+    def consume_lbrace(self) -> None:
+        current_state = self.stack[-1]
+        if current_state == STATE_MIDDLE:
+            self.stack[-1] = STATE_IN_BRACES
+        elif current_state == STATE_IN_COLON:
+            self.stack.append(STATE_IN_BRACES)
+        else:
+            assert False, current_state
+
+    def consume_rbrace(self) -> None:
+        current_state = self.stack[-1]
+        assert current_state in (STATE_IN_BRACES, STATE_IN_COLON)
+        if len(self.stack) > 1 and self.stack[-2] == STATE_IN_COLON:
+            self.stack.pop()
+        else:
+            self.stack[-1] = STATE_MIDDLE
+
+    def consume_colon(self) -> None:
+        assert self.stack[-1] == STATE_IN_BRACES, self.stack
+        self.stack[-1] = STATE_IN_COLON
+
+
+def generate_tokens(
+    readline: Callable[[], str], grammar: Optional[Grammar] = None
+) -> Iterator[GoodTokenInfo]:
+    """
+    The generate_tokens() generator requires one argument, readline, which
+    must be a callable object which provides the same interface as the
+    readline() method of built-in file objects. Each call to the function
+    should return one line of input as a string.  Alternately, readline
+    can be a callable function terminating with StopIteration:
+        readline = open(myfile).next    # Example of alternate readline
+
+    The generator produces 5-tuples with these members: the token type; the
+    token string; a 2-tuple (srow, scol) of ints specifying the row and
+    column where the token begins in the source; a 2-tuple (erow, ecol) of
+    ints specifying the row and column where the token ends in the source;
+    and the line on which the token was found. The line passed is the
+    logical line; continuation lines are included.
+    """
+    lnum = parenlev = continued = 0
+    parenlev_stack: list[int] = []
+    fstring_state = FStringState()
+    formatspec = ""
+    numchars: Final[str] = "0123456789"
+    contstr, needcont = "", 0
+    contline: Optional[str] = None
+    indents = [0]
+
+    # If we know we're parsing 3.7+, we can unconditionally parse `async` and
+    # `await` as keywords.
+    async_keywords = False if grammar is None else grammar.async_keywords
+    # 'stashed' and 'async_*' are used for async/await parsing
+    stashed: Optional[GoodTokenInfo] = None
+    async_def = False
+    async_def_indent = 0
+    async_def_nl = False
+
+    strstart: tuple[int, int]
+    endprog_stack: list[Pattern[str]] = []
+    formatspec_start: tuple[int, int]
+
+    while 1:  # loop over lines in stream
+        try:
+            line = readline()
+        except StopIteration:
+            line = ""
+        lnum += 1
+
+        # skip lines that are just indent characters ending with a slash
+        # to avoid storing that line's indent information.
+        if not contstr and line.rstrip("\n").strip(" \t\f") == "\\":
+            continue
+
+        pos, max = 0, len(line)
+
+        if contstr:  # continued string
+            assert contline is not None
+            if not line:
+                raise TokenError("EOF in multi-line string", strstart)
+            endprog = endprog_stack[-1]
+            endmatch = endprog.match(line)
+            if endmatch:
+                end = endmatch.end(0)
+                token = contstr + line[:end]
+                spos = strstart
+                epos = (lnum, end)
+                tokenline = contline + line
+                if fstring_state.current() in (
+                    STATE_NOT_FSTRING,
+                    STATE_IN_BRACES,
+                ) and not is_fstring_start(token):
+                    yield (STRING, token, spos, epos, tokenline)
+                    endprog_stack.pop()
+                    parenlev = parenlev_stack.pop()
+                else:
+                    if is_fstring_start(token):
+                        fstring_start, token = _split_fstring_start_and_middle(token)
+                        fstring_start_epos = (spos[0], spos[1] + len(fstring_start))
+                        yield (
+                            FSTRING_START,
+                            fstring_start,
+                            spos,
+                            fstring_start_epos,
+                            tokenline,
+                        )
+                        fstring_state.enter_fstring()
+                        # increase spos to the end of the fstring start
+                        spos = fstring_start_epos
+
+                    if token.endswith("{"):
+                        fstring_middle, lbrace = token[:-1], token[-1]
+                        fstring_middle_epos = lbrace_spos = (lnum, end - 1)
+                        yield (
+                            FSTRING_MIDDLE,
+                            fstring_middle,
+                            spos,
+                            fstring_middle_epos,
+                            line,
+                        )
+                        yield (LBRACE, lbrace, lbrace_spos, epos, line)
+                        fstring_state.consume_lbrace()
+                    else:
+                        if token.endswith(('"""', "'''")):
+                            fstring_middle, fstring_end = token[:-3], token[-3:]
+                            fstring_middle_epos = end_spos = (lnum, end - 3)
+                        else:
+                            fstring_middle, fstring_end = token[:-1], token[-1]
+                            fstring_middle_epos = end_spos = (lnum, end - 1)
+                        yield (
+                            FSTRING_MIDDLE,
+                            fstring_middle,
+                            spos,
+                            fstring_middle_epos,
+                            line,
+                        )
+                        yield (
+                            FSTRING_END,
+                            fstring_end,
+                            end_spos,
+                            epos,
+                            line,
+                        )
+                        fstring_state.leave_fstring()
+                        endprog_stack.pop()
+                        parenlev = parenlev_stack.pop()
+                pos = end
+                contstr, needcont = "", 0
+                contline = None
+            elif needcont and line[-2:] != "\\\n" and line[-3:] != "\\\r\n":
+                yield (
+                    ERRORTOKEN,
+                    contstr + line,
+                    strstart,
+                    (lnum, len(line)),
+                    contline,
+                )
+                contstr = ""
+                contline = None
+                continue
+            else:
+                contstr = contstr + line
+                contline = contline + line
+                continue
+
+        # new statement
+        elif (
+            parenlev == 0
+            and not continued
+            and not fstring_state.is_in_fstring_expression()
+        ):
+            if not line:
+                break
+            column = 0
+            while pos < max:  # measure leading whitespace
+                if line[pos] == " ":
+                    column += 1
+                elif line[pos] == "\t":
+                    column = (column // tabsize + 1) * tabsize
+                elif line[pos] == "\f":
+                    column = 0
+                else:
+                    break
+                pos += 1
+            if pos == max:
+                break
+
+            if stashed:
+                yield stashed
+                stashed = None
+
+            if line[pos] in "\r\n":  # skip blank lines
+                yield (NL, line[pos:], (lnum, pos), (lnum, len(line)), line)
+                continue
+
+            if line[pos] == "#":  # skip comments
+                comment_token = line[pos:].rstrip("\r\n")
+                nl_pos = pos + len(comment_token)
+                yield (
+                    COMMENT,
+                    comment_token,
+                    (lnum, pos),
+                    (lnum, nl_pos),
+                    line,
+                )
+                yield (NL, line[nl_pos:], (lnum, nl_pos), (lnum, len(line)), line)
+                continue
+
+            if column > indents[-1]:  # count indents
+                indents.append(column)
+                yield (INDENT, line[:pos], (lnum, 0), (lnum, pos), line)
+
+            while column < indents[-1]:  # count dedents
+                if column not in indents:
+                    raise IndentationError(
+                        "unindent does not match any outer indentation level",
+                        ("<tokenize>", lnum, pos, line),
+                    )
+                indents = indents[:-1]
+
+                if async_def and async_def_indent >= indents[-1]:
+                    async_def = False
+                    async_def_nl = False
+                    async_def_indent = 0
+
+                yield (DEDENT, "", (lnum, pos), (lnum, pos), line)
+
+            if async_def and async_def_nl and async_def_indent >= indents[-1]:
+                async_def = False
+                async_def_nl = False
+                async_def_indent = 0
+
+        else:  # continued statement
+            if not line:
+                raise TokenError("EOF in multi-line statement", (lnum, 0))
+            continued = 0
+
+        while pos < max:
+            if fstring_state.current() == STATE_MIDDLE:
+                endprog = endprog_stack[-1]
+                endmatch = endprog.match(line, pos)
+                if endmatch:  # all on one line
+                    start, end = endmatch.span(0)
+                    token = line[start:end]
+                    if token.endswith(('"""', "'''")):
+                        middle_token, end_token = token[:-3], token[-3:]
+                        middle_epos = end_spos = (lnum, end - 3)
+                    else:
+                        middle_token, end_token = token[:-1], token[-1]
+                        middle_epos = end_spos = (lnum, end - 1)
+                    # TODO: unsure if this can be safely removed
+                    if stashed:
+                        yield stashed
+                        stashed = None
+                    yield (
+                        FSTRING_MIDDLE,
+                        middle_token,
+                        (lnum, pos),
+                        middle_epos,
+                        line,
+                    )
+                    if not token.endswith("{"):
+                        yield (
+                            FSTRING_END,
+                            end_token,
+                            end_spos,
+                            (lnum, end),
+                            line,
+                        )
+                        fstring_state.leave_fstring()
+                        endprog_stack.pop()
+                        parenlev = parenlev_stack.pop()
+                    else:
+                        yield (LBRACE, "{", (lnum, end - 1), (lnum, end), line)
+                        fstring_state.consume_lbrace()
+                    pos = end
+                    continue
+                else:  # multiple lines
+                    strstart = (lnum, end)
+                    contstr = line[end:]
+                    contline = line
+                    break
+
+            if fstring_state.current() == STATE_IN_COLON:
+                match = fstring_middle_after_colon.match(line, pos)
+                if match is None:
+                    formatspec += line[pos:]
+                    pos = max
+                    continue
+
+                start, end = match.span(1)
+                token = line[start:end]
+                formatspec += token
+
+                brace_start, brace_end = match.span(2)
+                brace_or_nl = line[brace_start:brace_end]
+                if brace_or_nl == "\n":
+                    pos = brace_end
+
+                yield (FSTRING_MIDDLE, formatspec, formatspec_start, (lnum, end), line)
+                formatspec = ""
+
+                if brace_or_nl == "{":
+                    yield (LBRACE, "{", (lnum, brace_start), (lnum, brace_end), line)
+                    fstring_state.consume_lbrace()
+                    end = brace_end
+                elif brace_or_nl == "}":
+                    yield (RBRACE, "}", (lnum, brace_start), (lnum, brace_end), line)
+                    fstring_state.consume_rbrace()
+                    end = brace_end
+                    formatspec_start = (lnum, brace_end)
+
+                pos = end
+                continue
+
+            if fstring_state.current() == STATE_IN_BRACES and parenlev == 0:
+                match = bang.match(line, pos)
+                if match:
+                    start, end = match.span(1)
+                    yield (OP, "!", (lnum, start), (lnum, end), line)
+                    pos = end
+                    continue
+
+                match = colon.match(line, pos)
+                if match:
+                    start, end = match.span(1)
+                    yield (OP, ":", (lnum, start), (lnum, end), line)
+                    fstring_state.consume_colon()
+                    formatspec_start = (lnum, end)
+                    pos = end
+                    continue
+
+            pseudomatch = pseudoprog.match(line, pos)
+            if pseudomatch:  # scan for tokens
+                start, end = pseudomatch.span(1)
+                spos, epos, pos = (lnum, start), (lnum, end), end
+                token, initial = line[start:end], line[start]
+
+                if initial in numchars or (
+                    initial == "." and token != "."
+                ):  # ordinary number
+                    yield (NUMBER, token, spos, epos, line)
+                elif initial in "\r\n":
+                    newline = NEWLINE
+                    if parenlev > 0 or fstring_state.is_in_fstring_expression():
+                        newline = NL
+                    elif async_def:
+                        async_def_nl = True
+                    if stashed:
+                        yield stashed
+                        stashed = None
+                    yield (newline, token, spos, epos, line)
+
+                elif initial == "#":
+                    assert not token.endswith("\n")
+                    if stashed:
+                        yield stashed
+                        stashed = None
+                    yield (COMMENT, token, spos, epos, line)
+                elif token in triple_quoted:
+                    endprog = endprogs[token]
+                    endprog_stack.append(endprog)
+                    parenlev_stack.append(parenlev)
+                    parenlev = 0
+                    if is_fstring_start(token):
+                        yield (FSTRING_START, token, spos, epos, line)
+                        fstring_state.enter_fstring()
+
+                    endmatch = endprog.match(line, pos)
+                    if endmatch:  # all on one line
+                        if stashed:
+                            yield stashed
+                            stashed = None
+                        if not is_fstring_start(token):
+                            pos = endmatch.end(0)
+                            token = line[start:pos]
+                            epos = (lnum, pos)
+                            yield (STRING, token, spos, epos, line)
+                            endprog_stack.pop()
+                            parenlev = parenlev_stack.pop()
+                        else:
+                            end = endmatch.end(0)
+                            token = line[pos:end]
+                            spos, epos = (lnum, pos), (lnum, end)
+                            if not token.endswith("{"):
+                                fstring_middle, fstring_end = token[:-3], token[-3:]
+                                fstring_middle_epos = fstring_end_spos = (lnum, end - 3)
+                                yield (
+                                    FSTRING_MIDDLE,
+                                    fstring_middle,
+                                    spos,
+                                    fstring_middle_epos,
+                                    line,
+                                )
+                                yield (
+                                    FSTRING_END,
+                                    fstring_end,
+                                    fstring_end_spos,
+                                    epos,
+                                    line,
+                                )
+                                fstring_state.leave_fstring()
+                                endprog_stack.pop()
+                                parenlev = parenlev_stack.pop()
+                            else:
+                                fstring_middle, lbrace = token[:-1], token[-1]
+                                fstring_middle_epos = lbrace_spos = (lnum, end - 1)
+                                yield (
+                                    FSTRING_MIDDLE,
+                                    fstring_middle,
+                                    spos,
+                                    fstring_middle_epos,
+                                    line,
+                                )
+                                yield (LBRACE, lbrace, lbrace_spos, epos, line)
+                                fstring_state.consume_lbrace()
+                            pos = end
+                    else:
+                        # multiple lines
+                        if is_fstring_start(token):
+                            strstart = (lnum, pos)
+                            contstr = line[pos:]
+                        else:
+                            strstart = (lnum, start)
+                            contstr = line[start:]
+                        contline = line
+                        break
+                elif (
+                    initial in single_quoted
+                    or token[:2] in single_quoted
+                    or token[:3] in single_quoted
+                ):
+                    maybe_endprog = (
+                        endprogs.get(initial)
+                        or endprogs.get(token[:2])
+                        or endprogs.get(token[:3])
+                    )
+                    assert maybe_endprog is not None, f"endprog not found for {token}"
+                    endprog = maybe_endprog
+                    if token[-1] == "\n":  # continued string
+                        endprog_stack.append(endprog)
+                        parenlev_stack.append(parenlev)
+                        parenlev = 0
+                        strstart = (lnum, start)
+                        contstr, needcont = line[start:], 1
+                        contline = line
+                        break
+                    else:  # ordinary string
+                        if stashed:
+                            yield stashed
+                            stashed = None
+
+                        if not is_fstring_start(token):
+                            yield (STRING, token, spos, epos, line)
+                        else:
+                            if pseudomatch[20] is not None:
+                                fstring_start = pseudomatch[20]
+                                offset = pseudomatch.end(20) - pseudomatch.start(1)
+                            elif pseudomatch[22] is not None:
+                                fstring_start = pseudomatch[22]
+                                offset = pseudomatch.end(22) - pseudomatch.start(1)
+                            elif pseudomatch[24] is not None:
+                                fstring_start = pseudomatch[24]
+                                offset = pseudomatch.end(24) - pseudomatch.start(1)
+                            else:
+                                fstring_start = pseudomatch[26]
+                                offset = pseudomatch.end(26) - pseudomatch.start(1)
+
+                            start_epos = (lnum, start + offset)
+                            yield (FSTRING_START, fstring_start, spos, start_epos, line)
+                            fstring_state.enter_fstring()
+                            endprog = endprogs[fstring_start]
+                            endprog_stack.append(endprog)
+                            parenlev_stack.append(parenlev)
+                            parenlev = 0
+
+                            end_offset = pseudomatch.end(1) - 1
+                            fstring_middle = line[start + offset : end_offset]
+                            middle_spos = (lnum, start + offset)
+                            middle_epos = (lnum, end_offset)
+                            yield (
+                                FSTRING_MIDDLE,
+                                fstring_middle,
+                                middle_spos,
+                                middle_epos,
+                                line,
+                            )
+                            if not token.endswith("{"):
+                                end_spos = (lnum, end_offset)
+                                end_epos = (lnum, end_offset + 1)
+                                yield (FSTRING_END, token[-1], end_spos, end_epos, line)
+                                fstring_state.leave_fstring()
+                                endprog_stack.pop()
+                                parenlev = parenlev_stack.pop()
+                            else:
+                                end_spos = (lnum, end_offset)
+                                end_epos = (lnum, end_offset + 1)
+                                yield (LBRACE, "{", end_spos, end_epos, line)
+                                fstring_state.consume_lbrace()
+
+                elif initial.isidentifier():  # ordinary name
+                    if token in ("async", "await"):
+                        if async_keywords or async_def:
+                            yield (
+                                ASYNC if token == "async" else AWAIT,
+                                token,
+                                spos,
+                                epos,
+                                line,
+                            )
+                            continue
+
+                    tok = (NAME, token, spos, epos, line)
+                    if token == "async" and not stashed:
+                        stashed = tok
+                        continue
+
+                    if token in ("def", "for"):
+                        if stashed and stashed[0] == NAME and stashed[1] == "async":
+                            if token == "def":
+                                async_def = True
+                                async_def_indent = indents[-1]
+
+                            yield (
+                                ASYNC,
+                                stashed[1],
+                                stashed[2],
+                                stashed[3],
+                                stashed[4],
+                            )
+                            stashed = None
+
+                    if stashed:
+                        yield stashed
+                        stashed = None
+
+                    yield tok
+                elif initial == "\\":  # continued stmt
+                    # This yield is new; needed for better idempotency:
+                    if stashed:
+                        yield stashed
+                        stashed = None
+                    yield (NL, token, spos, (lnum, pos), line)
+                    continued = 1
+                elif (
+                    initial == "}"
+                    and parenlev == 0
+                    and fstring_state.is_in_fstring_expression()
+                ):
+                    yield (RBRACE, token, spos, epos, line)
+                    fstring_state.consume_rbrace()
+                    formatspec_start = epos
+                else:
+                    if initial in "([{":
+                        parenlev += 1
+                    elif initial in ")]}":
+                        parenlev -= 1
+                    if stashed:
+                        yield stashed
+                        stashed = None
+                    yield (OP, token, spos, epos, line)
+            else:
+                yield (ERRORTOKEN, line[pos], (lnum, pos), (lnum, pos + 1), line)
+                pos += 1
+
+    if stashed:
+        yield stashed
+        stashed = None
+
+    for _indent in indents[1:]:  # pop remaining indent levels
+        yield (DEDENT, "", (lnum, 0), (lnum, 0), "")
+    yield (ENDMARKER, "", (lnum, 0), (lnum, 0), "")
+    assert len(endprog_stack) == 0
+    assert len(parenlev_stack) == 0
+
+
+if __name__ == "__main__":  # testing
+    if len(sys.argv) > 1:
+        tokenize(open(sys.argv[1]).readline)
+    else:
+        tokenize(sys.stdin.readline)

openflamingo/lib/python3.10/site-packages/blib2to3/pygram.py

@@ -0,0 +1,204 @@
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""Export the Python grammar and symbols."""
+
+# Python imports
+import os
+from typing import Union
+
+# Local imports
+from .pgen2 import driver
+from .pgen2.grammar import Grammar
+
+# Moved into initialize because mypyc can't handle __file__ (XXX bug)
+# # The grammar file
+# _GRAMMAR_FILE = os.path.join(os.path.dirname(__file__), "Grammar.txt")
+# _PATTERN_GRAMMAR_FILE = os.path.join(os.path.dirname(__file__),
+#                                      "PatternGrammar.txt")
+
+
+class Symbols:
+    def __init__(self, grammar: Grammar) -> None:
+        """Initializer.
+
+        Creates an attribute for each grammar symbol (nonterminal),
+        whose value is the symbol's type (an int >= 256).
+        """
+        for name, symbol in grammar.symbol2number.items():
+            setattr(self, name, symbol)
+
+
+class _python_symbols(Symbols):
+    and_expr: int
+    and_test: int
+    annassign: int
+    arglist: int
+    argument: int
+    arith_expr: int
+    asexpr_test: int
+    assert_stmt: int
+    async_funcdef: int
+    async_stmt: int
+    atom: int
+    augassign: int
+    break_stmt: int
+    case_block: int
+    classdef: int
+    comp_for: int
+    comp_if: int
+    comp_iter: int
+    comp_op: int
+    comparison: int
+    compound_stmt: int
+    continue_stmt: int
+    decorated: int
+    decorator: int
+    decorators: int
+    del_stmt: int
+    dictsetmaker: int
+    dotted_as_name: int
+    dotted_as_names: int
+    dotted_name: int
+    encoding_decl: int
+    eval_input: int
+    except_clause: int
+    expr: int
+    expr_stmt: int
+    exprlist: int
+    factor: int
+    file_input: int
+    flow_stmt: int
+    for_stmt: int
+    fstring: int
+    fstring_format_spec: int
+    fstring_middle: int
+    fstring_replacement_field: int
+    funcdef: int
+    global_stmt: int
+    guard: int
+    if_stmt: int
+    import_as_name: int
+    import_as_names: int
+    import_from: int
+    import_name: int
+    import_stmt: int
+    lambdef: int
+    listmaker: int
+    match_stmt: int
+    namedexpr_test: int
+    not_test: int
+    old_comp_for: int
+    old_comp_if: int
+    old_comp_iter: int
+    old_lambdef: int
+    old_test: int
+    or_test: int
+    parameters: int
+    paramspec: int
+    pass_stmt: int
+    pattern: int
+    patterns: int
+    power: int
+    raise_stmt: int
+    return_stmt: int
+    shift_expr: int
+    simple_stmt: int
+    single_input: int
+    sliceop: int
+    small_stmt: int
+    subject_expr: int
+    star_expr: int
+    stmt: int
+    subscript: int
+    subscriptlist: int
+    suite: int
+    term: int
+    test: int
+    testlist: int
+    testlist1: int
+    testlist_gexp: int
+    testlist_safe: int
+    testlist_star_expr: int
+    tfpdef: int
+    tfplist: int
+    tname: int
+    tname_star: int
+    trailer: int
+    try_stmt: int
+    type_stmt: int
+    typedargslist: int
+    typeparam: int
+    typeparams: int
+    typevar: int
+    typevartuple: int
+    varargslist: int
+    vfpdef: int
+    vfplist: int
+    vname: int
+    while_stmt: int
+    with_stmt: int
+    xor_expr: int
+    yield_arg: int
+    yield_expr: int
+    yield_stmt: int
+
+
+class _pattern_symbols(Symbols):
+    Alternative: int
+    Alternatives: int
+    Details: int
+    Matcher: int
+    NegatedUnit: int
+    Repeater: int
+    Unit: int
+
+
+python_grammar: Grammar
+python_grammar_async_keywords: Grammar
+python_grammar_soft_keywords: Grammar
+pattern_grammar: Grammar
+python_symbols: _python_symbols
+pattern_symbols: _pattern_symbols
+
+
+def initialize(cache_dir: Union[str, "os.PathLike[str]", None] = None) -> None:
+    global python_grammar
+    global python_grammar_async_keywords
+    global python_grammar_soft_keywords
+    global python_symbols
+    global pattern_grammar
+    global pattern_symbols
+
+    # The grammar file
+    _GRAMMAR_FILE = os.path.join(os.path.dirname(__file__), "Grammar.txt")
+    _PATTERN_GRAMMAR_FILE = os.path.join(
+        os.path.dirname(__file__), "PatternGrammar.txt"
+    )
+
+    python_grammar = driver.load_packaged_grammar("blib2to3", _GRAMMAR_FILE, cache_dir)
+    assert "print" not in python_grammar.keywords
+    assert "exec" not in python_grammar.keywords
+
+    soft_keywords = python_grammar.soft_keywords.copy()
+    python_grammar.soft_keywords.clear()
+
+    python_symbols = _python_symbols(python_grammar)
+
+    # Python 3.0-3.6
+    python_grammar.version = (3, 0)
+
+    # Python 3.7+
+    python_grammar_async_keywords = python_grammar.copy()
+    python_grammar_async_keywords.async_keywords = True
+    python_grammar_async_keywords.version = (3, 7)
+
+    # Python 3.10+
+    python_grammar_soft_keywords = python_grammar_async_keywords.copy()
+    python_grammar_soft_keywords.soft_keywords = soft_keywords
+    python_grammar_soft_keywords.version = (3, 10)
+
+    pattern_grammar = driver.load_packaged_grammar(
+        "blib2to3", _PATTERN_GRAMMAR_FILE, cache_dir
+    )
+    pattern_symbols = _pattern_symbols(pattern_grammar)

openflamingo/lib/python3.10/site-packages/blib2to3/pytree.py

@@ -0,0 +1,975 @@
+# Copyright 2006 Google, Inc. All Rights Reserved.
+# Licensed to PSF under a Contributor Agreement.
+
+"""
+Python parse tree definitions.
+
+This is a very concrete parse tree; we need to keep every token and
+even the comments and whitespace between tokens.
+
+There's also a pattern matching implementation here.
+"""
+
+# mypy: allow-untyped-defs, allow-incomplete-defs
+
+from typing import Any, Iterable, Iterator, Optional, TypeVar, Union
+
+from blib2to3.pgen2.grammar import Grammar
+
+__author__ = "Guido van Rossum <guido@python.org>"
+
+import sys
+from io import StringIO
+
+HUGE: int = 0x7FFFFFFF  # maximum repeat count, default max
+
+_type_reprs: dict[int, Union[str, int]] = {}
+
+
+def type_repr(type_num: int) -> Union[str, int]:
+    global _type_reprs
+    if not _type_reprs:
+        from . import pygram
+
+        if not hasattr(pygram, "python_symbols"):
+            pygram.initialize(cache_dir=None)
+
+        # printing tokens is possible but not as useful
+        # from .pgen2 import token // token.__dict__.items():
+        for name in dir(pygram.python_symbols):
+            val = getattr(pygram.python_symbols, name)
+            if type(val) == int:
+                _type_reprs[val] = name
+    return _type_reprs.setdefault(type_num, type_num)
+
+
+_P = TypeVar("_P", bound="Base")
+
+NL = Union["Node", "Leaf"]
+Context = tuple[str, tuple[int, int]]
+RawNode = tuple[int, Optional[str], Optional[Context], Optional[list[NL]]]
+
+
+class Base:
+    """
+    Abstract base class for Node and Leaf.
+
+    This provides some default functionality and boilerplate using the
+    template pattern.
+
+    A node may be a subnode of at most one parent.
+    """
+
+    # Default values for instance variables
+    type: int  # int: token number (< 256) or symbol number (>= 256)
+    parent: Optional["Node"] = None  # Parent node pointer, or None
+    children: list[NL]  # List of subnodes
+    was_changed: bool = False
+    was_checked: bool = False
+
+    def __new__(cls, *args, **kwds):
+        """Constructor that prevents Base from being instantiated."""
+        assert cls is not Base, "Cannot instantiate Base"
+        return object.__new__(cls)
+
+    def __eq__(self, other: Any) -> bool:
+        """
+        Compare two nodes for equality.
+
+        This calls the method _eq().
+        """
+        if self.__class__ is not other.__class__:
+            return NotImplemented
+        return self._eq(other)
+
+    @property
+    def prefix(self) -> str:
+        raise NotImplementedError
+
+    def _eq(self: _P, other: _P) -> bool:
+        """
+        Compare two nodes for equality.
+
+        This is called by __eq__ and __ne__.  It is only called if the two nodes
+        have the same type.  This must be implemented by the concrete subclass.
+        Nodes should be considered equal if they have the same structure,
+        ignoring the prefix string and other context information.
+        """
+        raise NotImplementedError
+
+    def __deepcopy__(self: _P, memo: Any) -> _P:
+        return self.clone()
+
+    def clone(self: _P) -> _P:
+        """
+        Return a cloned (deep) copy of self.
+
+        This must be implemented by the concrete subclass.
+        """
+        raise NotImplementedError
+
+    def post_order(self) -> Iterator[NL]:
+        """
+        Return a post-order iterator for the tree.
+
+        This must be implemented by the concrete subclass.
+        """
+        raise NotImplementedError
+
+    def pre_order(self) -> Iterator[NL]:
+        """
+        Return a pre-order iterator for the tree.
+
+        This must be implemented by the concrete subclass.
+        """
+        raise NotImplementedError
+
+    def replace(self, new: Union[NL, list[NL]]) -> None:
+        """Replace this node with a new one in the parent."""
+        assert self.parent is not None, str(self)
+        assert new is not None
+        if not isinstance(new, list):
+            new = [new]
+        l_children = []
+        found = False
+        for ch in self.parent.children:
+            if ch is self:
+                assert not found, (self.parent.children, self, new)
+                if new is not None:
+                    l_children.extend(new)
+                found = True
+            else:
+                l_children.append(ch)
+        assert found, (self.children, self, new)
+        self.parent.children = l_children
+        self.parent.changed()
+        self.parent.invalidate_sibling_maps()
+        for x in new:
+            x.parent = self.parent
+        self.parent = None
+
+    def get_lineno(self) -> Optional[int]:
+        """Return the line number which generated the invocant node."""
+        node = self
+        while not isinstance(node, Leaf):
+            if not node.children:
+                return None
+            node = node.children[0]
+        return node.lineno
+
+    def changed(self) -> None:
+        if self.was_changed:
+            return
+        if self.parent:
+            self.parent.changed()
+        self.was_changed = True
+
+    def remove(self) -> Optional[int]:
+        """
+        Remove the node from the tree. Returns the position of the node in its
+        parent's children before it was removed.
+        """
+        if self.parent:
+            for i, node in enumerate(self.parent.children):
+                if node is self:
+                    del self.parent.children[i]
+                    self.parent.changed()
+                    self.parent.invalidate_sibling_maps()
+                    self.parent = None
+                    return i
+        return None
+
+    @property
+    def next_sibling(self) -> Optional[NL]:
+        """
+        The node immediately following the invocant in their parent's children
+        list. If the invocant does not have a next sibling, it is None
+        """
+        if self.parent is None:
+            return None
+
+        if self.parent.next_sibling_map is None:
+            self.parent.update_sibling_maps()
+        assert self.parent.next_sibling_map is not None
+        return self.parent.next_sibling_map[id(self)]
+
+    @property
+    def prev_sibling(self) -> Optional[NL]:
+        """
+        The node immediately preceding the invocant in their parent's children
+        list. If the invocant does not have a previous sibling, it is None.
+        """
+        if self.parent is None:
+            return None
+
+        if self.parent.prev_sibling_map is None:
+            self.parent.update_sibling_maps()
+        assert self.parent.prev_sibling_map is not None
+        return self.parent.prev_sibling_map[id(self)]
+
+    def leaves(self) -> Iterator["Leaf"]:
+        for child in self.children:
+            yield from child.leaves()
+
+    def depth(self) -> int:
+        if self.parent is None:
+            return 0
+        return 1 + self.parent.depth()
+
+    def get_suffix(self) -> str:
+        """
+        Return the string immediately following the invocant node. This is
+        effectively equivalent to node.next_sibling.prefix
+        """
+        next_sib = self.next_sibling
+        if next_sib is None:
+            return ""
+        prefix = next_sib.prefix
+        return prefix
+
+
+class Node(Base):
+    """Concrete implementation for interior nodes."""
+
+    fixers_applied: Optional[list[Any]]
+    used_names: Optional[set[str]]
+
+    def __init__(
+        self,
+        type: int,
+        children: list[NL],
+        context: Optional[Any] = None,
+        prefix: Optional[str] = None,
+        fixers_applied: Optional[list[Any]] = None,
+    ) -> None:
+        """
+        Initializer.
+
+        Takes a type constant (a symbol number >= 256), a sequence of
+        child nodes, and an optional context keyword argument.
+
+        As a side effect, the parent pointers of the children are updated.
+        """
+        assert type >= 256, type
+        self.type = type
+        self.children = list(children)
+        for ch in self.children:
+            assert ch.parent is None, repr(ch)
+            ch.parent = self
+        self.invalidate_sibling_maps()
+        if prefix is not None:
+            self.prefix = prefix
+        if fixers_applied:
+            self.fixers_applied = fixers_applied[:]
+        else:
+            self.fixers_applied = None
+
+    def __repr__(self) -> str:
+        """Return a canonical string representation."""
+        assert self.type is not None
+        return "{}({}, {!r})".format(
+            self.__class__.__name__,
+            type_repr(self.type),
+            self.children,
+        )
+
+    def __str__(self) -> str:
+        """
+        Return a pretty string representation.
+
+        This reproduces the input source exactly.
+        """
+        return "".join(map(str, self.children))
+
+    def _eq(self, other: Base) -> bool:
+        """Compare two nodes for equality."""
+        return (self.type, self.children) == (other.type, other.children)
+
+    def clone(self) -> "Node":
+        assert self.type is not None
+        """Return a cloned (deep) copy of self."""
+        return Node(
+            self.type,
+            [ch.clone() for ch in self.children],
+            fixers_applied=self.fixers_applied,
+        )
+
+    def post_order(self) -> Iterator[NL]:
+        """Return a post-order iterator for the tree."""
+        for child in self.children:
+            yield from child.post_order()
+        yield self
+
+    def pre_order(self) -> Iterator[NL]:
+        """Return a pre-order iterator for the tree."""
+        yield self
+        for child in self.children:
+            yield from child.pre_order()
+
+    @property
+    def prefix(self) -> str:
+        """
+        The whitespace and comments preceding this node in the input.
+        """
+        if not self.children:
+            return ""
+        return self.children[0].prefix
+
+    @prefix.setter
+    def prefix(self, prefix: str) -> None:
+        if self.children:
+            self.children[0].prefix = prefix
+
+    def set_child(self, i: int, child: NL) -> None:
+        """
+        Equivalent to 'node.children[i] = child'. This method also sets the
+        child's parent attribute appropriately.
+        """
+        child.parent = self
+        self.children[i].parent = None
+        self.children[i] = child
+        self.changed()
+        self.invalidate_sibling_maps()
+
+    def insert_child(self, i: int, child: NL) -> None:
+        """
+        Equivalent to 'node.children.insert(i, child)'. This method also sets
+        the child's parent attribute appropriately.
+        """
+        child.parent = self
+        self.children.insert(i, child)
+        self.changed()
+        self.invalidate_sibling_maps()
+
+    def append_child(self, child: NL) -> None:
+        """
+        Equivalent to 'node.children.append(child)'. This method also sets the
+        child's parent attribute appropriately.
+        """
+        child.parent = self
+        self.children.append(child)
+        self.changed()
+        self.invalidate_sibling_maps()
+
+    def invalidate_sibling_maps(self) -> None:
+        self.prev_sibling_map: Optional[dict[int, Optional[NL]]] = None
+        self.next_sibling_map: Optional[dict[int, Optional[NL]]] = None
+
+    def update_sibling_maps(self) -> None:
+        _prev: dict[int, Optional[NL]] = {}
+        _next: dict[int, Optional[NL]] = {}
+        self.prev_sibling_map = _prev
+        self.next_sibling_map = _next
+        previous: Optional[NL] = None
+        for current in self.children:
+            _prev[id(current)] = previous
+            _next[id(previous)] = current
+            previous = current
+        _next[id(current)] = None
+
+
+class Leaf(Base):
+    """Concrete implementation for leaf nodes."""
+
+    # Default values for instance variables
+    value: str
+    fixers_applied: list[Any]
+    bracket_depth: int
+    # Changed later in brackets.py
+    opening_bracket: Optional["Leaf"] = None
+    used_names: Optional[set[str]]
+    _prefix = ""  # Whitespace and comments preceding this token in the input
+    lineno: int = 0  # Line where this token starts in the input
+    column: int = 0  # Column where this token starts in the input
+    # If not None, this Leaf is created by converting a block of fmt off/skip
+    # code, and `fmt_pass_converted_first_leaf` points to the first Leaf in the
+    # converted code.
+    fmt_pass_converted_first_leaf: Optional["Leaf"] = None
+
+    def __init__(
+        self,
+        type: int,
+        value: str,
+        context: Optional[Context] = None,
+        prefix: Optional[str] = None,
+        fixers_applied: list[Any] = [],
+        opening_bracket: Optional["Leaf"] = None,
+        fmt_pass_converted_first_leaf: Optional["Leaf"] = None,
+    ) -> None:
+        """
+        Initializer.
+
+        Takes a type constant (a token number < 256), a string value, and an
+        optional context keyword argument.
+        """
+
+        assert 0 <= type < 256, type
+        if context is not None:
+            self._prefix, (self.lineno, self.column) = context
+        self.type = type
+        self.value = value
+        if prefix is not None:
+            self._prefix = prefix
+        self.fixers_applied: Optional[list[Any]] = fixers_applied[:]
+        self.children = []
+        self.opening_bracket = opening_bracket
+        self.fmt_pass_converted_first_leaf = fmt_pass_converted_first_leaf
+
+    def __repr__(self) -> str:
+        """Return a canonical string representation."""
+        from .pgen2.token import tok_name
+
+        assert self.type is not None
+        return "{}({}, {!r})".format(
+            self.__class__.__name__,
+            tok_name.get(self.type, self.type),
+            self.value,
+        )
+
+    def __str__(self) -> str:
+        """
+        Return a pretty string representation.
+
+        This reproduces the input source exactly.
+        """
+        return self._prefix + str(self.value)
+
+    def _eq(self, other: "Leaf") -> bool:
+        """Compare two nodes for equality."""
+        return (self.type, self.value) == (other.type, other.value)
+
+    def clone(self) -> "Leaf":
+        assert self.type is not None
+        """Return a cloned (deep) copy of self."""
+        return Leaf(
+            self.type,
+            self.value,
+            (self.prefix, (self.lineno, self.column)),
+            fixers_applied=self.fixers_applied,
+        )
+
+    def leaves(self) -> Iterator["Leaf"]:
+        yield self
+
+    def post_order(self) -> Iterator["Leaf"]:
+        """Return a post-order iterator for the tree."""
+        yield self
+
+    def pre_order(self) -> Iterator["Leaf"]:
+        """Return a pre-order iterator for the tree."""
+        yield self
+
+    @property
+    def prefix(self) -> str:
+        """
+        The whitespace and comments preceding this token in the input.
+        """
+        return self._prefix
+
+    @prefix.setter
+    def prefix(self, prefix: str) -> None:
+        self.changed()
+        self._prefix = prefix
+
+
+def convert(gr: Grammar, raw_node: RawNode) -> NL:
+    """
+    Convert raw node information to a Node or Leaf instance.
+
+    This is passed to the parser driver which calls it whenever a reduction of a
+    grammar rule produces a new complete node, so that the tree is build
+    strictly bottom-up.
+    """
+    type, value, context, children = raw_node
+    if children or type in gr.number2symbol:
+        # If there's exactly one child, return that child instead of
+        # creating a new node.
+        assert children is not None
+        if len(children) == 1:
+            return children[0]
+        return Node(type, children, context=context)
+    else:
+        return Leaf(type, value or "", context=context)
+
+
+_Results = dict[str, NL]
+
+
+class BasePattern:
+    """
+    A pattern is a tree matching pattern.
+
+    It looks for a specific node type (token or symbol), and
+    optionally for a specific content.
+
+    This is an abstract base class.  There are three concrete
+    subclasses:
+
+    - LeafPattern matches a single leaf node;
+    - NodePattern matches a single node (usually non-leaf);
+    - WildcardPattern matches a sequence of nodes of variable length.
+    """
+
+    # Defaults for instance variables
+    type: Optional[int]
+    type = None  # Node type (token if < 256, symbol if >= 256)
+    content: Any = None  # Optional content matching pattern
+    name: Optional[str] = None  # Optional name used to store match in results dict
+
+    def __new__(cls, *args, **kwds):
+        """Constructor that prevents BasePattern from being instantiated."""
+        assert cls is not BasePattern, "Cannot instantiate BasePattern"
+        return object.__new__(cls)
+
+    def __repr__(self) -> str:
+        assert self.type is not None
+        args = [type_repr(self.type), self.content, self.name]
+        while args and args[-1] is None:
+            del args[-1]
+        return "{}({})".format(self.__class__.__name__, ", ".join(map(repr, args)))
+
+    def _submatch(self, node, results=None) -> bool:
+        raise NotImplementedError
+
+    def optimize(self) -> "BasePattern":
+        """
+        A subclass can define this as a hook for optimizations.
+
+        Returns either self or another node with the same effect.
+        """
+        return self
+
+    def match(self, node: NL, results: Optional[_Results] = None) -> bool:
+        """
+        Does this pattern exactly match a node?
+
+        Returns True if it matches, False if not.
+
+        If results is not None, it must be a dict which will be
+        updated with the nodes matching named subpatterns.
+
+        Default implementation for non-wildcard patterns.
+        """
+        if self.type is not None and node.type != self.type:
+            return False
+        if self.content is not None:
+            r: Optional[_Results] = None
+            if results is not None:
+                r = {}
+            if not self._submatch(node, r):
+                return False
+            if r:
+                assert results is not None
+                results.update(r)
+        if results is not None and self.name:
+            results[self.name] = node
+        return True
+
+    def match_seq(self, nodes: list[NL], results: Optional[_Results] = None) -> bool:
+        """
+        Does this pattern exactly match a sequence of nodes?
+
+        Default implementation for non-wildcard patterns.
+        """
+        if len(nodes) != 1:
+            return False
+        return self.match(nodes[0], results)
+
+    def generate_matches(self, nodes: list[NL]) -> Iterator[tuple[int, _Results]]:
+        """
+        Generator yielding all matches for this pattern.
+
+        Default implementation for non-wildcard patterns.
+        """
+        r: _Results = {}
+        if nodes and self.match(nodes[0], r):
+            yield 1, r
+
+
+class LeafPattern(BasePattern):
+    def __init__(
+        self,
+        type: Optional[int] = None,
+        content: Optional[str] = None,
+        name: Optional[str] = None,
+    ) -> None:
+        """
+        Initializer.  Takes optional type, content, and name.
+
+        The type, if given must be a token type (< 256).  If not given,
+        this matches any *leaf* node; the content may still be required.
+
+        The content, if given, must be a string.
+
+        If a name is given, the matching node is stored in the results
+        dict under that key.
+        """
+        if type is not None:
+            assert 0 <= type < 256, type
+        if content is not None:
+            assert isinstance(content, str), repr(content)
+        self.type = type
+        self.content = content
+        self.name = name
+
+    def match(self, node: NL, results=None) -> bool:
+        """Override match() to insist on a leaf node."""
+        if not isinstance(node, Leaf):
+            return False
+        return BasePattern.match(self, node, results)
+
+    def _submatch(self, node, results=None):
+        """
+        Match the pattern's content to the node's children.
+
+        This assumes the node type matches and self.content is not None.
+
+        Returns True if it matches, False if not.
+
+        If results is not None, it must be a dict which will be
+        updated with the nodes matching named subpatterns.
+
+        When returning False, the results dict may still be updated.
+        """
+        return self.content == node.value
+
+
+class NodePattern(BasePattern):
+    wildcards: bool = False
+
+    def __init__(
+        self,
+        type: Optional[int] = None,
+        content: Optional[Iterable[str]] = None,
+        name: Optional[str] = None,
+    ) -> None:
+        """
+        Initializer.  Takes optional type, content, and name.
+
+        The type, if given, must be a symbol type (>= 256).  If the
+        type is None this matches *any* single node (leaf or not),
+        except if content is not None, in which it only matches
+        non-leaf nodes that also match the content pattern.
+
+        The content, if not None, must be a sequence of Patterns that
+        must match the node's children exactly.  If the content is
+        given, the type must not be None.
+
+        If a name is given, the matching node is stored in the results
+        dict under that key.
+        """
+        if type is not None:
+            assert type >= 256, type
+        if content is not None:
+            assert not isinstance(content, str), repr(content)
+            newcontent = list(content)
+            for i, item in enumerate(newcontent):
+                assert isinstance(item, BasePattern), (i, item)
+                # I don't even think this code is used anywhere, but it does cause
+                # unreachable errors from mypy. This function's signature does look
+                # odd though *shrug*.
+                if isinstance(item, WildcardPattern):  # type: ignore[unreachable]
+                    self.wildcards = True  # type: ignore[unreachable]
+        self.type = type
+        self.content = newcontent  # TODO: this is unbound when content is None
+        self.name = name
+
+    def _submatch(self, node, results=None) -> bool:
+        """
+        Match the pattern's content to the node's children.
+
+        This assumes the node type matches and self.content is not None.
+
+        Returns True if it matches, False if not.
+
+        If results is not None, it must be a dict which will be
+        updated with the nodes matching named subpatterns.
+
+        When returning False, the results dict may still be updated.
+        """
+        if self.wildcards:
+            for c, r in generate_matches(self.content, node.children):
+                if c == len(node.children):
+                    if results is not None:
+                        results.update(r)
+                    return True
+            return False
+        if len(self.content) != len(node.children):
+            return False
+        for subpattern, child in zip(self.content, node.children):
+            if not subpattern.match(child, results):
+                return False
+        return True
+
+
+class WildcardPattern(BasePattern):
+    """
+    A wildcard pattern can match zero or more nodes.
+
+    This has all the flexibility needed to implement patterns like:
+
+    .*      .+      .?      .{m,n}
+    (a b c | d e | f)
+    (...)*  (...)+  (...)?  (...){m,n}
+
+    except it always uses non-greedy matching.
+    """
+
+    min: int
+    max: int
+
+    def __init__(
+        self,
+        content: Optional[str] = None,
+        min: int = 0,
+        max: int = HUGE,
+        name: Optional[str] = None,
+    ) -> None:
+        """
+        Initializer.
+
+        Args:
+            content: optional sequence of subsequences of patterns;
+                     if absent, matches one node;
+                     if present, each subsequence is an alternative [*]
+            min: optional minimum number of times to match, default 0
+            max: optional maximum number of times to match, default HUGE
+            name: optional name assigned to this match
+
+        [*] Thus, if content is [[a, b, c], [d, e], [f, g, h]] this is
+            equivalent to (a b c | d e | f g h); if content is None,
+            this is equivalent to '.' in regular expression terms.
+            The min and max parameters work as follows:
+                min=0, max=maxint: .*
+                min=1, max=maxint: .+
+                min=0, max=1: .?
+                min=1, max=1: .
+            If content is not None, replace the dot with the parenthesized
+            list of alternatives, e.g. (a b c | d e | f g h)*
+        """
+        assert 0 <= min <= max <= HUGE, (min, max)
+        if content is not None:
+            f = lambda s: tuple(s)
+            wrapped_content = tuple(map(f, content))  # Protect against alterations
+            # Check sanity of alternatives
+            assert len(wrapped_content), repr(
+                wrapped_content
+            )  # Can't have zero alternatives
+            for alt in wrapped_content:
+                assert len(alt), repr(alt)  # Can have empty alternatives
+        self.content = wrapped_content
+        self.min = min
+        self.max = max
+        self.name = name
+
+    def optimize(self) -> Any:
+        """Optimize certain stacked wildcard patterns."""
+        subpattern = None
+        if (
+            self.content is not None
+            and len(self.content) == 1
+            and len(self.content[0]) == 1
+        ):
+            subpattern = self.content[0][0]
+        if self.min == 1 and self.max == 1:
+            if self.content is None:
+                return NodePattern(name=self.name)
+            if subpattern is not None and self.name == subpattern.name:
+                return subpattern.optimize()
+        if (
+            self.min <= 1
+            and isinstance(subpattern, WildcardPattern)
+            and subpattern.min <= 1
+            and self.name == subpattern.name
+        ):
+            return WildcardPattern(
+                subpattern.content,
+                self.min * subpattern.min,
+                self.max * subpattern.max,
+                subpattern.name,
+            )
+        return self
+
+    def match(self, node, results=None) -> bool:
+        """Does this pattern exactly match a node?"""
+        return self.match_seq([node], results)
+
+    def match_seq(self, nodes, results=None) -> bool:
+        """Does this pattern exactly match a sequence of nodes?"""
+        for c, r in self.generate_matches(nodes):
+            if c == len(nodes):
+                if results is not None:
+                    results.update(r)
+                    if self.name:
+                        results[self.name] = list(nodes)
+                return True
+        return False
+
+    def generate_matches(self, nodes) -> Iterator[tuple[int, _Results]]:
+        """
+        Generator yielding matches for a sequence of nodes.
+
+        Args:
+            nodes: sequence of nodes
+
+        Yields:
+            (count, results) tuples where:
+            count: the match comprises nodes[:count];
+            results: dict containing named submatches.
+        """
+        if self.content is None:
+            # Shortcut for special case (see __init__.__doc__)
+            for count in range(self.min, 1 + min(len(nodes), self.max)):
+                r = {}
+                if self.name:
+                    r[self.name] = nodes[:count]
+                yield count, r
+        elif self.name == "bare_name":
+            yield self._bare_name_matches(nodes)
+        else:
+            # The reason for this is that hitting the recursion limit usually
+            # results in some ugly messages about how RuntimeErrors are being
+            # ignored. We only have to do this on CPython, though, because other
+            # implementations don't have this nasty bug in the first place.
+            if hasattr(sys, "getrefcount"):
+                save_stderr = sys.stderr
+                sys.stderr = StringIO()
+            try:
+                for count, r in self._recursive_matches(nodes, 0):
+                    if self.name:
+                        r[self.name] = nodes[:count]
+                    yield count, r
+            except RuntimeError:
+                # We fall back to the iterative pattern matching scheme if the recursive
+                # scheme hits the recursion limit.
+                for count, r in self._iterative_matches(nodes):
+                    if self.name:
+                        r[self.name] = nodes[:count]
+                    yield count, r
+            finally:
+                if hasattr(sys, "getrefcount"):
+                    sys.stderr = save_stderr
+
+    def _iterative_matches(self, nodes) -> Iterator[tuple[int, _Results]]:
+        """Helper to iteratively yield the matches."""
+        nodelen = len(nodes)
+        if 0 >= self.min:
+            yield 0, {}
+
+        results = []
+        # generate matches that use just one alt from self.content
+        for alt in self.content:
+            for c, r in generate_matches(alt, nodes):
+                yield c, r
+                results.append((c, r))
+
+        # for each match, iterate down the nodes
+        while results:
+            new_results = []
+            for c0, r0 in results:
+                # stop if the entire set of nodes has been matched
+                if c0 < nodelen and c0 <= self.max:
+                    for alt in self.content:
+                        for c1, r1 in generate_matches(alt, nodes[c0:]):
+                            if c1 > 0:
+                                r = {}
+                                r.update(r0)
+                                r.update(r1)
+                                yield c0 + c1, r
+                                new_results.append((c0 + c1, r))
+            results = new_results
+
+    def _bare_name_matches(self, nodes) -> tuple[int, _Results]:
+        """Special optimized matcher for bare_name."""
+        count = 0
+        r = {}  # type: _Results
+        done = False
+        max = len(nodes)
+        while not done and count < max:
+            done = True
+            for leaf in self.content:
+                if leaf[0].match(nodes[count], r):
+                    count += 1
+                    done = False
+                    break
+        assert self.name is not None
+        r[self.name] = nodes[:count]
+        return count, r
+
+    def _recursive_matches(self, nodes, count) -> Iterator[tuple[int, _Results]]:
+        """Helper to recursively yield the matches."""
+        assert self.content is not None
+        if count >= self.min:
+            yield 0, {}
+        if count < self.max:
+            for alt in self.content:
+                for c0, r0 in generate_matches(alt, nodes):
+                    for c1, r1 in self._recursive_matches(nodes[c0:], count + 1):
+                        r = {}
+                        r.update(r0)
+                        r.update(r1)
+                        yield c0 + c1, r
+
+
+class NegatedPattern(BasePattern):
+    def __init__(self, content: Optional[BasePattern] = None) -> None:
+        """
+        Initializer.
+
+        The argument is either a pattern or None.  If it is None, this
+        only matches an empty sequence (effectively '$' in regex
+        lingo).  If it is not None, this matches whenever the argument
+        pattern doesn't have any matches.
+        """
+        if content is not None:
+            assert isinstance(content, BasePattern), repr(content)
+        self.content = content
+
+    def match(self, node, results=None) -> bool:
+        # We never match a node in its entirety
+        return False
+
+    def match_seq(self, nodes, results=None) -> bool:
+        # We only match an empty sequence of nodes in its entirety
+        return len(nodes) == 0
+
+    def generate_matches(self, nodes: list[NL]) -> Iterator[tuple[int, _Results]]:
+        if self.content is None:
+            # Return a match if there is an empty sequence
+            if len(nodes) == 0:
+                yield 0, {}
+        else:
+            # Return a match if the argument pattern has no matches
+            for c, r in self.content.generate_matches(nodes):
+                return
+            yield 0, {}
+
+
+def generate_matches(
+    patterns: list[BasePattern], nodes: list[NL]
+) -> Iterator[tuple[int, _Results]]:
+    """
+    Generator yielding matches for a sequence of patterns and nodes.
+
+    Args:
+        patterns: a sequence of patterns
+        nodes: a sequence of nodes
+
+    Yields:
+        (count, results) tuples where:
+        count: the entire sequence of patterns matches nodes[:count];
+        results: dict containing named submatches.
+    """
+    if not patterns:
+        yield 0, {}
+    else:
+        p, rest = patterns[0], patterns[1:]
+        for c0, r0 in p.generate_matches(nodes):
+            if not rest:
+                yield c0, r0
+            else:
+                for c1, r1 in generate_matches(rest, nodes[c0:]):
+                    r = {}
+                    r.update(r0)
+                    r.update(r1)
+                    yield c0 + c1, r

openflamingo/lib/python3.10/site-packages/fontTools/__pycache__/agl.cpython-310.pyc

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1904f7b41f710b379e929c4ef367b9b8535a6b0ecf6c74e35ef080f299a23680
+size 111024

openflamingo/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn.h

@@ -0,0 +1,78 @@
+/*
+ * Copyright 2017-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn : Neural Networks Library
+
+*/
+
+#if !defined(CUDNN_H_)
+#define CUDNN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_adv_infer.h"
+#include "cudnn_adv_train.h"
+#include "cudnn_cnn_infer.h"
+#include "cudnn_cnn_train.h"
+
+#include "cudnn_backend.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_H_ */

openflamingo/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_backend_v8.h

@@ -0,0 +1,566 @@
+/*
+ * Copyright 2017-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+#ifndef _CUDNN_BACKEND_H_
+#define _CUDNN_BACKEND_H_
+
+/*
+ * The content in this header file is under development to be included in cudnn.h in the future
+ * Production code should have all include of this header file remove.
+ */
+
+#include "cudnn_ops_infer.h"
+#include "cudnn_cnn_infer.h"
+
+/* NOTE: definition in extern "C" to be copied later to public header */
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+typedef void *cudnnBackendDescriptor_t;
+
+typedef struct cudnnFractionStruct {
+    int64_t numerator;
+    int64_t denominator;
+} cudnnFraction_t;
+
+typedef enum {
+    CUDNN_POINTWISE_ADD        = 0,
+    CUDNN_POINTWISE_ADD_SQUARE = 5,
+    CUDNN_POINTWISE_DIV        = 6,
+    CUDNN_POINTWISE_MAX        = 3,
+    CUDNN_POINTWISE_MIN        = 2,
+    CUDNN_POINTWISE_MOD        = 7,
+    CUDNN_POINTWISE_MUL        = 1,
+    CUDNN_POINTWISE_POW        = 8,
+    CUDNN_POINTWISE_SUB        = 9,
+
+    CUDNN_POINTWISE_ABS      = 10,
+    CUDNN_POINTWISE_CEIL     = 11,
+    CUDNN_POINTWISE_COS      = 12,
+    CUDNN_POINTWISE_EXP      = 13,
+    CUDNN_POINTWISE_FLOOR    = 14,
+    CUDNN_POINTWISE_LOG      = 15,
+    CUDNN_POINTWISE_NEG      = 16,
+    CUDNN_POINTWISE_RSQRT    = 17,
+    CUDNN_POINTWISE_SIN      = 18,
+    CUDNN_POINTWISE_SQRT     = 4,
+    CUDNN_POINTWISE_TAN      = 19,
+    CUDNN_POINTWISE_ERF      = 20,
+    CUDNN_POINTWISE_IDENTITY = 21,
+
+    CUDNN_POINTWISE_RELU_FWD             = 100,
+    CUDNN_POINTWISE_TANH_FWD             = 101,
+    CUDNN_POINTWISE_SIGMOID_FWD          = 102,
+    CUDNN_POINTWISE_ELU_FWD              = 103,
+    CUDNN_POINTWISE_GELU_FWD             = 104,
+    CUDNN_POINTWISE_SOFTPLUS_FWD         = 105,
+    CUDNN_POINTWISE_SWISH_FWD            = 106,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_FWD = 107,
+
+    CUDNN_POINTWISE_RELU_BWD             = 200,
+    CUDNN_POINTWISE_TANH_BWD             = 201,
+    CUDNN_POINTWISE_SIGMOID_BWD          = 202,
+    CUDNN_POINTWISE_ELU_BWD              = 203,
+    CUDNN_POINTWISE_GELU_BWD             = 204,
+    CUDNN_POINTWISE_SOFTPLUS_BWD         = 205,
+    CUDNN_POINTWISE_SWISH_BWD            = 206,
+    CUDNN_POINTWISE_GELU_APPROX_TANH_BWD = 207,
+
+    CUDNN_POINTWISE_CMP_EQ  = 300,
+    CUDNN_POINTWISE_CMP_NEQ = 301,
+    CUDNN_POINTWISE_CMP_GT  = 302,
+    CUDNN_POINTWISE_CMP_GE  = 303,
+    CUDNN_POINTWISE_CMP_LT  = 304,
+    CUDNN_POINTWISE_CMP_LE  = 305,
+
+    CUDNN_POINTWISE_LOGICAL_AND = 400,
+    CUDNN_POINTWISE_LOGICAL_OR  = 401,
+    CUDNN_POINTWISE_LOGICAL_NOT = 402,
+
+    CUDNN_POINTWISE_GEN_INDEX = 501,
+
+    CUDNN_POINTWISE_BINARY_SELECT = 601,
+} cudnnPointwiseMode_t;
+
+typedef enum {
+    CUDNN_RESAMPLE_NEAREST  = 0,
+    CUDNN_RESAMPLE_BILINEAR = 1,
+    CUDNN_RESAMPLE_AVGPOOL  = 2,
+    CUDNN_RESAMPLE_MAXPOOL  = 3,
+} cudnnResampleMode_t;
+
+typedef enum {
+    CUDNN_SIGNAL_SET  = 0,
+    CUDNN_SIGNAL_WAIT = 1,
+} cudnnSignalMode_t;
+
+typedef enum {
+    CUDNN_GENSTATS_SUM_SQSUM = 0,
+} cudnnGenStatsMode_t;
+
+typedef enum {
+    CUDNN_BN_FINALIZE_STATISTICS_TRAINING  = 0,
+    CUDNN_BN_FINALIZE_STATISTICS_INFERENCE = 1,
+} cudnnBnFinalizeStatsMode_t;
+
+typedef enum {
+    CUDNN_ATTR_POINTWISE_MODE                  = 0,
+    CUDNN_ATTR_POINTWISE_MATH_PREC             = 1,
+    CUDNN_ATTR_POINTWISE_NAN_PROPAGATION       = 2,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP       = 3,
+    CUDNN_ATTR_POINTWISE_RELU_UPPER_CLIP       = 4,
+    CUDNN_ATTR_POINTWISE_RELU_LOWER_CLIP_SLOPE = 5,
+    CUDNN_ATTR_POINTWISE_ELU_ALPHA             = 6,
+    CUDNN_ATTR_POINTWISE_SOFTPLUS_BETA         = 7,
+    CUDNN_ATTR_POINTWISE_SWISH_BETA            = 8,
+    CUDNN_ATTR_POINTWISE_AXIS                  = 9,
+
+    CUDNN_ATTR_CONVOLUTION_COMP_TYPE      = 100,
+    CUDNN_ATTR_CONVOLUTION_CONV_MODE      = 101,
+    CUDNN_ATTR_CONVOLUTION_DILATIONS      = 102,
+    CUDNN_ATTR_CONVOLUTION_FILTER_STRIDES = 103,
+    CUDNN_ATTR_CONVOLUTION_POST_PADDINGS  = 104,
+    CUDNN_ATTR_CONVOLUTION_PRE_PADDINGS   = 105,
+    CUDNN_ATTR_CONVOLUTION_SPATIAL_DIMS   = 106,
+
+    CUDNN_ATTR_ENGINEHEUR_MODE            = 200,
+    CUDNN_ATTR_ENGINEHEUR_OPERATION_GRAPH = 201,
+    CUDNN_ATTR_ENGINEHEUR_RESULTS         = 202,
+
+    CUDNN_ATTR_ENGINECFG_ENGINE            = 300,
+    CUDNN_ATTR_ENGINECFG_INTERMEDIATE_INFO = 301,
+    CUDNN_ATTR_ENGINECFG_KNOB_CHOICES      = 302,
+
+    CUDNN_ATTR_EXECUTION_PLAN_HANDLE                     = 400,
+    CUDNN_ATTR_EXECUTION_PLAN_ENGINE_CONFIG              = 401,
+    CUDNN_ATTR_EXECUTION_PLAN_WORKSPACE_SIZE             = 402,
+    CUDNN_ATTR_EXECUTION_PLAN_COMPUTED_INTERMEDIATE_UIDS = 403,
+    CUDNN_ATTR_EXECUTION_PLAN_RUN_ONLY_INTERMEDIATE_UIDS = 404,
+    CUDNN_ATTR_EXECUTION_PLAN_JSON_REPRESENTATION        = 405,
+
+    CUDNN_ATTR_INTERMEDIATE_INFO_UNIQUE_ID            = 500,
+    CUDNN_ATTR_INTERMEDIATE_INFO_SIZE                 = 501,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_DATA_UIDS  = 502,
+    CUDNN_ATTR_INTERMEDIATE_INFO_DEPENDENT_ATTRIBUTES = 503,
+
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_TYPE  = 600,
+    CUDNN_ATTR_KNOB_CHOICE_KNOB_VALUE = 601,
+
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_ALPHA        = 700,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_BETA         = 701,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_CONV_DESC    = 702,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_W            = 703,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_X            = 704,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_FORWARD_Y            = 705,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_ALPHA       = 706,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_BETA        = 707,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_CONV_DESC   = 708,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_W           = 709,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DX          = 710,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_DATA_DY          = 711,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_ALPHA     = 712,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_BETA      = 713,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_CONV_DESC = 714,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DW        = 715,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_X         = 716,
+    CUDNN_ATTR_OPERATION_CONVOLUTION_BWD_FILTER_DY        = 717,
+
+    CUDNN_ATTR_OPERATION_POINTWISE_PW_DESCRIPTOR = 750,
+    CUDNN_ATTR_OPERATION_POINTWISE_XDESC         = 751,
+    CUDNN_ATTR_OPERATION_POINTWISE_BDESC         = 752,
+    CUDNN_ATTR_OPERATION_POINTWISE_YDESC         = 753,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA1        = 754,
+    CUDNN_ATTR_OPERATION_POINTWISE_ALPHA2        = 755,
+    CUDNN_ATTR_OPERATION_POINTWISE_DXDESC        = 756,
+    CUDNN_ATTR_OPERATION_POINTWISE_DYDESC        = 757,
+    CUDNN_ATTR_OPERATION_POINTWISE_TDESC         = 758,
+
+    CUDNN_ATTR_OPERATION_GENSTATS_MODE      = 770,
+    CUDNN_ATTR_OPERATION_GENSTATS_MATH_PREC = 771,
+    CUDNN_ATTR_OPERATION_GENSTATS_XDESC     = 772,
+    CUDNN_ATTR_OPERATION_GENSTATS_SUMDESC   = 773,
+    CUDNN_ATTR_OPERATION_GENSTATS_SQSUMDESC = 774,
+
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_STATS_MODE                = 780,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_MATH_PREC                 = 781,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SUM_DESC                = 782,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_Y_SQ_SUM_DESC             = 783,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SCALE_DESC                = 784,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_BIAS_DESC                 = 785,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_MEAN_DESC    = 786,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_PREV_RUNNING_VAR_DESC     = 787,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_MEAN_DESC = 788,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_UPDATED_RUNNING_VAR_DESC  = 789,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_MEAN_DESC           = 790,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_SAVED_INV_STD_DESC        = 791,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_SCALE_DESC             = 792,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EQ_BIAS_DESC              = 793,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_ACCUM_COUNT_DESC          = 794,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EPSILON_DESC              = 795,
+    CUDNN_ATTR_OPERATION_BN_FINALIZE_EXP_AVERATE_FACTOR_DESC   = 796,
+
+    CUDNN_ATTR_OPERATIONGRAPH_HANDLE              = 800,
+    CUDNN_ATTR_OPERATIONGRAPH_OPS                 = 801,
+    CUDNN_ATTR_OPERATIONGRAPH_ENGINE_GLOBAL_COUNT = 802,
+
+    CUDNN_ATTR_TENSOR_BYTE_ALIGNMENT       = 900,
+    CUDNN_ATTR_TENSOR_DATA_TYPE            = 901,
+    CUDNN_ATTR_TENSOR_DIMENSIONS           = 902,
+    CUDNN_ATTR_TENSOR_STRIDES              = 903,
+    CUDNN_ATTR_TENSOR_VECTOR_COUNT         = 904,
+    CUDNN_ATTR_TENSOR_VECTORIZED_DIMENSION = 905,
+    CUDNN_ATTR_TENSOR_UNIQUE_ID            = 906,
+    CUDNN_ATTR_TENSOR_IS_VIRTUAL           = 907,
+    CUDNN_ATTR_TENSOR_IS_BY_VALUE          = 908,
+    CUDNN_ATTR_TENSOR_REORDERING_MODE      = 909,
+
+    CUDNN_ATTR_VARIANT_PACK_UNIQUE_IDS    = 1000,
+    CUDNN_ATTR_VARIANT_PACK_DATA_POINTERS = 1001,
+    CUDNN_ATTR_VARIANT_PACK_INTERMEDIATES = 1002,
+    CUDNN_ATTR_VARIANT_PACK_WORKSPACE     = 1003,
+
+    CUDNN_ATTR_LAYOUT_INFO_TENSOR_UID = 1100,
+    CUDNN_ATTR_LAYOUT_INFO_TYPES      = 1101,
+
+    CUDNN_ATTR_KNOB_INFO_TYPE          = 1200,
+    CUDNN_ATTR_KNOB_INFO_MAXIMUM_VALUE = 1201,
+    CUDNN_ATTR_KNOB_INFO_MINIMUM_VALUE = 1202,
+    CUDNN_ATTR_KNOB_INFO_STRIDE        = 1203,
+
+    CUDNN_ATTR_ENGINE_OPERATION_GRAPH = 1300,
+    CUDNN_ATTR_ENGINE_GLOBAL_INDEX    = 1301,
+    CUDNN_ATTR_ENGINE_KNOB_INFO       = 1302,
+    CUDNN_ATTR_ENGINE_NUMERICAL_NOTE  = 1303,
+    CUDNN_ATTR_ENGINE_LAYOUT_INFO     = 1304,
+    CUDNN_ATTR_ENGINE_BEHAVIOR_NOTE   = 1305,
+
+    CUDNN_ATTR_MATMUL_COMP_TYPE = 1500,
+
+    CUDNN_ATTR_OPERATION_MATMUL_ADESC                           = 1520,
+    CUDNN_ATTR_OPERATION_MATMUL_BDESC                           = 1521,
+    CUDNN_ATTR_OPERATION_MATMUL_CDESC                           = 1522,
+    CUDNN_ATTR_OPERATION_MATMUL_DESC                            = 1523,
+    CUDNN_ATTR_OPERATION_MATMUL_IRREGULARLY_STRIDED_BATCH_COUNT = 1524,
+
+    CUDNN_ATTR_REDUCTION_OPERATOR  = 1600,
+    CUDNN_ATTR_REDUCTION_COMP_TYPE = 1601,
+
+    CUDNN_ATTR_OPERATION_REDUCTION_XDESC = 1610,
+    CUDNN_ATTR_OPERATION_REDUCTION_YDESC = 1611,
+    CUDNN_ATTR_OPERATION_REDUCTION_DESC  = 1612,
+
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MATH_PREC        = 1620,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_MEAN_DESC        = 1621,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_INVSTD_DESC      = 1622,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_BN_SCALE_DESC    = 1623,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_X_DESC           = 1624,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DY_DESC          = 1625,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_SCALE_DESC   = 1626,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_DBN_BIAS_DESC    = 1627,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_DY_SCALE_DESC = 1628,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_X_SCALE_DESC  = 1629,
+    CUDNN_ATTR_OPERATION_BN_BWD_WEIGHTS_EQ_BIAS          = 1630,
+
+    CUDNN_ATTR_RESAMPLE_MODE            = 1700,
+    CUDNN_ATTR_RESAMPLE_COMP_TYPE       = 1701,
+    CUDNN_ATTR_RESAMPLE_SPATIAL_DIMS    = 1702,
+    CUDNN_ATTR_RESAMPLE_POST_PADDINGS   = 1703,
+    CUDNN_ATTR_RESAMPLE_PRE_PADDINGS    = 1704,
+    CUDNN_ATTR_RESAMPLE_STRIDES         = 1705,
+    CUDNN_ATTR_RESAMPLE_WINDOW_DIMS     = 1706,
+    CUDNN_ATTR_RESAMPLE_NAN_PROPAGATION = 1707,
+    CUDNN_ATTR_RESAMPLE_PADDING_MODE    = 1708,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_XDESC   = 1710,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_YDESC   = 1711,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_IDXDESC = 1712,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_ALPHA   = 1713,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_BETA    = 1714,
+    CUDNN_ATTR_OPERATION_RESAMPLE_FWD_DESC    = 1716,
+
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DXDESC  = 1720,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DYDESC  = 1721,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_IDXDESC = 1722,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_ALPHA   = 1723,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_BETA    = 1724,
+    CUDNN_ATTR_OPERATION_RESAMPLE_BWD_DESC    = 1725,
+
+    CUDNN_ATTR_OPERATION_CONCAT_AXIS          = 1800,
+    CUDNN_ATTR_OPERATION_CONCAT_INPUT_DESCS   = 1801,
+    CUDNN_ATTR_OPERATION_CONCAT_INPLACE_INDEX = 1802,
+    CUDNN_ATTR_OPERATION_CONCAT_OUTPUT_DESC   = 1803,
+
+    CUDNN_ATTR_OPERATION_SIGNAL_MODE     = 1900,
+    CUDNN_ATTR_OPERATION_SIGNAL_FLAGDESC = 1901,
+    CUDNN_ATTR_OPERATION_SIGNAL_VALUE    = 1902,
+    CUDNN_ATTR_OPERATION_SIGNAL_XDESC    = 1903,
+    CUDNN_ATTR_OPERATION_SIGNAL_YDESC    = 1904,
+
+    CUDNN_ATTR_OPERATION_NORM_FWD_MODE                     = 2000,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PHASE                    = 2001,
+    CUDNN_ATTR_OPERATION_NORM_FWD_XDESC                    = 2002,
+    CUDNN_ATTR_OPERATION_NORM_FWD_MEAN_DESC                = 2003,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INV_VARIANCE_DESC        = 2004,
+    CUDNN_ATTR_OPERATION_NORM_FWD_SCALE_DESC               = 2005,
+    CUDNN_ATTR_OPERATION_NORM_FWD_BIAS_DESC                = 2006,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EPSILON_DESC             = 2007,
+    CUDNN_ATTR_OPERATION_NORM_FWD_EXP_AVG_FACTOR_DESC      = 2008,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_MEAN_DESC  = 2009,
+    CUDNN_ATTR_OPERATION_NORM_FWD_INPUT_RUNNING_VAR_DESC   = 2010,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_MEAN_DESC = 2011,
+    CUDNN_ATTR_OPERATION_NORM_FWD_OUTPUT_RUNNING_VAR_DESC  = 2012,
+    CUDNN_ATTR_OPERATION_NORM_FWD_YDESC                    = 2013,
+    CUDNN_ATTR_OPERATION_NORM_FWD_PEER_STAT_DESCS          = 2014,
+
+    CUDNN_ATTR_OPERATION_NORM_BWD_MODE              = 2100,
+    CUDNN_ATTR_OPERATION_NORM_BWD_XDESC             = 2101,
+    CUDNN_ATTR_OPERATION_NORM_BWD_MEAN_DESC         = 2102,
+    CUDNN_ATTR_OPERATION_NORM_BWD_INV_VARIANCE_DESC = 2103,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DYDESC            = 2104,
+    CUDNN_ATTR_OPERATION_NORM_BWD_SCALE_DESC        = 2105,
+    CUDNN_ATTR_OPERATION_NORM_BWD_EPSILON_DESC      = 2106,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DSCALE_DESC       = 2107,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DBIAS_DESC        = 2108,
+    CUDNN_ATTR_OPERATION_NORM_BWD_DXDESC            = 2109,
+    CUDNN_ATTR_OPERATION_NORM_BWD_PEER_STAT_DESCS   = 2110,
+
+} cudnnBackendAttributeName_t;
+
+typedef enum {
+    CUDNN_TYPE_HANDLE = 0,
+    CUDNN_TYPE_DATA_TYPE,
+    CUDNN_TYPE_BOOLEAN,
+    CUDNN_TYPE_INT64,
+    CUDNN_TYPE_FLOAT,
+    CUDNN_TYPE_DOUBLE,
+    CUDNN_TYPE_VOID_PTR,
+    CUDNN_TYPE_CONVOLUTION_MODE,
+    CUDNN_TYPE_HEUR_MODE,
+    CUDNN_TYPE_KNOB_TYPE,
+    CUDNN_TYPE_NAN_PROPOGATION,
+    CUDNN_TYPE_NUMERICAL_NOTE,
+    CUDNN_TYPE_LAYOUT_TYPE,
+    CUDNN_TYPE_ATTRIB_NAME,
+    CUDNN_TYPE_POINTWISE_MODE,
+    CUDNN_TYPE_BACKEND_DESCRIPTOR,
+    CUDNN_TYPE_GENSTATS_MODE,
+    CUDNN_TYPE_BN_FINALIZE_STATS_MODE,
+    CUDNN_TYPE_REDUCTION_OPERATOR_TYPE,
+    CUDNN_TYPE_BEHAVIOR_NOTE,
+    CUDNN_TYPE_TENSOR_REORDERING_MODE,
+    CUDNN_TYPE_RESAMPLE_MODE,
+    CUDNN_TYPE_PADDING_MODE,
+    CUDNN_TYPE_INT32,
+    CUDNN_TYPE_CHAR,
+    CUDNN_TYPE_SIGNAL_MODE,
+    CUDNN_TYPE_FRACTION,
+    CUDNN_TYPE_NORM_MODE,
+    CUDNN_TYPE_NORM_FWD_PHASE,
+} cudnnBackendAttributeType_t;
+
+typedef enum {
+    CUDNN_BACKEND_POINTWISE_DESCRIPTOR = 0,
+    CUDNN_BACKEND_CONVOLUTION_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINE_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINECFG_DESCRIPTOR,
+    CUDNN_BACKEND_ENGINEHEUR_DESCRIPTOR,
+    CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR,
+    CUDNN_BACKEND_INTERMEDIATE_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_KNOB_CHOICE_DESCRIPTOR,
+    CUDNN_BACKEND_KNOB_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_LAYOUT_INFO_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_FORWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_FILTER_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONVOLUTION_BACKWARD_DATA_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_POINTWISE_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_GEN_STATS_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATIONGRAPH_DESCRIPTOR,
+    CUDNN_BACKEND_VARIANT_PACK_DESCRIPTOR,
+    CUDNN_BACKEND_TENSOR_DESCRIPTOR,
+    CUDNN_BACKEND_MATMUL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_MATMUL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_BN_FINALIZE_STATISTICS_DESCRIPTOR,
+    CUDNN_BACKEND_REDUCTION_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_REDUCTION_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_BN_BWD_WEIGHTS_DESCRIPTOR,
+    CUDNN_BACKEND_RESAMPLE_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_FWD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_RESAMPLE_BWD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_CONCAT_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_SIGNAL_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_NORM_FORWARD_DESCRIPTOR,
+    CUDNN_BACKEND_OPERATION_NORM_BACKWARD_DESCRIPTOR,
+} cudnnBackendDescriptorType_t;
+
+typedef enum {
+    CUDNN_NUMERICAL_NOTE_TENSOR_CORE = 0,
+    CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS,
+    CUDNN_NUMERICAL_NOTE_REDUCED_PRECISION_REDUCTION,
+    CUDNN_NUMERICAL_NOTE_FFT,
+    CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_4x4,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_6x6,
+    CUDNN_NUMERICAL_NOTE_WINOGRAD_TILE_13x13,
+    CUDNN_NUMERICAL_NOTE_TYPE_COUNT,
+} cudnnBackendNumericalNote_t;
+
+typedef enum {
+    CUDNN_BEHAVIOR_NOTE_RUNTIME_COMPILATION             = 0,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_FILTER_INT8x32_REORDER = 1,
+    CUDNN_BEHAVIOR_NOTE_REQUIRES_BIAS_INT8x32_REORDER   = 2,
+    CUDNN_BEHAVIOR_NOTE_TYPE_COUNT,
+} cudnnBackendBehaviorNote_t;
+
+typedef enum {
+    CUDNN_KNOB_TYPE_SPLIT_K          = 0,
+    CUDNN_KNOB_TYPE_SWIZZLE          = 1,
+    CUDNN_KNOB_TYPE_TILE_SIZE        = 2,
+    CUDNN_KNOB_TYPE_USE_TEX          = 3,
+    CUDNN_KNOB_TYPE_EDGE             = 4,
+    CUDNN_KNOB_TYPE_KBLOCK           = 5,
+    CUDNN_KNOB_TYPE_LDGA             = 6,
+    CUDNN_KNOB_TYPE_LDGB             = 7,
+    CUDNN_KNOB_TYPE_CHUNK_K          = 8,
+    CUDNN_KNOB_TYPE_SPLIT_H          = 9,
+    CUDNN_KNOB_TYPE_WINO_TILE        = 10,
+    CUDNN_KNOB_TYPE_MULTIPLY         = 11,
+    CUDNN_KNOB_TYPE_SPLIT_K_BUF      = 12,
+    CUDNN_KNOB_TYPE_TILEK            = 13,
+    CUDNN_KNOB_TYPE_STAGES           = 14,
+    CUDNN_KNOB_TYPE_REDUCTION_MODE   = 15,
+    CUDNN_KNOB_TYPE_CTA_SPLIT_K_MODE = 16,
+    CUDNN_KNOB_TYPE_SPLIT_K_SLC      = 17,
+    CUDNN_KNOB_TYPE_IDX_MODE         = 18,
+    CUDNN_KNOB_TYPE_SLICED           = 19,
+    CUDNN_KNOB_TYPE_SPLIT_RS         = 20,
+    CUDNN_KNOB_TYPE_SINGLEBUFFER     = 21,
+    CUDNN_KNOB_TYPE_LDGC             = 22,
+    CUDNN_KNOB_TYPE_SPECFILT         = 23,
+    CUDNN_KNOB_TYPE_KERNEL_CFG       = 24,
+    CUDNN_KNOB_TYPE_WORKSPACE        = 25,
+
+    CUDNN_KNOB_TYPE_COUNTS = 26,
+} cudnnBackendKnobType_t;
+
+typedef enum {
+    CUDNN_LAYOUT_TYPE_PREFERRED_NCHW   = 0,
+    CUDNN_LAYOUT_TYPE_PREFERRED_NHWC   = 1,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD4CK = 2,
+    CUDNN_LAYOUT_TYPE_PREFERRED_PAD8CK = 3,
+    CUDNN_LAYOUT_TYPE_COUNT            = 4,
+} cudnnBackendLayoutType_t;
+
+typedef enum {
+    CUDNN_HEUR_MODE_INSTANT  = 0,
+    CUDNN_HEUR_MODE_B        = 1,
+    CUDNN_HEUR_MODE_FALLBACK = 2,
+    CUDNN_HEUR_MODE_A        = 3,
+    CUDNN_HEUR_MODES_COUNT   = 4,
+} cudnnBackendHeurMode_t;
+
+typedef enum {
+    CUDNN_TENSOR_REORDERING_NONE    = 0,
+    CUDNN_TENSOR_REORDERING_INT8x32 = 1,
+} cudnnBackendTensorReordering_t;
+
+typedef enum {
+    CUDNN_ZERO_PAD     = 0,
+    CUDNN_NEG_INF_PAD  = 1,
+    CUDNN_EDGE_VAL_PAD = 2,
+} cudnnPaddingMode_t;
+
+typedef enum {
+    CUDNN_LAYER_NORM    = 0,
+    CUDNN_INSTANCE_NORM = 1,
+    CUDNN_BATCH_NORM    = 2,
+    CUDNN_GROUP_NORM    = 3,
+} cudnnBackendNormMode_t;
+
+typedef enum {
+    CUDNN_NORM_FWD_INFERENCE = 0,
+    CUDNN_NORM_FWD_TRAINING  = 1,
+} cudnnBackendNormFwdPhase_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendCreateDescriptor(cudnnBackendDescriptorType_t descriptorType, cudnnBackendDescriptor_t *descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendDestroyDescriptor(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendInitialize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendFinalize(cudnnBackendDescriptor_t descriptor);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendSetAttribute(cudnnBackendDescriptor_t descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t elementCount,
+                         const void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendGetAttribute(cudnnBackendDescriptor_t const descriptor,
+                         cudnnBackendAttributeName_t attributeName,
+                         cudnnBackendAttributeType_t attributeType,
+                         int64_t requestedElementCount,
+                         int64_t *elementCount,
+                         void *arrayOfElements);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBackendExecute(cudnnHandle_t handle, cudnnBackendDescriptor_t executionPlan, cudnnBackendDescriptor_t variantPack);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* _CUDNN_BACKEND_H_ */

openflamingo/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_infer.h

@@ -0,0 +1,1177 @@
+/*
+ * Copyright 2017-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops_infer : cuDNN's basic definitions and inference operations.
+ */
+
+#if !defined(CUDNN_OPS_INFER_H_)
+#define CUDNN_OPS_INFER_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_INFER_MAJOR 8
+#define CUDNN_OPS_INFER_MINOR 5
+#define CUDNN_OPS_INFER_PATCH 0
+
+#if (CUDNN_OPS_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_OPS_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS INFER!!!
+#endif
+
+#ifndef CUDNNWINAPI
+#ifdef _WIN32
+#define CUDNNWINAPI __stdcall
+#else
+#define CUDNNWINAPI
+#endif
+#endif
+
+/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
+#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
+/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
+#define CUDNN_DEPRECATED __attribute__((deprecated))
+#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
+/* Microsoft Visual C++ */
+#define CUDNN_DEPRECATED __declspec(deprecated)
+#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
+/* C++14 compilers */
+#define CUDNN_DEPRECATED [[deprecated]]
+#else
+/* No support for the deprecated attribute */
+#define CUDNN_DEPRECATED
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+struct cudnnContext;
+typedef struct cudnnContext *cudnnHandle_t;
+
+size_t CUDNNWINAPI
+cudnnGetVersion(void);
+
+/* Returns CUDA Runtime version statically linked against cudnn */
+size_t CUDNNWINAPI
+cudnnGetCudartVersion(void);
+
+/*
+ * CUDNN return codes
+ */
+typedef enum {
+    CUDNN_STATUS_SUCCESS                      = 0,
+    CUDNN_STATUS_NOT_INITIALIZED              = 1,
+    CUDNN_STATUS_ALLOC_FAILED                 = 2,
+    CUDNN_STATUS_BAD_PARAM                    = 3,
+    CUDNN_STATUS_INTERNAL_ERROR               = 4,
+    CUDNN_STATUS_INVALID_VALUE                = 5,
+    CUDNN_STATUS_ARCH_MISMATCH                = 6,
+    CUDNN_STATUS_MAPPING_ERROR                = 7,
+    CUDNN_STATUS_EXECUTION_FAILED             = 8,
+    CUDNN_STATUS_NOT_SUPPORTED                = 9,
+    CUDNN_STATUS_LICENSE_ERROR                = 10,
+    CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING = 11,
+    CUDNN_STATUS_RUNTIME_IN_PROGRESS          = 12,
+    CUDNN_STATUS_RUNTIME_FP_OVERFLOW          = 13,
+    CUDNN_STATUS_VERSION_MISMATCH             = 14,
+} cudnnStatus_t;
+
+/* human-readable error messages */
+const char *CUDNNWINAPI
+cudnnGetErrorString(cudnnStatus_t status);
+
+/* Forward definition in this version only */
+typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t;
+
+typedef enum {
+    CUDNN_ERRQUERY_RAWCODE     = 0,
+    CUDNN_ERRQUERY_NONBLOCKING = 1,
+    CUDNN_ERRQUERY_BLOCKING    = 2,
+} cudnnErrQueryMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);
+
+#ifndef __LIBRARY_TYPES_H__
+
+typedef enum libraryPropertyType_t { MAJOR_VERSION, MINOR_VERSION, PATCH_LEVEL } libraryPropertyType;
+
+#endif
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetProperty(libraryPropertyType type, int *value);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreate(cudnnHandle_t *handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroy(cudnnHandle_t handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
+
+/* Data structures to represent Image/Filter and the Neural Network Layer */
+typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;
+typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t;
+typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t;
+typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;
+typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t;
+typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;
+typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t;
+typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t;
+typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;
+typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t;
+/*
+ * CUDNN data type
+ */
+typedef enum {
+    CUDNN_DATA_FLOAT    = 0,
+    CUDNN_DATA_DOUBLE   = 1,
+    CUDNN_DATA_HALF     = 2,
+    CUDNN_DATA_INT8     = 3,
+    CUDNN_DATA_INT32    = 4,
+    CUDNN_DATA_INT8x4   = 5,
+    CUDNN_DATA_UINT8    = 6,
+    CUDNN_DATA_UINT8x4  = 7,
+    CUDNN_DATA_INT8x32  = 8,
+    CUDNN_DATA_BFLOAT16 = 9,
+    CUDNN_DATA_INT64    = 10,
+    CUDNN_DATA_BOOLEAN  = 11,
+} cudnnDataType_t;
+
+/*
+ * CUDNN math type
+ */
+typedef enum {
+    CUDNN_DEFAULT_MATH                    = 0,
+    CUDNN_TENSOR_OP_MATH                  = 1,
+    CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2,
+    CUDNN_FMA_MATH                        = 3,
+} cudnnMathType_t;
+
+/*
+ * CUDNN propagate Nan
+ */
+typedef enum {
+    CUDNN_NOT_PROPAGATE_NAN = 0,
+    CUDNN_PROPAGATE_NAN     = 1,
+} cudnnNanPropagation_t;
+
+/*
+ * CUDNN Determinism
+ */
+typedef enum {
+    CUDNN_NON_DETERMINISTIC = 0,
+    CUDNN_DETERMINISTIC     = 1,
+} cudnnDeterminism_t;
+
+/* Maximum supported number of tensor dimensions */
+#define CUDNN_DIM_MAX 8
+
+/* Create an instance of a generic Tensor descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);
+
+typedef enum {
+    CUDNN_TENSOR_NCHW        = 0, /* row major (wStride = 1, hStride = w) */
+    CUDNN_TENSOR_NHWC        = 1, /* feature maps interleaved ( cStride = 1 )*/
+    CUDNN_TENSOR_NCHW_VECT_C = 2, /* each image point is vector of element of C, vector length in data type */
+} cudnnTensorFormat_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnTensorFormat_t format,
+                           cudnnDataType_t dataType, /* image data type */
+                           int n,                    /* number of inputs (batch size) */
+                           int c,                    /* number of input feature maps */
+                           int h,                    /* height of input section */
+                           int w);                   /* width of input section */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnDataType_t dataType, /* image data type */
+                             int n,                    /* number of inputs (batch size) */
+                             int c,                    /* number of input feature maps */
+                             int h,                    /* height of input section */
+                             int w,                    /* width of input section */
+                             int nStride,
+                             int cStride,
+                             int hStride,
+                             int wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           int *n,                    /* number of inputs (batch size) */
+                           int *c,                    /* number of input feature maps  */
+                           int *h,                    /* height of input section */
+                           int *w,                    /* width of input section */
+                           int *nStride,
+                           int *cStride,
+                           int *hStride,
+                           int *wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t dataType,
+                           int nbDims,
+                           const int dimA[],
+                           const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnTensorFormat_t format,
+                             cudnnDataType_t dataType,
+                             int nbDims,
+                             const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType,
+                           int *nbDims,
+                           int dimA[],
+                           int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);
+
+/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride
+
+   1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
+   input_stride :  c x h x h_stride
+   feature_stride : h x h_stride
+   h_stride  :  >= w  ( h_stride = w if no padding)
+   w_stride  : 1
+
+
+   2)Example of all images in row major with features maps interleaved
+   input_stride :  c x h x h_stride
+   feature_stride : 1
+   h_stride  :  w x c
+   w_stride  : c
+
+   3)Example of all images in column major order one batch of features after the other (with optional padding on column)
+   input_stride :  c x w x w_stride
+   feature_stride : w x w_stride
+   h_stride  :  1
+   w_stride  :  >= h
+
+*/
+
+/* Destroy an instance of Tensor4d descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);
+
+/* Fold/unfold transforms */
+typedef enum {
+    CUDNN_TRANSFORM_FOLD   = 0U,
+    CUDNN_TRANSFORM_UNFOLD = 1U,
+} cudnnFoldingDirection_t;
+
+/** Create a destination descriptor for cudnnTransformTensor */
+cudnnStatus_t CUDNNWINAPI
+cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       cudnnTensorDescriptor_t destDesc,
+                       size_t *destSizeInBytes);
+
+/** Create an empty tensor transform descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);
+
+/** Initialize a previously created tensor transform descriptor. */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  const uint32_t nbDims,
+                                  const cudnnTensorFormat_t destFormat,
+                                  const int32_t padBeforeA[],
+                                  const int32_t padAfterA[],
+                                  const uint32_t foldA[],
+                                  const cudnnFoldingDirection_t direction);
+
+/**
+ * Retrieves the values stored in a previously initialized tensor transform
+ * descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  uint32_t nbDimsRequested,
+                                  cudnnTensorFormat_t *destFormat,
+                                  int32_t padBeforeA[],
+                                  int32_t padAfterA[],
+                                  uint32_t foldA[],
+                                  cudnnFoldingDirection_t *direction);
+
+/**
+ * Destroys a previously created tensor transform descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);
+
+/* Tensor layout conversion helper (y = alpha * x + beta * y) */
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensor(cudnnHandle_t handle,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t yDesc,
+                     void *y);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensorEx(cudnnHandle_t handle,
+                       const cudnnTensorTransformDescriptor_t transDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       const void *srcData,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t destDesc,
+                       void *destData);
+
+/* Tensor Bias addition : C = alpha * A + beta * C  */
+cudnnStatus_t CUDNNWINAPI
+cudnnAddTensor(cudnnHandle_t handle,
+               const void *alpha,
+               const cudnnTensorDescriptor_t aDesc,
+               const void *A,
+               const void *beta,
+               const cudnnTensorDescriptor_t cDesc,
+               void *C);
+
+/*
+ * CUDNN OpTensor op type
+ */
+typedef enum {
+    CUDNN_OP_TENSOR_ADD  = 0,
+    CUDNN_OP_TENSOR_MUL  = 1,
+    CUDNN_OP_TENSOR_MIN  = 2,
+    CUDNN_OP_TENSOR_MAX  = 3,
+    CUDNN_OP_TENSOR_SQRT = 4,
+    CUDNN_OP_TENSOR_NOT  = 5,
+} cudnnOpTensorOp_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t opTensorOp,
+                           cudnnDataType_t opTensorCompType,
+                           cudnnNanPropagation_t opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t *opTensorOp,
+                           cudnnDataType_t *opTensorCompType,
+                           cudnnNanPropagation_t *opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);
+
+/* Tensor operation : C = op( alpha1 * A, alpha2 * B ) + beta * C */
+/* B tensor is ignored for CUDNN_OP_TENSOR_SQRT, CUDNN_OP_TENSOR_NOT. */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpTensor(cudnnHandle_t handle,
+              const cudnnOpTensorDescriptor_t opTensorDesc,
+              const void *alpha1,
+              const cudnnTensorDescriptor_t aDesc,
+              const void *A,
+              const void *alpha2,
+              const cudnnTensorDescriptor_t bDesc,
+              const void *B,
+              const void *beta,
+              const cudnnTensorDescriptor_t cDesc,
+              void *C);
+
+/*
+ * CUDNN ReduceTensor op type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_ADD          = 0,
+    CUDNN_REDUCE_TENSOR_MUL          = 1,
+    CUDNN_REDUCE_TENSOR_MIN          = 2,
+    CUDNN_REDUCE_TENSOR_MAX          = 3,
+    CUDNN_REDUCE_TENSOR_AMAX         = 4,
+    CUDNN_REDUCE_TENSOR_AVG          = 5,
+    CUDNN_REDUCE_TENSOR_NORM1        = 6,
+    CUDNN_REDUCE_TENSOR_NORM2        = 7,
+    CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8,
+} cudnnReduceTensorOp_t;
+
+/*
+ * CUDNN ReduceTensor indices type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_NO_INDICES        = 0,
+    CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1,
+} cudnnReduceTensorIndices_t;
+
+/*
+ * CUDNN tensor indices type size (all unsigned)
+ * Currently not supported, default is 32 bit unsigned.
+ */
+typedef enum {
+    CUDNN_32BIT_INDICES = 0,
+    CUDNN_64BIT_INDICES = 1,
+    CUDNN_16BIT_INDICES = 2,
+    CUDNN_8BIT_INDICES  = 3,
+} cudnnIndicesType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t reduceTensorOp,
+                               cudnnDataType_t reduceTensorCompType,
+                               cudnnNanPropagation_t reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t reduceTensorIndices,
+                               cudnnIndicesType_t reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t *reduceTensorOp,
+                               cudnnDataType_t *reduceTensorCompType,
+                               cudnnNanPropagation_t *reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t *reduceTensorIndices,
+                               cudnnIndicesType_t *reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);
+
+/* Helper function to return the minimum size of the index space to be passed to the reduction given the input and
+ * output tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionIndicesSize(cudnnHandle_t handle,
+                             const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                             const cudnnTensorDescriptor_t aDesc,
+                             const cudnnTensorDescriptor_t cDesc,
+                             size_t *sizeInBytes);
+
+/* Helper function to return the minimum size of the workspace to be passed to the reduction given the input and output
+ * tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
+                               const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               const cudnnTensorDescriptor_t aDesc,
+                               const cudnnTensorDescriptor_t cDesc,
+                               size_t *sizeInBytes);
+
+/* Tensor operation : C = reduce op( alpha * A ) + beta * C */
+/* The NaN propagation enum applies to only the min and max reduce ops; the other reduce ops propagate NaN as usual. */
+/* The indices space is ignored for reduce ops other than min or max. */
+cudnnStatus_t CUDNNWINAPI
+cudnnReduceTensor(cudnnHandle_t handle,
+                  const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                  void *indices,
+                  size_t indicesSizeInBytes,
+                  void *workspace,
+                  size_t workspaceSizeInBytes,
+                  const void *alpha,
+                  const cudnnTensorDescriptor_t aDesc,
+                  const void *A,
+                  const void *beta,
+                  const cudnnTensorDescriptor_t cDesc,
+                  void *C);
+
+/* Set all values of a tensor to a given value : y[i] = value[0] */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);
+
+/* Scale all values of a tensor by a given factor : y[i] = alpha * y[i] */
+cudnnStatus_t CUDNNWINAPI
+cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);
+
+/* Create an instance of FilterStruct */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int k,  /* number of output feature maps */
+                           int c,  /* number of input feature maps */
+                           int h,  /* height of each input filter */
+                           int w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *k,  /* number of output feature maps */
+                           int *c,  /* number of input feature maps */
+                           int *h,  /* height of each input filter */
+                           int *w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int nbDims,
+                           const int filterDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *nbDims,
+                           int filterDimA[]);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformFilter(cudnnHandle_t handle,
+                     const cudnnTensorTransformDescriptor_t transDesc,
+                     const void *alpha,
+                     const cudnnFilterDescriptor_t srcDesc,
+                     const void *srcData,
+                     const void *beta,
+                     const cudnnFilterDescriptor_t destDesc,
+                     void *destData);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);
+
+/*
+ *  softmax algorithm
+ */
+typedef enum {
+    CUDNN_SOFTMAX_FAST     = 0, /* straightforward implementation */
+    CUDNN_SOFTMAX_ACCURATE = 1, /* subtract max from every point to avoid overflow */
+    CUDNN_SOFTMAX_LOG      = 2
+} cudnnSoftmaxAlgorithm_t;
+
+typedef enum {
+    CUDNN_SOFTMAX_MODE_INSTANCE = 0, /* compute the softmax over all C, H, W for each N */
+    CUDNN_SOFTMAX_MODE_CHANNEL  = 1  /* compute the softmax over all C for each H, W, N */
+} cudnnSoftmaxMode_t;
+
+/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxForward(cudnnHandle_t handle,
+                    cudnnSoftmaxAlgorithm_t algo,
+                    cudnnSoftmaxMode_t mode,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ *  pooling mode
+ */
+typedef enum {
+    CUDNN_POOLING_MAX                           = 0,
+    CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /* count for average includes padded values */
+    CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /* count for average does not include padded values */
+    CUDNN_POOLING_MAX_DETERMINISTIC             = 3
+} cudnnPoolingMode_t;
+
+/* Create an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t mode,
+                            cudnnNanPropagation_t maxpoolingNanOpt,
+                            int windowHeight,
+                            int windowWidth,
+                            int verticalPadding,
+                            int horizontalPadding,
+                            int verticalStride,
+                            int horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *windowHeight,
+                            int *windowWidth,
+                            int *verticalPadding,
+                            int *horizontalPadding,
+                            int *verticalStride,
+                            int *horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            const cudnnPoolingMode_t mode,
+                            const cudnnNanPropagation_t maxpoolingNanOpt,
+                            int nbDims,
+                            const int windowDimA[],
+                            const int paddingA[],
+                            const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            int nbDimsRequested,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *nbDims,
+                            int windowDimA[],
+                            int paddingA[],
+                            int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int nbDims,
+                                  int outputTensorDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int *n,
+                                  int *c,
+                                  int *h,
+                                  int *w);
+
+/* Destroy an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);
+
+/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward pooling */
+cudnnStatus_t CUDNNWINAPI
+cudnnPoolingForward(cudnnHandle_t handle,
+                    const cudnnPoolingDescriptor_t poolingDesc,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ * activation mode
+ */
+typedef enum {
+    CUDNN_ACTIVATION_SIGMOID      = 0,
+    CUDNN_ACTIVATION_RELU         = 1,
+    CUDNN_ACTIVATION_TANH         = 2,
+    CUDNN_ACTIVATION_CLIPPED_RELU = 3,
+    CUDNN_ACTIVATION_ELU          = 4,
+    CUDNN_ACTIVATION_IDENTITY     = 5,
+    CUDNN_ACTIVATION_SWISH        = 6
+} cudnnActivationMode_t;
+
+/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t mode,
+                             cudnnNanPropagation_t reluNanOpt,
+                             double coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t *mode,
+                             cudnnNanPropagation_t *reluNanOpt,
+                             double *coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double *swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);
+
+/* Function to perform forward activation  */
+cudnnStatus_t CUDNNWINAPI
+cudnnActivationForward(cudnnHandle_t handle,
+                       cudnnActivationDescriptor_t activationDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t xDesc,
+                       const void *x,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t yDesc,
+                       void *y);
+
+/*
+ * Create an instance of LRN (Local Response Normalization) descriptor
+ * Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from Krizhevsky'12 ImageNet paper
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);
+
+#define CUDNN_LRN_MIN_N 1       /* minimum allowed lrnN */
+#define CUDNN_LRN_MAX_N 16      /* maximum allowed lrnN */
+#define CUDNN_LRN_MIN_K 1e-5    /* minimum allowed lrnK */
+#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */
+
+/* LRN layer mode */
+typedef enum {
+    CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /* Normalize across tensor's dimA[1] dimension */
+} cudnnLRNMode_t;
+
+/*
+ * Uses a window [center-lookBehind, center+lookAhead], where
+ * lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1.
+ * Values of double parameters cast to tensor data type.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
+/*
+ * Retrieve the settings currently stored in an LRN layer descriptor
+ * Any of the provided pointers can be NULL (no corresponding value will be returned)
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);
+
+/* Destroy an instance of LRN descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);
+
+/* LRN functions: output = alpha * normalize(x) + beta * old_y */
+
+/* LRN cross-channel forward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelForward(cudnnHandle_t handle,
+                            cudnnLRNDescriptor_t normDesc,
+                            cudnnLRNMode_t lrnMode,
+                            const void *alpha,
+                            const cudnnTensorDescriptor_t xDesc,
+                            const void *x,
+                            const void *beta,
+                            const cudnnTensorDescriptor_t yDesc,
+                            void *y);
+
+typedef enum {
+    CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0,
+} cudnnDivNormMode_t;
+
+/* LCN/divisive normalization functions: y = alpha * normalize(x) + beta * y */
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
+                                  cudnnLRNDescriptor_t normDesc,
+                                  cudnnDivNormMode_t mode,
+                                  const void *alpha,
+                                  const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
+                                  const void *x,
+                                  const void *means, /* if NULL, means are assumed to be zero */
+                                  void *temp,
+                                  void *temp2,
+                                  const void *beta,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *y);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_BATCHNORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_BATCHNORM_SPATIAL = 1,
+
+    /*
+     * bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors).
+     * May be faster than CUDNN_BATCHNORM_SPATIAL but imposes some limits on the range of values
+     */
+    CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2,
+} cudnnBatchNormMode_t;
+
+#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for BatchNormalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc in Batch Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
+                              const cudnnTensorDescriptor_t xDesc,
+                              cudnnBatchNormMode_t mode);
+
+typedef enum {
+    CUDNN_BATCHNORM_OPS_BN                = 0, /* do batch normalization only */
+    CUDNN_BATCHNORM_OPS_BN_ACTIVATION     = 1, /* do batchNorm, then activation */
+    CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /* do batchNorm, then elemWiseAdd, then activation */
+} cudnnBatchNormOps_t;
+
+/*
+ * Performs Batch Normalization during Inference:
+ * y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
+ * with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
+ * according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
+                                        cudnnBatchNormMode_t mode,
+                                        const void *alpha, /* alpha[0] = result blend factor */
+                                        const void *beta,  /* beta[0] = dest layer blend factor */
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const void *x, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        void *y, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                        const void *bnScale,
+                                        const void *bnBias,
+                                        const void *estimatedMean,
+                                        const void *estimatedVariance,
+                                        double epsilon);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_NORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_NORM_PER_CHANNEL = 1,
+} cudnnNormMode_t;
+
+typedef enum { CUDNN_NORM_ALGO_STANDARD = 0, CUDNN_NORM_ALGO_PERSIST = 1 } cudnnNormAlgo_t;
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for Normalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * normScaleBiasMeanVarDesc and normScaleBiasDiffDesc in Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
+                                cudnnTensorDescriptor_t derivedNormMeanVarDesc,
+                                const cudnnTensorDescriptor_t xDesc,
+                                cudnnNormMode_t mode,
+                                int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+typedef enum {
+    CUDNN_NORM_OPS_NORM                = 0, /* do normalization only */
+    CUDNN_NORM_OPS_NORM_ACTIVATION     = 1, /* do Norm, then activation */
+    CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /* do Norm, then elemWiseAdd, then activation */
+} cudnnNormOps_t;
+
+/*
+ * Performs Normalization during Inference:
+ * y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k]
+ * with normScale, normBias, runningMean, runningInvVariance tensors indexed
+ * according to per-channel or per-activation mode. Refer to cudnnNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardInference(cudnnHandle_t handle,
+                                   cudnnNormMode_t mode,
+                                   cudnnNormOps_t normOps,
+                                   cudnnNormAlgo_t algo,
+                                   const void *alpha, /* alpha[0] = result blend factor */
+                                   const void *beta,  /* beta[0] = dest layer blend factor */
+                                   const cudnnTensorDescriptor_t xDesc,
+                                   const void *x, /* NxCxHxW */
+                                   const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                   const void *normScale,
+                                   const void *normBias,
+                                   const cudnnTensorDescriptor_t normMeanVarDesc,
+                                   const void *estimatedMean,
+                                   const void *estimatedVariance,
+                                   const cudnnTensorDescriptor_t zDesc,
+                                   const void *z,
+                                   cudnnActivationDescriptor_t activationDesc,
+                                   const cudnnTensorDescriptor_t yDesc,
+                                   void *y, /* NxCxHxW */
+                                   double epsilon,
+                                   int groupCnt); /* Place hold for future work*/
+
+/* APIs for spatial transformer network*/
+typedef enum {
+    CUDNN_SAMPLER_BILINEAR = 0,
+} cudnnSamplerType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
+                                       cudnnSamplerType_t samplerType,
+                                       cudnnDataType_t dataType,
+                                       const int nbDims,
+                                       const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
+                                   const cudnnSpatialTransformerDescriptor_t stDesc,
+                                   const void *theta,
+                                   void *grid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
+                             cudnnSpatialTransformerDescriptor_t stDesc,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *grid,
+                             const void *beta,
+                             cudnnTensorDescriptor_t yDesc,
+                             void *y);
+
+typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);
+
+/*helper function to determine size of the states to be passed to cudnnSetDropoutDescriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);
+
+/*helper function to determine size of the reserve space to be passed to dropout forward/backward calls */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float dropout,
+                          void *states,
+                          size_t stateSizeInBytes,
+                          unsigned long long seed);
+
+/* Restores the dropout descriptor to a previously saved-off state */
+cudnnStatus_t CUDNNWINAPI
+cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                              cudnnHandle_t handle,
+                              float dropout,
+                              void *states,
+                              size_t stateSizeInBytes,
+                              unsigned long long seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float *dropout,
+                          void **states,
+                          unsigned long long *seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutForward(cudnnHandle_t handle,
+                    const cudnnDropoutDescriptor_t dropoutDesc,
+                    const cudnnTensorDescriptor_t xdesc,
+                    const void *x,
+                    const cudnnTensorDescriptor_t ydesc,
+                    void *y,
+                    void *reserveSpace,
+                    size_t reserveSpaceSizeInBytes);
+
+/* TODO: remove */
+
+typedef struct cudnnAlgorithmStruct *cudnnAlgorithmDescriptor_t;
+typedef struct cudnnAlgorithmPerformanceStruct *cudnnAlgorithmPerformance_t;
+
+/* TODO: move these enums out to the appropriate submodule */
+typedef enum {
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM         = 0,
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1,
+    CUDNN_CONVOLUTION_FWD_ALGO_GEMM                  = 2,
+    CUDNN_CONVOLUTION_FWD_ALGO_DIRECT                = 3,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT                   = 4,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING            = 5,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD              = 6,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED     = 7,
+    CUDNN_CONVOLUTION_FWD_ALGO_COUNT                 = 8
+} cudnnConvolutionFwdAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3                 = 3, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD          = 4, /* not implemented */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING        = 6,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT             = 7
+} cudnnConvolutionBwdFilterAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING        = 3,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD          = 4,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT             = 6
+} cudnnConvolutionBwdDataAlgo_t;
+
+typedef enum {
+    CUDNN_RNN_ALGO_STANDARD               = 0,
+    CUDNN_RNN_ALGO_PERSIST_STATIC         = 1,
+    CUDNN_RNN_ALGO_PERSIST_DYNAMIC        = 2,
+    CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3,
+    CUDNN_RNN_ALGO_COUNT                  = 4,
+} cudnnRNNAlgo_t;
+
+typedef enum { CUDNN_CTC_LOSS_ALGO_DETERMINISTIC = 0, CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1 } cudnnCTCLossAlgo_t;
+
+/* TODO: remove */
+typedef struct cudnnAlgorithmUnionStruct {
+    union Algorithm {
+        cudnnConvolutionFwdAlgo_t convFwdAlgo;
+        cudnnConvolutionBwdFilterAlgo_t convBwdFilterAlgo;
+        cudnnConvolutionBwdDataAlgo_t convBwdDataAlgo;
+        cudnnRNNAlgo_t RNNAlgo;
+        cudnnCTCLossAlgo_t CTCLossAlgo;
+    } algo;
+} cudnnAlgorithm_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmDescriptor(cudnnAlgorithmDescriptor_t *algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t *algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCopyAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t src, cudnnAlgorithmDescriptor_t dest);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToCreate);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmPerformance(cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t algoDesc,
+                             cudnnStatus_t status,
+                             float time,
+                             size_t memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmPerformance(const cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t *algoDesc,
+                             cudnnStatus_t *status,
+                             float *time,
+                             size_t *memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToDestroy);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmSpaceSize(cudnnHandle_t handle, cudnnAlgorithmDescriptor_t algoDesc, size_t *algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSaveAlgorithm(cudnnHandle_t handle,
+                   cudnnAlgorithmDescriptor_t algoDesc,
+                   void *algoSpace,
+                   size_t algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRestoreAlgorithm(cudnnHandle_t handle,
+                      void *algoSpace,
+                      size_t algoSpaceSizeInBytes,
+                      cudnnAlgorithmDescriptor_t algoDesc);
+
+typedef enum {
+    CUDNN_SEV_FATAL   = 0,
+    CUDNN_SEV_ERROR   = 1,
+    CUDNN_SEV_WARNING = 2,
+    CUDNN_SEV_INFO    = 3,
+} cudnnSeverity_t;
+
+/* Message masks to be used with cudnnSetCallback() */
+#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
+#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
+#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)
+
+/* struct containing useful informaiton for each API call */
+typedef struct cudnnDebugStruct {
+    unsigned cudnn_version;
+    cudnnStatus_t cudnnStatus;
+    unsigned time_sec;      /* epoch time in seconds */
+    unsigned time_usec;     /* microseconds part of epoch time */
+    unsigned time_delta;    /* time since start in seconds */
+    cudnnHandle_t handle;   /* cudnn handle */
+    cudaStream_t stream;    /* cuda stream ID */
+    unsigned long long pid; /* process ID */
+    unsigned long long tid; /* thread ID */
+    int cudaDeviceId;       /* CUDA device ID */
+    int reserved[15];       /* reserved for future use */
+} cudnnDebug_t;
+
+typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_INFER_H_ */

openflamingo/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_infer_v8.h

@@ -0,0 +1,1177 @@
+/*
+ * Copyright 2017-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops_infer : cuDNN's basic definitions and inference operations.
+ */
+
+#if !defined(CUDNN_OPS_INFER_H_)
+#define CUDNN_OPS_INFER_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_INFER_MAJOR 8
+#define CUDNN_OPS_INFER_MINOR 5
+#define CUDNN_OPS_INFER_PATCH 0
+
+#if (CUDNN_OPS_INFER_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_INFER_MINOR != CUDNN_MINOR) || \
+    (CUDNN_OPS_INFER_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS INFER!!!
+#endif
+
+#ifndef CUDNNWINAPI
+#ifdef _WIN32
+#define CUDNNWINAPI __stdcall
+#else
+#define CUDNNWINAPI
+#endif
+#endif
+
+/* Warnings for deprecated API-s are enabled using the CUDNN_WARN_DEPRECATED macro */
+#if defined(CUDNN_WARN_DEPRECATED) && (defined(__GNUC__) || defined(__clang__))
+/* GCC, Intel C/C++, Cray C/C++, CLANG, IBM XL C/C++ little endian */
+#define CUDNN_DEPRECATED __attribute__((deprecated))
+#elif defined(CUDNN_WARN_DEPRECATED) && defined(_MSC_VER)
+/* Microsoft Visual C++ */
+#define CUDNN_DEPRECATED __declspec(deprecated)
+#elif defined(CUDNN_WARN_DEPRECATED) && (__cplusplus >= 201402L)
+/* C++14 compilers */
+#define CUDNN_DEPRECATED [[deprecated]]
+#else
+/* No support for the deprecated attribute */
+#define CUDNN_DEPRECATED
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+struct cudnnContext;
+typedef struct cudnnContext *cudnnHandle_t;
+
+size_t CUDNNWINAPI
+cudnnGetVersion(void);
+
+/* Returns CUDA Runtime version statically linked against cudnn */
+size_t CUDNNWINAPI
+cudnnGetCudartVersion(void);
+
+/*
+ * CUDNN return codes
+ */
+typedef enum {
+    CUDNN_STATUS_SUCCESS                      = 0,
+    CUDNN_STATUS_NOT_INITIALIZED              = 1,
+    CUDNN_STATUS_ALLOC_FAILED                 = 2,
+    CUDNN_STATUS_BAD_PARAM                    = 3,
+    CUDNN_STATUS_INTERNAL_ERROR               = 4,
+    CUDNN_STATUS_INVALID_VALUE                = 5,
+    CUDNN_STATUS_ARCH_MISMATCH                = 6,
+    CUDNN_STATUS_MAPPING_ERROR                = 7,
+    CUDNN_STATUS_EXECUTION_FAILED             = 8,
+    CUDNN_STATUS_NOT_SUPPORTED                = 9,
+    CUDNN_STATUS_LICENSE_ERROR                = 10,
+    CUDNN_STATUS_RUNTIME_PREREQUISITE_MISSING = 11,
+    CUDNN_STATUS_RUNTIME_IN_PROGRESS          = 12,
+    CUDNN_STATUS_RUNTIME_FP_OVERFLOW          = 13,
+    CUDNN_STATUS_VERSION_MISMATCH             = 14,
+} cudnnStatus_t;
+
+/* human-readable error messages */
+const char *CUDNNWINAPI
+cudnnGetErrorString(cudnnStatus_t status);
+
+/* Forward definition in this version only */
+typedef struct cudnnRuntimeTag_t cudnnRuntimeTag_t;
+
+typedef enum {
+    CUDNN_ERRQUERY_RAWCODE     = 0,
+    CUDNN_ERRQUERY_NONBLOCKING = 1,
+    CUDNN_ERRQUERY_BLOCKING    = 2,
+} cudnnErrQueryMode_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnQueryRuntimeError(cudnnHandle_t handle, cudnnStatus_t *rstatus, cudnnErrQueryMode_t mode, cudnnRuntimeTag_t *tag);
+
+#ifndef __LIBRARY_TYPES_H__
+
+typedef enum libraryPropertyType_t { MAJOR_VERSION, MINOR_VERSION, PATCH_LEVEL } libraryPropertyType;
+
+#endif
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetProperty(libraryPropertyType type, int *value);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreate(cudnnHandle_t *handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroy(cudnnHandle_t handle);
+cudnnStatus_t CUDNNWINAPI
+cudnnSetStream(cudnnHandle_t handle, cudaStream_t streamId);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetStream(cudnnHandle_t handle, cudaStream_t *streamId);
+
+/* Data structures to represent Image/Filter and the Neural Network Layer */
+typedef struct cudnnTensorStruct *cudnnTensorDescriptor_t;
+typedef struct cudnnPoolingStruct *cudnnPoolingDescriptor_t;
+typedef struct cudnnFilterStruct *cudnnFilterDescriptor_t;
+typedef struct cudnnLRNStruct *cudnnLRNDescriptor_t;
+typedef struct cudnnActivationStruct *cudnnActivationDescriptor_t;
+typedef struct cudnnSpatialTransformerStruct *cudnnSpatialTransformerDescriptor_t;
+typedef struct cudnnOpTensorStruct *cudnnOpTensorDescriptor_t;
+typedef struct cudnnReduceTensorStruct *cudnnReduceTensorDescriptor_t;
+typedef struct cudnnCTCLossStruct *cudnnCTCLossDescriptor_t;
+typedef struct cudnnTensorTransformStruct *cudnnTensorTransformDescriptor_t;
+/*
+ * CUDNN data type
+ */
+typedef enum {
+    CUDNN_DATA_FLOAT    = 0,
+    CUDNN_DATA_DOUBLE   = 1,
+    CUDNN_DATA_HALF     = 2,
+    CUDNN_DATA_INT8     = 3,
+    CUDNN_DATA_INT32    = 4,
+    CUDNN_DATA_INT8x4   = 5,
+    CUDNN_DATA_UINT8    = 6,
+    CUDNN_DATA_UINT8x4  = 7,
+    CUDNN_DATA_INT8x32  = 8,
+    CUDNN_DATA_BFLOAT16 = 9,
+    CUDNN_DATA_INT64    = 10,
+    CUDNN_DATA_BOOLEAN  = 11,
+} cudnnDataType_t;
+
+/*
+ * CUDNN math type
+ */
+typedef enum {
+    CUDNN_DEFAULT_MATH                    = 0,
+    CUDNN_TENSOR_OP_MATH                  = 1,
+    CUDNN_TENSOR_OP_MATH_ALLOW_CONVERSION = 2,
+    CUDNN_FMA_MATH                        = 3,
+} cudnnMathType_t;
+
+/*
+ * CUDNN propagate Nan
+ */
+typedef enum {
+    CUDNN_NOT_PROPAGATE_NAN = 0,
+    CUDNN_PROPAGATE_NAN     = 1,
+} cudnnNanPropagation_t;
+
+/*
+ * CUDNN Determinism
+ */
+typedef enum {
+    CUDNN_NON_DETERMINISTIC = 0,
+    CUDNN_DETERMINISTIC     = 1,
+} cudnnDeterminism_t;
+
+/* Maximum supported number of tensor dimensions */
+#define CUDNN_DIM_MAX 8
+
+/* Create an instance of a generic Tensor descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorDescriptor(cudnnTensorDescriptor_t *tensorDesc);
+
+typedef enum {
+    CUDNN_TENSOR_NCHW        = 0, /* row major (wStride = 1, hStride = w) */
+    CUDNN_TENSOR_NHWC        = 1, /* feature maps interleaved ( cStride = 1 )*/
+    CUDNN_TENSOR_NCHW_VECT_C = 2, /* each image point is vector of element of C, vector length in data type */
+} cudnnTensorFormat_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnTensorFormat_t format,
+                           cudnnDataType_t dataType, /* image data type */
+                           int n,                    /* number of inputs (batch size) */
+                           int c,                    /* number of input feature maps */
+                           int h,                    /* height of input section */
+                           int w);                   /* width of input section */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor4dDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnDataType_t dataType, /* image data type */
+                             int n,                    /* number of inputs (batch size) */
+                             int c,                    /* number of input feature maps */
+                             int h,                    /* height of input section */
+                             int w,                    /* width of input section */
+                             int nStride,
+                             int cStride,
+                             int hStride,
+                             int wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensor4dDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           int *n,                    /* number of inputs (batch size) */
+                           int *c,                    /* number of input feature maps  */
+                           int *h,                    /* height of input section */
+                           int *w,                    /* width of input section */
+                           int *nStride,
+                           int *cStride,
+                           int *hStride,
+                           int *wStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptor(cudnnTensorDescriptor_t tensorDesc,
+                           cudnnDataType_t dataType,
+                           int nbDims,
+                           const int dimA[],
+                           const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorNdDescriptorEx(cudnnTensorDescriptor_t tensorDesc,
+                             cudnnTensorFormat_t format,
+                             cudnnDataType_t dataType,
+                             int nbDims,
+                             const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorNdDescriptor(const cudnnTensorDescriptor_t tensorDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType,
+                           int *nbDims,
+                           int dimA[],
+                           int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorSizeInBytes(const cudnnTensorDescriptor_t tensorDesc, size_t *size);
+
+/* PixelOffset( n, c, h, w ) = n *input_stride + c * feature_stride + h * h_stride + w * w_stride
+
+   1)Example of all images in row major order one batch of features after the other (with an optional padding on row)
+   input_stride :  c x h x h_stride
+   feature_stride : h x h_stride
+   h_stride  :  >= w  ( h_stride = w if no padding)
+   w_stride  : 1
+
+
+   2)Example of all images in row major with features maps interleaved
+   input_stride :  c x h x h_stride
+   feature_stride : 1
+   h_stride  :  w x c
+   w_stride  : c
+
+   3)Example of all images in column major order one batch of features after the other (with optional padding on column)
+   input_stride :  c x w x w_stride
+   feature_stride : w x w_stride
+   h_stride  :  1
+   w_stride  :  >= h
+
+*/
+
+/* Destroy an instance of Tensor4d descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorDescriptor(cudnnTensorDescriptor_t tensorDesc);
+
+/* Fold/unfold transforms */
+typedef enum {
+    CUDNN_TRANSFORM_FOLD   = 0U,
+    CUDNN_TRANSFORM_UNFOLD = 1U,
+} cudnnFoldingDirection_t;
+
+/** Create a destination descriptor for cudnnTransformTensor */
+cudnnStatus_t CUDNNWINAPI
+cudnnInitTransformDest(const cudnnTensorTransformDescriptor_t transformDesc,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       cudnnTensorDescriptor_t destDesc,
+                       size_t *destSizeInBytes);
+
+/** Create an empty tensor transform descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateTensorTransformDescriptor(cudnnTensorTransformDescriptor_t *transformDesc);
+
+/** Initialize a previously created tensor transform descriptor. */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  const uint32_t nbDims,
+                                  const cudnnTensorFormat_t destFormat,
+                                  const int32_t padBeforeA[],
+                                  const int32_t padAfterA[],
+                                  const uint32_t foldA[],
+                                  const cudnnFoldingDirection_t direction);
+
+/**
+ * Retrieves the values stored in a previously initialized tensor transform
+ * descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc,
+                                  uint32_t nbDimsRequested,
+                                  cudnnTensorFormat_t *destFormat,
+                                  int32_t padBeforeA[],
+                                  int32_t padAfterA[],
+                                  uint32_t foldA[],
+                                  cudnnFoldingDirection_t *direction);
+
+/**
+ * Destroys a previously created tensor transform descriptor.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyTensorTransformDescriptor(cudnnTensorTransformDescriptor_t transformDesc);
+
+/* Tensor layout conversion helper (y = alpha * x + beta * y) */
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensor(cudnnHandle_t handle,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t yDesc,
+                     void *y);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformTensorEx(cudnnHandle_t handle,
+                       const cudnnTensorTransformDescriptor_t transDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t srcDesc,
+                       const void *srcData,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t destDesc,
+                       void *destData);
+
+/* Tensor Bias addition : C = alpha * A + beta * C  */
+cudnnStatus_t CUDNNWINAPI
+cudnnAddTensor(cudnnHandle_t handle,
+               const void *alpha,
+               const cudnnTensorDescriptor_t aDesc,
+               const void *A,
+               const void *beta,
+               const cudnnTensorDescriptor_t cDesc,
+               void *C);
+
+/*
+ * CUDNN OpTensor op type
+ */
+typedef enum {
+    CUDNN_OP_TENSOR_ADD  = 0,
+    CUDNN_OP_TENSOR_MUL  = 1,
+    CUDNN_OP_TENSOR_MIN  = 2,
+    CUDNN_OP_TENSOR_MAX  = 3,
+    CUDNN_OP_TENSOR_SQRT = 4,
+    CUDNN_OP_TENSOR_NOT  = 5,
+} cudnnOpTensorOp_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateOpTensorDescriptor(cudnnOpTensorDescriptor_t *opTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t opTensorOp,
+                           cudnnDataType_t opTensorCompType,
+                           cudnnNanPropagation_t opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetOpTensorDescriptor(const cudnnOpTensorDescriptor_t opTensorDesc,
+                           cudnnOpTensorOp_t *opTensorOp,
+                           cudnnDataType_t *opTensorCompType,
+                           cudnnNanPropagation_t *opTensorNanOpt);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyOpTensorDescriptor(cudnnOpTensorDescriptor_t opTensorDesc);
+
+/* Tensor operation : C = op( alpha1 * A, alpha2 * B ) + beta * C */
+/* B tensor is ignored for CUDNN_OP_TENSOR_SQRT, CUDNN_OP_TENSOR_NOT. */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpTensor(cudnnHandle_t handle,
+              const cudnnOpTensorDescriptor_t opTensorDesc,
+              const void *alpha1,
+              const cudnnTensorDescriptor_t aDesc,
+              const void *A,
+              const void *alpha2,
+              const cudnnTensorDescriptor_t bDesc,
+              const void *B,
+              const void *beta,
+              const cudnnTensorDescriptor_t cDesc,
+              void *C);
+
+/*
+ * CUDNN ReduceTensor op type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_ADD          = 0,
+    CUDNN_REDUCE_TENSOR_MUL          = 1,
+    CUDNN_REDUCE_TENSOR_MIN          = 2,
+    CUDNN_REDUCE_TENSOR_MAX          = 3,
+    CUDNN_REDUCE_TENSOR_AMAX         = 4,
+    CUDNN_REDUCE_TENSOR_AVG          = 5,
+    CUDNN_REDUCE_TENSOR_NORM1        = 6,
+    CUDNN_REDUCE_TENSOR_NORM2        = 7,
+    CUDNN_REDUCE_TENSOR_MUL_NO_ZEROS = 8,
+} cudnnReduceTensorOp_t;
+
+/*
+ * CUDNN ReduceTensor indices type
+ */
+typedef enum {
+    CUDNN_REDUCE_TENSOR_NO_INDICES        = 0,
+    CUDNN_REDUCE_TENSOR_FLATTENED_INDICES = 1,
+} cudnnReduceTensorIndices_t;
+
+/*
+ * CUDNN tensor indices type size (all unsigned)
+ * Currently not supported, default is 32 bit unsigned.
+ */
+typedef enum {
+    CUDNN_32BIT_INDICES = 0,
+    CUDNN_64BIT_INDICES = 1,
+    CUDNN_16BIT_INDICES = 2,
+    CUDNN_8BIT_INDICES  = 3,
+} cudnnIndicesType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateReduceTensorDescriptor(cudnnReduceTensorDescriptor_t *reduceTensorDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t reduceTensorOp,
+                               cudnnDataType_t reduceTensorCompType,
+                               cudnnNanPropagation_t reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t reduceTensorIndices,
+                               cudnnIndicesType_t reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReduceTensorDescriptor(const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               cudnnReduceTensorOp_t *reduceTensorOp,
+                               cudnnDataType_t *reduceTensorCompType,
+                               cudnnNanPropagation_t *reduceTensorNanOpt,
+                               cudnnReduceTensorIndices_t *reduceTensorIndices,
+                               cudnnIndicesType_t *reduceTensorIndicesType);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyReduceTensorDescriptor(cudnnReduceTensorDescriptor_t reduceTensorDesc);
+
+/* Helper function to return the minimum size of the index space to be passed to the reduction given the input and
+ * output tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionIndicesSize(cudnnHandle_t handle,
+                             const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                             const cudnnTensorDescriptor_t aDesc,
+                             const cudnnTensorDescriptor_t cDesc,
+                             size_t *sizeInBytes);
+
+/* Helper function to return the minimum size of the workspace to be passed to the reduction given the input and output
+ * tensors */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetReductionWorkspaceSize(cudnnHandle_t handle,
+                               const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                               const cudnnTensorDescriptor_t aDesc,
+                               const cudnnTensorDescriptor_t cDesc,
+                               size_t *sizeInBytes);
+
+/* Tensor operation : C = reduce op( alpha * A ) + beta * C */
+/* The NaN propagation enum applies to only the min and max reduce ops; the other reduce ops propagate NaN as usual. */
+/* The indices space is ignored for reduce ops other than min or max. */
+cudnnStatus_t CUDNNWINAPI
+cudnnReduceTensor(cudnnHandle_t handle,
+                  const cudnnReduceTensorDescriptor_t reduceTensorDesc,
+                  void *indices,
+                  size_t indicesSizeInBytes,
+                  void *workspace,
+                  size_t workspaceSizeInBytes,
+                  const void *alpha,
+                  const cudnnTensorDescriptor_t aDesc,
+                  const void *A,
+                  const void *beta,
+                  const cudnnTensorDescriptor_t cDesc,
+                  void *C);
+
+/* Set all values of a tensor to a given value : y[i] = value[0] */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *valuePtr);
+
+/* Scale all values of a tensor by a given factor : y[i] = alpha * y[i] */
+cudnnStatus_t CUDNNWINAPI
+cudnnScaleTensor(cudnnHandle_t handle, const cudnnTensorDescriptor_t yDesc, void *y, const void *alpha);
+
+/* Create an instance of FilterStruct */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateFilterDescriptor(cudnnFilterDescriptor_t *filterDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilter4dDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int k,  /* number of output feature maps */
+                           int c,  /* number of input feature maps */
+                           int h,  /* height of each input filter */
+                           int w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilter4dDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *k,  /* number of output feature maps */
+                           int *c,  /* number of input feature maps */
+                           int *h,  /* height of each input filter */
+                           int *w); /* width of  each input filter */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetFilterNdDescriptor(cudnnFilterDescriptor_t filterDesc,
+                           cudnnDataType_t dataType, /* image data type */
+                           cudnnTensorFormat_t format,
+                           int nbDims,
+                           const int filterDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterNdDescriptor(const cudnnFilterDescriptor_t filterDesc,
+                           int nbDimsRequested,
+                           cudnnDataType_t *dataType, /* image data type */
+                           cudnnTensorFormat_t *format,
+                           int *nbDims,
+                           int filterDimA[]);
+cudnnStatus_t CUDNNWINAPI
+cudnnGetFilterSizeInBytes(const cudnnFilterDescriptor_t filterDesc, size_t *size);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnTransformFilter(cudnnHandle_t handle,
+                     const cudnnTensorTransformDescriptor_t transDesc,
+                     const void *alpha,
+                     const cudnnFilterDescriptor_t srcDesc,
+                     const void *srcData,
+                     const void *beta,
+                     const cudnnFilterDescriptor_t destDesc,
+                     void *destData);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyFilterDescriptor(cudnnFilterDescriptor_t filterDesc);
+
+/*
+ *  softmax algorithm
+ */
+typedef enum {
+    CUDNN_SOFTMAX_FAST     = 0, /* straightforward implementation */
+    CUDNN_SOFTMAX_ACCURATE = 1, /* subtract max from every point to avoid overflow */
+    CUDNN_SOFTMAX_LOG      = 2
+} cudnnSoftmaxAlgorithm_t;
+
+typedef enum {
+    CUDNN_SOFTMAX_MODE_INSTANCE = 0, /* compute the softmax over all C, H, W for each N */
+    CUDNN_SOFTMAX_MODE_CHANNEL  = 1  /* compute the softmax over all C for each H, W, N */
+} cudnnSoftmaxMode_t;
+
+/* Softmax functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxForward(cudnnHandle_t handle,
+                    cudnnSoftmaxAlgorithm_t algo,
+                    cudnnSoftmaxMode_t mode,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ *  pooling mode
+ */
+typedef enum {
+    CUDNN_POOLING_MAX                           = 0,
+    CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING = 1, /* count for average includes padded values */
+    CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING = 2, /* count for average does not include padded values */
+    CUDNN_POOLING_MAX_DETERMINISTIC             = 3
+} cudnnPoolingMode_t;
+
+/* Create an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreatePoolingDescriptor(cudnnPoolingDescriptor_t *poolingDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPooling2dDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t mode,
+                            cudnnNanPropagation_t maxpoolingNanOpt,
+                            int windowHeight,
+                            int windowWidth,
+                            int verticalPadding,
+                            int horizontalPadding,
+                            int verticalStride,
+                            int horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *windowHeight,
+                            int *windowWidth,
+                            int *verticalPadding,
+                            int *horizontalPadding,
+                            int *verticalStride,
+                            int *horizontalStride);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetPoolingNdDescriptor(cudnnPoolingDescriptor_t poolingDesc,
+                            const cudnnPoolingMode_t mode,
+                            const cudnnNanPropagation_t maxpoolingNanOpt,
+                            int nbDims,
+                            const int windowDimA[],
+                            const int paddingA[],
+                            const int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdDescriptor(const cudnnPoolingDescriptor_t poolingDesc,
+                            int nbDimsRequested,
+                            cudnnPoolingMode_t *mode,
+                            cudnnNanPropagation_t *maxpoolingNanOpt,
+                            int *nbDims,
+                            int windowDimA[],
+                            int paddingA[],
+                            int strideA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPoolingNdForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int nbDims,
+                                  int outputTensorDimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetPooling2dForwardOutputDim(const cudnnPoolingDescriptor_t poolingDesc,
+                                  const cudnnTensorDescriptor_t inputTensorDesc,
+                                  int *n,
+                                  int *c,
+                                  int *h,
+                                  int *w);
+
+/* Destroy an instance of pooling descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyPoolingDescriptor(cudnnPoolingDescriptor_t poolingDesc);
+
+/* Pooling functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+
+/* Function to perform forward pooling */
+cudnnStatus_t CUDNNWINAPI
+cudnnPoolingForward(cudnnHandle_t handle,
+                    const cudnnPoolingDescriptor_t poolingDesc,
+                    const void *alpha,
+                    const cudnnTensorDescriptor_t xDesc,
+                    const void *x,
+                    const void *beta,
+                    const cudnnTensorDescriptor_t yDesc,
+                    void *y);
+
+/*
+ * activation mode
+ */
+typedef enum {
+    CUDNN_ACTIVATION_SIGMOID      = 0,
+    CUDNN_ACTIVATION_RELU         = 1,
+    CUDNN_ACTIVATION_TANH         = 2,
+    CUDNN_ACTIVATION_CLIPPED_RELU = 3,
+    CUDNN_ACTIVATION_ELU          = 4,
+    CUDNN_ACTIVATION_IDENTITY     = 5,
+    CUDNN_ACTIVATION_SWISH        = 6
+} cudnnActivationMode_t;
+
+/* Activation functions: All of the form "output = alpha * Op(inputs) + beta * output" */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateActivationDescriptor(cudnnActivationDescriptor_t *activationDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptor(cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t mode,
+                             cudnnNanPropagation_t reluNanOpt,
+                             double coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptor(const cudnnActivationDescriptor_t activationDesc,
+                             cudnnActivationMode_t *mode,
+                             cudnnNanPropagation_t *reluNanOpt,
+                             double *coef); /* ceiling for clipped RELU, alpha for ELU */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetActivationDescriptorSwishBeta(cudnnActivationDescriptor_t activationDesc, double *swish_beta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyActivationDescriptor(cudnnActivationDescriptor_t activationDesc);
+
+/* Function to perform forward activation  */
+cudnnStatus_t CUDNNWINAPI
+cudnnActivationForward(cudnnHandle_t handle,
+                       cudnnActivationDescriptor_t activationDesc,
+                       const void *alpha,
+                       const cudnnTensorDescriptor_t xDesc,
+                       const void *x,
+                       const void *beta,
+                       const cudnnTensorDescriptor_t yDesc,
+                       void *y);
+
+/*
+ * Create an instance of LRN (Local Response Normalization) descriptor
+ * Uses lrnN=5, lrnAlpha=1e-4, lrnBeta=0.75, lrnK=2.0 as defaults from Krizhevsky'12 ImageNet paper
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateLRNDescriptor(cudnnLRNDescriptor_t *normDesc);
+
+#define CUDNN_LRN_MIN_N 1       /* minimum allowed lrnN */
+#define CUDNN_LRN_MAX_N 16      /* maximum allowed lrnN */
+#define CUDNN_LRN_MIN_K 1e-5    /* minimum allowed lrnK */
+#define CUDNN_LRN_MIN_BETA 0.01 /* minimum allowed lrnBeta */
+
+/* LRN layer mode */
+typedef enum {
+    CUDNN_LRN_CROSS_CHANNEL_DIM1 = 0, /* Normalize across tensor's dimA[1] dimension */
+} cudnnLRNMode_t;
+
+/*
+ * Uses a window [center-lookBehind, center+lookAhead], where
+ * lookBehind = floor( (lrnN-1)/2 ), lookAhead = lrnN-lookBehind-1.
+ * Values of double parameters cast to tensor data type.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnSetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned lrnN, double lrnAlpha, double lrnBeta, double lrnK);
+/*
+ * Retrieve the settings currently stored in an LRN layer descriptor
+ * Any of the provided pointers can be NULL (no corresponding value will be returned)
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnGetLRNDescriptor(cudnnLRNDescriptor_t normDesc, unsigned *lrnN, double *lrnAlpha, double *lrnBeta, double *lrnK);
+
+/* Destroy an instance of LRN descriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyLRNDescriptor(cudnnLRNDescriptor_t lrnDesc);
+
+/* LRN functions: output = alpha * normalize(x) + beta * old_y */
+
+/* LRN cross-channel forward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelForward(cudnnHandle_t handle,
+                            cudnnLRNDescriptor_t normDesc,
+                            cudnnLRNMode_t lrnMode,
+                            const void *alpha,
+                            const cudnnTensorDescriptor_t xDesc,
+                            const void *x,
+                            const void *beta,
+                            const cudnnTensorDescriptor_t yDesc,
+                            void *y);
+
+typedef enum {
+    CUDNN_DIVNORM_PRECOMPUTED_MEANS = 0,
+} cudnnDivNormMode_t;
+
+/* LCN/divisive normalization functions: y = alpha * normalize(x) + beta * y */
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationForward(cudnnHandle_t handle,
+                                  cudnnLRNDescriptor_t normDesc,
+                                  cudnnDivNormMode_t mode,
+                                  const void *alpha,
+                                  const cudnnTensorDescriptor_t xDesc, /* same desc for means, temp, temp2 */
+                                  const void *x,
+                                  const void *means, /* if NULL, means are assumed to be zero */
+                                  void *temp,
+                                  void *temp2,
+                                  const void *beta,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *y);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_BATCHNORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_BATCHNORM_SPATIAL = 1,
+
+    /*
+     * bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors).
+     * May be faster than CUDNN_BATCHNORM_SPATIAL but imposes some limits on the range of values
+     */
+    CUDNN_BATCHNORM_SPATIAL_PERSISTENT = 2,
+} cudnnBatchNormMode_t;
+
+#define CUDNN_BN_MIN_EPSILON 0.0 /* Minimum epsilon allowed to be used in the Batch Normalization formula */
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for BatchNormalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * bnScaleBiasMeanVarDesc and bnScaleBiasDiffDesc in Batch Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveBNTensorDescriptor(cudnnTensorDescriptor_t derivedBnDesc,
+                              const cudnnTensorDescriptor_t xDesc,
+                              cudnnBatchNormMode_t mode);
+
+typedef enum {
+    CUDNN_BATCHNORM_OPS_BN                = 0, /* do batch normalization only */
+    CUDNN_BATCHNORM_OPS_BN_ACTIVATION     = 1, /* do batchNorm, then activation */
+    CUDNN_BATCHNORM_OPS_BN_ADD_ACTIVATION = 2, /* do batchNorm, then elemWiseAdd, then activation */
+} cudnnBatchNormOps_t;
+
+/*
+ * Performs Batch Normalization during Inference:
+ * y[i] = bnScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + bnBias[k]
+ * with bnScale, bnBias, runningMean, runningInvVariance tensors indexed
+ * according to spatial or per-activation mode. Refer to cudnnBatchNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardInference(cudnnHandle_t handle,
+                                        cudnnBatchNormMode_t mode,
+                                        const void *alpha, /* alpha[0] = result blend factor */
+                                        const void *beta,  /* beta[0] = dest layer blend factor */
+                                        const cudnnTensorDescriptor_t xDesc,
+                                        const void *x, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t yDesc,
+                                        void *y, /* NxCxHxW */
+                                        const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                        const void *bnScale,
+                                        const void *bnBias,
+                                        const void *estimatedMean,
+                                        const void *estimatedVariance,
+                                        double epsilon);
+
+typedef enum {
+    /* bnScale, bnBias tensor dims are 1xCxHxWx.. (one value per CHW...-slice, normalized over N slice) */
+    CUDNN_NORM_PER_ACTIVATION = 0,
+
+    /* bnScale, bnBias tensor dims are 1xCx1x1 (one value per C-dim normalized over Nx1xHxW subtensors) */
+    CUDNN_NORM_PER_CHANNEL = 1,
+} cudnnNormMode_t;
+
+typedef enum { CUDNN_NORM_ALGO_STANDARD = 0, CUDNN_NORM_ALGO_PERSIST = 1 } cudnnNormAlgo_t;
+
+/*
+ * Derives a tensor descriptor from layer data descriptor for Normalization
+ * scale, invVariance, bnBias, bnScale tensors. Use this tensor desc for
+ * normScaleBiasMeanVarDesc and normScaleBiasDiffDesc in Normalization forward and backward functions.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnDeriveNormTensorDescriptor(cudnnTensorDescriptor_t derivedNormScaleBiasDesc,
+                                cudnnTensorDescriptor_t derivedNormMeanVarDesc,
+                                const cudnnTensorDescriptor_t xDesc,
+                                cudnnNormMode_t mode,
+                                int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+typedef enum {
+    CUDNN_NORM_OPS_NORM                = 0, /* do normalization only */
+    CUDNN_NORM_OPS_NORM_ACTIVATION     = 1, /* do Norm, then activation */
+    CUDNN_NORM_OPS_NORM_ADD_ACTIVATION = 2, /* do Norm, then elemWiseAdd, then activation */
+} cudnnNormOps_t;
+
+/*
+ * Performs Normalization during Inference:
+ * y[i] = normScale[k]*(x[i]-estimatedMean[k])/sqrt(epsilon+estimatedVariance[k]) + normBias[k]
+ * with normScale, normBias, runningMean, runningInvVariance tensors indexed
+ * according to per-channel or per-activation mode. Refer to cudnnNormalizationForwardTraining
+ * above for notes on function arguments.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardInference(cudnnHandle_t handle,
+                                   cudnnNormMode_t mode,
+                                   cudnnNormOps_t normOps,
+                                   cudnnNormAlgo_t algo,
+                                   const void *alpha, /* alpha[0] = result blend factor */
+                                   const void *beta,  /* beta[0] = dest layer blend factor */
+                                   const cudnnTensorDescriptor_t xDesc,
+                                   const void *x, /* NxCxHxW */
+                                   const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                   const void *normScale,
+                                   const void *normBias,
+                                   const cudnnTensorDescriptor_t normMeanVarDesc,
+                                   const void *estimatedMean,
+                                   const void *estimatedVariance,
+                                   const cudnnTensorDescriptor_t zDesc,
+                                   const void *z,
+                                   cudnnActivationDescriptor_t activationDesc,
+                                   const cudnnTensorDescriptor_t yDesc,
+                                   void *y, /* NxCxHxW */
+                                   double epsilon,
+                                   int groupCnt); /* Place hold for future work*/
+
+/* APIs for spatial transformer network*/
+typedef enum {
+    CUDNN_SAMPLER_BILINEAR = 0,
+} cudnnSamplerType_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateSpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t *stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetSpatialTransformerNdDescriptor(cudnnSpatialTransformerDescriptor_t stDesc,
+                                       cudnnSamplerType_t samplerType,
+                                       cudnnDataType_t dataType,
+                                       const int nbDims,
+                                       const int dimA[]);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroySpatialTransformerDescriptor(cudnnSpatialTransformerDescriptor_t stDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorForward(cudnnHandle_t handle,
+                                   const cudnnSpatialTransformerDescriptor_t stDesc,
+                                   const void *theta,
+                                   void *grid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerForward(cudnnHandle_t handle,
+                             cudnnSpatialTransformerDescriptor_t stDesc,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *grid,
+                             const void *beta,
+                             cudnnTensorDescriptor_t yDesc,
+                             void *y);
+
+typedef struct cudnnDropoutStruct *cudnnDropoutDescriptor_t;
+
+cudnnStatus_t CUDNNWINAPI
+cudnnCreateDropoutDescriptor(cudnnDropoutDescriptor_t *dropoutDesc);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDestroyDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc);
+
+/*helper function to determine size of the states to be passed to cudnnSetDropoutDescriptor */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetStatesSize(cudnnHandle_t handle, size_t *sizeInBytes);
+
+/*helper function to determine size of the reserve space to be passed to dropout forward/backward calls */
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutGetReserveSpaceSize(cudnnTensorDescriptor_t xdesc, size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float dropout,
+                          void *states,
+                          size_t stateSizeInBytes,
+                          unsigned long long seed);
+
+/* Restores the dropout descriptor to a previously saved-off state */
+cudnnStatus_t CUDNNWINAPI
+cudnnRestoreDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                              cudnnHandle_t handle,
+                              float dropout,
+                              void *states,
+                              size_t stateSizeInBytes,
+                              unsigned long long seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetDropoutDescriptor(cudnnDropoutDescriptor_t dropoutDesc,
+                          cudnnHandle_t handle,
+                          float *dropout,
+                          void **states,
+                          unsigned long long *seed);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutForward(cudnnHandle_t handle,
+                    const cudnnDropoutDescriptor_t dropoutDesc,
+                    const cudnnTensorDescriptor_t xdesc,
+                    const void *x,
+                    const cudnnTensorDescriptor_t ydesc,
+                    void *y,
+                    void *reserveSpace,
+                    size_t reserveSpaceSizeInBytes);
+
+/* TODO: remove */
+
+typedef struct cudnnAlgorithmStruct *cudnnAlgorithmDescriptor_t;
+typedef struct cudnnAlgorithmPerformanceStruct *cudnnAlgorithmPerformance_t;
+
+/* TODO: move these enums out to the appropriate submodule */
+typedef enum {
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM         = 0,
+    CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM = 1,
+    CUDNN_CONVOLUTION_FWD_ALGO_GEMM                  = 2,
+    CUDNN_CONVOLUTION_FWD_ALGO_DIRECT                = 3,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT                   = 4,
+    CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING            = 5,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD              = 6,
+    CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED     = 7,
+    CUDNN_CONVOLUTION_FWD_ALGO_COUNT                 = 8
+} cudnnConvolutionFwdAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_3                 = 3, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD          = 4, /* not implemented */
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_FFT_TILING        = 6,
+    CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT             = 7
+} cudnnConvolutionBwdFilterAlgo_t;
+
+typedef enum {
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_0                 = 0, /* non-deterministic */
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_1                 = 1,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT               = 2,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT_TILING        = 3,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD          = 4,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_WINOGRAD_NONFUSED = 5,
+    CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT             = 6
+} cudnnConvolutionBwdDataAlgo_t;
+
+typedef enum {
+    CUDNN_RNN_ALGO_STANDARD               = 0,
+    CUDNN_RNN_ALGO_PERSIST_STATIC         = 1,
+    CUDNN_RNN_ALGO_PERSIST_DYNAMIC        = 2,
+    CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3,
+    CUDNN_RNN_ALGO_COUNT                  = 4,
+} cudnnRNNAlgo_t;
+
+typedef enum { CUDNN_CTC_LOSS_ALGO_DETERMINISTIC = 0, CUDNN_CTC_LOSS_ALGO_NON_DETERMINISTIC = 1 } cudnnCTCLossAlgo_t;
+
+/* TODO: remove */
+typedef struct cudnnAlgorithmUnionStruct {
+    union Algorithm {
+        cudnnConvolutionFwdAlgo_t convFwdAlgo;
+        cudnnConvolutionBwdFilterAlgo_t convBwdFilterAlgo;
+        cudnnConvolutionBwdDataAlgo_t convBwdDataAlgo;
+        cudnnRNNAlgo_t RNNAlgo;
+        cudnnCTCLossAlgo_t CTCLossAlgo;
+    } algo;
+} cudnnAlgorithm_t;
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmDescriptor(cudnnAlgorithmDescriptor_t *algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t algoDesc, cudnnAlgorithm_t *algorithm);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCopyAlgorithmDescriptor(const cudnnAlgorithmDescriptor_t src, cudnnAlgorithmDescriptor_t dest);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmDescriptor(cudnnAlgorithmDescriptor_t algoDesc);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnCreateAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToCreate);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSetAlgorithmPerformance(cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t algoDesc,
+                             cudnnStatus_t status,
+                             float time,
+                             size_t memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmPerformance(const cudnnAlgorithmPerformance_t algoPerf,
+                             cudnnAlgorithmDescriptor_t *algoDesc,
+                             cudnnStatus_t *status,
+                             float *time,
+                             size_t *memory);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnDestroyAlgorithmPerformance(cudnnAlgorithmPerformance_t *algoPerf, int numberToDestroy);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnGetAlgorithmSpaceSize(cudnnHandle_t handle, cudnnAlgorithmDescriptor_t algoDesc, size_t *algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnSaveAlgorithm(cudnnHandle_t handle,
+                   cudnnAlgorithmDescriptor_t algoDesc,
+                   void *algoSpace,
+                   size_t algoSpaceSizeInBytes);
+
+CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
+cudnnRestoreAlgorithm(cudnnHandle_t handle,
+                      void *algoSpace,
+                      size_t algoSpaceSizeInBytes,
+                      cudnnAlgorithmDescriptor_t algoDesc);
+
+typedef enum {
+    CUDNN_SEV_FATAL   = 0,
+    CUDNN_SEV_ERROR   = 1,
+    CUDNN_SEV_WARNING = 2,
+    CUDNN_SEV_INFO    = 3,
+} cudnnSeverity_t;
+
+/* Message masks to be used with cudnnSetCallback() */
+#define CUDNN_SEV_ERROR_EN (1U << CUDNN_SEV_ERROR)
+#define CUDNN_SEV_WARNING_EN (1U << CUDNN_SEV_WARNING)
+#define CUDNN_SEV_INFO_EN (1U << CUDNN_SEV_INFO)
+
+/* struct containing useful informaiton for each API call */
+typedef struct cudnnDebugStruct {
+    unsigned cudnn_version;
+    cudnnStatus_t cudnnStatus;
+    unsigned time_sec;      /* epoch time in seconds */
+    unsigned time_usec;     /* microseconds part of epoch time */
+    unsigned time_delta;    /* time since start in seconds */
+    cudnnHandle_t handle;   /* cudnn handle */
+    cudaStream_t stream;    /* cuda stream ID */
+    unsigned long long pid; /* process ID */
+    unsigned long long tid; /* thread ID */
+    int cudaDeviceId;       /* CUDA device ID */
+    int reserved[15];       /* reserved for future use */
+} cudnnDebug_t;
+
+typedef void (*cudnnCallback_t)(cudnnSeverity_t sev, void *udata, const cudnnDebug_t *dbg, const char *msg);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSetCallback(unsigned mask, void *udata, cudnnCallback_t fptr);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetCallback(unsigned *mask, void **udata, cudnnCallback_t *fptr);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsInferVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_INFER_H_ */

openflamingo/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_ops_train_v8.h

@@ -0,0 +1,501 @@
+/*
+ * Copyright 2017-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*
+ *  cudnn_ops_train : cuDNN's basic training operations and algorithms.
+ */
+
+#if !defined(CUDNN_OPS_TRAIN_H_)
+#define CUDNN_OPS_TRAIN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+
+/* These version numbers are autogenerated, do not edit manually. */
+#define CUDNN_OPS_TRAIN_MAJOR 8
+#define CUDNN_OPS_TRAIN_MINOR 5
+#define CUDNN_OPS_TRAIN_PATCH 0
+
+#if (CUDNN_OPS_TRAIN_MAJOR != CUDNN_MAJOR) || (CUDNN_OPS_TRAIN_MINOR != CUDNN_MINOR) || \
+    (CUDNN_OPS_TRAIN_PATCH != CUDNN_PATCHLEVEL)
+#error Version mismatch in cuDNN OPS TRAIN!!!
+#endif
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+/* Function to perform backward softmax */
+cudnnStatus_t CUDNNWINAPI
+cudnnSoftmaxBackward(cudnnHandle_t handle,
+                     cudnnSoftmaxAlgorithm_t algo,
+                     cudnnSoftmaxMode_t mode,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward pooling */
+cudnnStatus_t CUDNNWINAPI
+cudnnPoolingBackward(cudnnHandle_t handle,
+                     const cudnnPoolingDescriptor_t poolingDesc,
+                     const void *alpha,
+                     const cudnnTensorDescriptor_t yDesc,
+                     const void *y,
+                     const cudnnTensorDescriptor_t dyDesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t xDesc,
+                     const void *x,
+                     const void *beta,
+                     const cudnnTensorDescriptor_t dxDesc,
+                     void *dx);
+
+/* Function to perform backward activation  */
+cudnnStatus_t CUDNNWINAPI
+cudnnActivationBackward(cudnnHandle_t handle,
+                        cudnnActivationDescriptor_t activationDesc,
+                        const void *alpha,
+                        const cudnnTensorDescriptor_t yDesc,
+                        const void *y,
+                        const cudnnTensorDescriptor_t dyDesc,
+                        const void *dy,
+                        const cudnnTensorDescriptor_t xDesc,
+                        const void *x,
+                        const void *beta,
+                        const cudnnTensorDescriptor_t dxDesc,
+                        void *dx);
+
+/* LRN cross-channel backward computation. Double parameters cast to tensor data type */
+cudnnStatus_t CUDNNWINAPI
+cudnnLRNCrossChannelBackward(cudnnHandle_t handle,
+                             cudnnLRNDescriptor_t normDesc,
+                             cudnnLRNMode_t lrnMode,
+                             const void *alpha,
+                             const cudnnTensorDescriptor_t yDesc,
+                             const void *y,
+                             const cudnnTensorDescriptor_t dyDesc,
+                             const void *dy,
+                             const cudnnTensorDescriptor_t xDesc,
+                             const void *x,
+                             const void *beta,
+                             const cudnnTensorDescriptor_t dxDesc,
+                             void *dx);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDivisiveNormalizationBackward(cudnnHandle_t handle,
+                                   cudnnLRNDescriptor_t normDesc,
+                                   cudnnDivNormMode_t mode,
+                                   const void *alpha,
+                                   const cudnnTensorDescriptor_t xDesc, /* same desc for x, means, dy, temp, temp2 */
+                                   const void *x,
+                                   const void *means, /* if NULL, means are assumed to be zero */
+                                   const void *dy,
+                                   void *temp,
+                                   void *temp2,
+                                   const void *beta,
+                                   const cudnnTensorDescriptor_t dXdMeansDesc, /* same desc for dx, dMeans */
+                                   void *dx,                                   /* output x differential */
+                                   void *dMeans); /* output means differential, can be NULL */
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(cudnnHandle_t handle,
+                                                         cudnnBatchNormMode_t mode,
+                                                         cudnnBatchNormOps_t bnOps,
+                                                         const cudnnTensorDescriptor_t xDesc,
+                                                         const cudnnTensorDescriptor_t zDesc,
+                                                         const cudnnTensorDescriptor_t yDesc,
+                                                         const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+                                                         const cudnnActivationDescriptor_t activationDesc,
+                                                         size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationBackwardExWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnBatchNormMode_t mode,
+                                                  cudnnBatchNormOps_t bnOps,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t dyDesc,
+                                                  const cudnnTensorDescriptor_t dzDesc,
+                                                  const cudnnTensorDescriptor_t dxDesc,
+                                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  size_t *sizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetBatchNormalizationTrainingExReserveSpaceSize(cudnnHandle_t handle,
+                                                     cudnnBatchNormMode_t mode,
+                                                     cudnnBatchNormOps_t bnOps,
+                                                     const cudnnActivationDescriptor_t activationDesc,
+                                                     const cudnnTensorDescriptor_t xDesc,
+                                                     size_t *sizeInBytes);
+
+/* Computes y = BN(x). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTraining(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *x, /* NxCxHxW */
+    const cudnnTensorDescriptor_t yDesc,
+    void *y, /* NxCxHxW */
+
+    /* Shared desc for the next 6 tensors in the argument list.
+       Data type to be set as follows:
+       type = (typeOf(x) == double) ? double : float
+       Dimensions for this descriptor depend on normalization mode
+       - Spatial Normalization : tensors are expected to have dims 1xCx1x1
+        (normalization is performed across NxHxW)
+       - Per-Activation Normalization : tensors are expected to have dims of 1xCxHxW
+        (normalization is performed across N) */
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+
+    /* 'Gamma' and 'Beta' respectively in Ioffe and Szegedy's paper's notation */
+    const void *bnScale,
+    const void *bnBias,
+
+    /* MUST use factor=1 in the very first call of a complete training cycle.
+       Use a factor=1/(1+n) at N-th call to the function to get
+       Cumulative Moving Average (CMA) behavior
+       CMA[n] = (x[1]+...+x[n])/n
+       Since CMA[n+1] = (n*CMA[n]+x[n+1])/(n+1) =
+       ((n+1)*CMA[n]-CMA[n])/(n+1) + x[n+1]/(n+1) =
+       CMA[n]*(1-1/(n+1)) + x[n+1]*1/(n+1) */
+    double exponentialAverageFactor,
+
+    /* Used in Training phase only.
+       runningMean = newMean*factor + runningMean*(1-factor) */
+    void *resultRunningMean,
+    /* Output in training mode, input in inference. Is the moving average
+       of  variance[x] (factor is applied in the same way as for runningMean) */
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance);
+
+/* Computes y = relu(BN(x) + z). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationForwardTrainingEx(
+    cudnnHandle_t handle,
+    cudnnBatchNormMode_t mode,
+    cudnnBatchNormOps_t bnOps,
+
+    const void *alpha, /* alpha[0] = result blend factor */
+    const void *beta,  /* beta[0] = dest layer blend factor */
+
+    const cudnnTensorDescriptor_t xDesc,
+    const void *xData,
+    const cudnnTensorDescriptor_t zDesc,
+    const void *zData,
+    const cudnnTensorDescriptor_t yDesc,
+    void *yData,
+
+    const cudnnTensorDescriptor_t bnScaleBiasMeanVarDesc,
+    const void *bnScale,
+    const void *bnBias,
+
+    double exponentialAverageFactor,
+    void *resultRunningMean,
+    void *resultRunningVariance,
+
+    /* Has to be >= CUDNN_BN_MIN_EPSILON. Should be the same in forward and backward functions. */
+    double epsilon,
+
+    /* Optionally save intermediate results from the forward pass here
+       - can be reused to speed up backward pass. NULL if unused */
+    void *resultSaveMean,
+    void *resultSaveInvVariance,
+
+    cudnnActivationDescriptor_t activationDesc,
+    void *workspace,
+    size_t workSpaceSizeInBytes,
+    void *reserveSpace,
+    size_t reserveSpaceSizeInBytes);
+
+/* Performs backward pass of Batch Normalization layer. Returns x gradient,
+* bnScale gradient and bnBias gradient */
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackward(cudnnHandle_t handle,
+                                cudnnBatchNormMode_t mode,
+                                const void *alphaDataDiff,
+                                const void *betaDataDiff,
+                                const void *alphaParamDiff,
+                                const void *betaParamDiff,
+                                const cudnnTensorDescriptor_t xDesc, /* same desc for x, dx, dy */
+                                const void *x,
+                                const cudnnTensorDescriptor_t dyDesc,
+                                const void *dy,
+                                const cudnnTensorDescriptor_t dxDesc,
+                                void *dx,
+                                /* Shared tensor desc for the 4 tensors below */
+                                const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                const void *bnScale, /* bnBias doesn't affect backpropagation */
+                                /* scale and bias diff are not backpropagated below this layer */
+                                void *dBnScaleResult,
+                                void *dBnBiasResult,
+                                /* Same epsilon as forward pass */
+                                double epsilon,
+
+                                /* Optionally cached intermediate results from
+                                   forward pass */
+                                const void *savedMean,
+                                const void *savedInvVariance);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnBatchNormalizationBackwardEx(cudnnHandle_t handle,
+                                  cudnnBatchNormMode_t mode,
+                                  cudnnBatchNormOps_t bnOps,
+
+                                  const void *alphaDataDiff,
+                                  const void *betaDataDiff,
+                                  const void *alphaParamDiff,
+                                  const void *betaParamDiff,
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  const void *yData,
+                                  const cudnnTensorDescriptor_t dyDesc,
+                                  const void *dyData,
+                                  const cudnnTensorDescriptor_t dzDesc,
+                                  void *dzData,
+                                  const cudnnTensorDescriptor_t dxDesc,
+                                  void *dxData,
+
+                                  /* Shared tensor desc for the 4 tensors below */
+                                  const cudnnTensorDescriptor_t dBnScaleBiasDesc,
+                                  const void *bnScaleData,
+                                  const void *bnBiasData, /* needed if there is activation */
+                                  void *dBnScaleData,
+                                  void *dBnBiasData,
+                                  double epsilon, /* Same epsilon as forward pass */
+
+                                  /* Optionally cached intermediate results from
+                                     forward pass */
+                                  const void *savedMean,
+                                  const void *savedInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  void *workSpace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationForwardTrainingWorkspaceSize(cudnnHandle_t handle,
+                                                  cudnnNormMode_t mode,
+                                                  cudnnNormOps_t normOps,
+                                                  cudnnNormAlgo_t algo,
+                                                  const cudnnTensorDescriptor_t xDesc,
+                                                  const cudnnTensorDescriptor_t zDesc,
+                                                  const cudnnTensorDescriptor_t yDesc,
+                                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                                  const cudnnActivationDescriptor_t activationDesc,
+                                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                                  size_t *sizeInBytes,
+                                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationBackwardWorkspaceSize(cudnnHandle_t handle,
+                                           cudnnNormMode_t mode,
+                                           cudnnNormOps_t normOps,
+                                           cudnnNormAlgo_t algo,
+                                           const cudnnTensorDescriptor_t xDesc,
+                                           const cudnnTensorDescriptor_t yDesc,
+                                           const cudnnTensorDescriptor_t dyDesc,
+                                           const cudnnTensorDescriptor_t dzDesc,
+                                           const cudnnTensorDescriptor_t dxDesc,
+                                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                                           const cudnnActivationDescriptor_t activationDesc,
+                                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                                           size_t *sizeInBytes,
+                                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnGetNormalizationTrainingReserveSpaceSize(cudnnHandle_t handle,
+                                              cudnnNormMode_t mode,
+                                              cudnnNormOps_t normOps,
+                                              cudnnNormAlgo_t algo,
+                                              const cudnnActivationDescriptor_t activationDesc,
+                                              const cudnnTensorDescriptor_t xDesc,
+                                              size_t *sizeInBytes,
+                                              int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+/* Computes y = relu(Norm(x) + z). Also accumulates moving averages of mean and inverse variances */
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationForwardTraining(cudnnHandle_t handle,
+                                  cudnnNormMode_t mode,
+                                  cudnnNormOps_t normOps,
+                                  cudnnNormAlgo_t algo,
+                                  const void *alpha, /* alpha[0] = result blend factor */
+                                  const void *beta,  /* beta[0] = dest layer blend factor */
+                                  const cudnnTensorDescriptor_t xDesc,
+                                  const void *xData,
+                                  const cudnnTensorDescriptor_t normScaleBiasDesc,
+                                  const void *normScale,
+                                  const void *normBias,
+                                  double exponentialAverageFactor,
+                                  const cudnnTensorDescriptor_t normMeanVarDesc,
+                                  void *resultRunningMean,
+                                  void *resultRunningVariance,
+                                  /* Has to be >= 0. Should be the same in forward and backward functions. */
+                                  double epsilon,
+                                  /* Optionally save intermediate results from the forward pass here
+                                     - can be reused to speed up backward pass. NULL if unused */
+                                  void *resultSaveMean,
+                                  void *resultSaveInvVariance,
+                                  cudnnActivationDescriptor_t activationDesc,
+                                  const cudnnTensorDescriptor_t zDesc,
+                                  const void *zData,
+                                  const cudnnTensorDescriptor_t yDesc,
+                                  void *yData,
+                                  void *workspace,
+                                  size_t workSpaceSizeInBytes,
+                                  void *reserveSpace,
+                                  size_t reserveSpaceSizeInBytes,
+                                  int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnNormalizationBackward(cudnnHandle_t handle,
+                           cudnnNormMode_t mode,
+                           cudnnNormOps_t normOps,
+                           cudnnNormAlgo_t algo,
+                           const void *alphaDataDiff,
+                           const void *betaDataDiff,
+                           const void *alphaParamDiff,
+                           const void *betaParamDiff,
+                           const cudnnTensorDescriptor_t xDesc,
+                           const void *xData,
+                           const cudnnTensorDescriptor_t yDesc,
+                           const void *yData,
+                           const cudnnTensorDescriptor_t dyDesc,
+                           const void *dyData,
+                           const cudnnTensorDescriptor_t dzDesc,
+                           void *dzData,
+                           const cudnnTensorDescriptor_t dxDesc,
+                           void *dxData,
+                           /* Shared tensor desc for the 4 tensors below */
+                           const cudnnTensorDescriptor_t dNormScaleBiasDesc,
+                           const void *normScaleData,
+                           const void *normBiasData, /* needed if there is activation */
+                           void *dNormScaleData,
+                           void *dNormBiasData,
+                           double epsilon, /* Same epsilon as forward pass */
+                           const cudnnTensorDescriptor_t normMeanVarDesc,
+                           /* Optionally cached intermediate results from
+                              forward pass */
+                           const void *savedMean,
+                           const void *savedInvVariance,
+                           cudnnActivationDescriptor_t activationDesc,
+                           void *workSpace,
+                           size_t workSpaceSizeInBytes,
+                           void *reserveSpace,
+                           size_t reserveSpaceSizeInBytes,
+                           int groupCnt); /* Place hold for future work, should be set to 1 now*/
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfGridGeneratorBackward(cudnnHandle_t handle,
+                                    const cudnnSpatialTransformerDescriptor_t stDesc,
+                                    const void *dgrid,
+                                    void *dtheta);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnSpatialTfSamplerBackward(cudnnHandle_t handle,
+                              cudnnSpatialTransformerDescriptor_t stDesc,
+                              const void *alpha,
+                              const cudnnTensorDescriptor_t xDesc,
+                              const void *x,
+                              const void *beta,
+                              const cudnnTensorDescriptor_t dxDesc,
+                              void *dx,
+                              const void *alphaDgrid,
+                              const cudnnTensorDescriptor_t dyDesc,
+                              const void *dy,
+                              const void *grid,
+                              const void *betaDgrid,
+                              void *dgrid);
+
+cudnnStatus_t CUDNNWINAPI
+cudnnDropoutBackward(cudnnHandle_t handle,
+                     const cudnnDropoutDescriptor_t dropoutDesc,
+                     const cudnnTensorDescriptor_t dydesc,
+                     const void *dy,
+                     const cudnnTensorDescriptor_t dxdesc,
+                     void *dx,
+                     void *reserveSpace,
+                     size_t reserveSpaceSizeInBytes);
+
+/*
+ * \brief Cross-library version checker.
+ * This function is implemented differently in each sub-library. Each sublib
+ * checks whether its own version matches that of its dependencies.
+ * \returns CUDNN_STATUS_SUCCESS if the version check passes,
+ *          CUDNN_STATUS_VERSION_MISMATCH if the versions are inconsistent.
+ */
+cudnnStatus_t CUDNNWINAPI
+cudnnOpsTrainVersionCheck(void);
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_OPS_TRAIN_H_ */

openflamingo/lib/python3.10/site-packages/nvidia/cudnn/include/cudnn_v8.h

@@ -0,0 +1,78 @@
+/*
+ * Copyright 2017-2022 NVIDIA Corporation.  All rights reserved.
+ *
+ * NOTICE TO LICENSEE:
+ *
+ * This source code and/or documentation ("Licensed Deliverables") are
+ * subject to NVIDIA intellectual property rights under U.S. and
+ * international Copyright laws.
+ *
+ * These Licensed Deliverables contained herein is PROPRIETARY and
+ * CONFIDENTIAL to NVIDIA and is being provided under the terms and
+ * conditions of a form of NVIDIA software license agreement by and
+ * between NVIDIA and Licensee ("License Agreement") or electronically
+ * accepted by Licensee.  Notwithstanding any terms or conditions to
+ * the contrary in the License Agreement, reproduction or disclosure
+ * of the Licensed Deliverables to any third party without the express
+ * written consent of NVIDIA is prohibited.
+ *
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, NVIDIA MAKES NO REPRESENTATION ABOUT THE
+ * SUITABILITY OF THESE LICENSED DELIVERABLES FOR ANY PURPOSE.  IT IS
+ * PROVIDED "AS IS" WITHOUT EXPRESS OR IMPLIED WARRANTY OF ANY KIND.
+ * NVIDIA DISCLAIMS ALL WARRANTIES WITH REGARD TO THESE LICENSED
+ * DELIVERABLES, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY,
+ * NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
+ * NOTWITHSTANDING ANY TERMS OR CONDITIONS TO THE CONTRARY IN THE
+ * LICENSE AGREEMENT, IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY
+ * SPECIAL, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, OR ANY
+ * DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+ * WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ * ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+ * OF THESE LICENSED DELIVERABLES.
+ *
+ * U.S. Government End Users.  These Licensed Deliverables are a
+ * "commercial item" as that term is defined at 48 C.F.R. 2.101 (OCT
+ * 1995), consisting of "commercial computer software" and "commercial
+ * computer software documentation" as such terms are used in 48
+ * C.F.R. 12.212 (SEPT 1995) and is provided to the U.S. Government
+ * only as a commercial end item.  Consistent with 48 C.F.R.12.212 and
+ * 48 C.F.R. 227.7202-1 through 227.7202-4 (JUNE 1995), all
+ * U.S. Government End Users acquire the Licensed Deliverables with
+ * only those rights set forth herein.
+ *
+ * Any use of the Licensed Deliverables in individual and commercial
+ * software must include, in the user documentation and internal
+ * comments to the code, the above Disclaimer and U.S. Government End
+ * Users Notice.
+ */
+
+/*   cudnn : Neural Networks Library
+
+*/
+
+#if !defined(CUDNN_H_)
+#define CUDNN_H_
+
+#include <cuda_runtime.h>
+#include <stdint.h>
+
+#include "cudnn_version.h"
+#include "cudnn_ops_infer.h"
+#include "cudnn_ops_train.h"
+#include "cudnn_adv_infer.h"
+#include "cudnn_adv_train.h"
+#include "cudnn_cnn_infer.h"
+#include "cudnn_cnn_train.h"
+
+#include "cudnn_backend.h"
+
+#if defined(__cplusplus)
+extern "C" {
+#endif
+
+#if defined(__cplusplus)
+}
+#endif
+
+#endif /* CUDNN_H_ */

openflamingo/lib/python3.10/site-packages/regex/_regex.cpython-310-x86_64-linux-gnu.so

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+version https://git-lfs.github.com/spec/v1
+oid sha256:29dd755c12af2aebb0e796b4bdd3878ddecf511ecd096f2a0e534c6ad6860f2c
+size 2549016

openflamingo/lib/python3.10/site-packages/six-1.17.0.dist-info/INSTALLER

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

openflamingo/lib/python3.10/site-packages/six-1.17.0.dist-info/LICENSE

@@ -0,0 +1,18 @@
+Copyright (c) 2010-2024 Benjamin Peterson
+
+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.

openflamingo/lib/python3.10/site-packages/six-1.17.0.dist-info/METADATA

@@ -0,0 +1,43 @@
+Metadata-Version: 2.1
+Name: six
+Version: 1.17.0
+Summary: Python 2 and 3 compatibility utilities
+Home-page: https://github.com/benjaminp/six
+Author: Benjamin Peterson
+Author-email: benjamin@python.org
+License: MIT
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 3
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Utilities
+Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*
+License-File: LICENSE
+
+.. image:: https://img.shields.io/pypi/v/six.svg
+   :target: https://pypi.org/project/six/
+   :alt: six on PyPI
+
+.. image:: https://readthedocs.org/projects/six/badge/?version=latest
+   :target: https://six.readthedocs.io/
+   :alt: six's documentation on Read the Docs
+
+.. image:: https://img.shields.io/badge/license-MIT-green.svg
+   :target: https://github.com/benjaminp/six/blob/master/LICENSE
+   :alt: MIT License badge
+
+Six is a Python 2 and 3 compatibility library.  It provides utility functions
+for smoothing over the differences between the Python versions with the goal of
+writing Python code that is compatible on both Python versions.  See the
+documentation for more information on what is provided.
+
+Six supports Python 2.7 and 3.3+.  It is contained in only one Python
+file, so it can be easily copied into your project. (The copyright and license
+notice must be retained.)
+
+Online documentation is at https://six.readthedocs.io/.
+
+Bugs can be reported to https://github.com/benjaminp/six.  The code can also
+be found there.