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35,743 | pefile | ResourceDataEntryData | Holds resource data entry information.
struct: IMAGE_RESOURCE_DATA_ENTRY structure
lang: Primary language ID
sublang: Sublanguage ID
| class ResourceDataEntryData(DataContainer):
"""Holds resource data entry information.
struct: IMAGE_RESOURCE_DATA_ENTRY structure
lang: Primary language ID
sublang: Sublanguage ID
"""
| (**args) | [
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|
35,745 | pefile | ResourceDirData | Holds resource directory information.
struct: IMAGE_RESOURCE_DIRECTORY structure
entries: list of entries (ResourceDirEntryData instances)
| class ResourceDirData(DataContainer):
"""Holds resource directory information.
struct: IMAGE_RESOURCE_DIRECTORY structure
entries: list of entries (ResourceDirEntryData instances)
"""
| (**args) | [
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|
35,747 | pefile | ResourceDirEntryData | Holds resource directory entry data.
struct: IMAGE_RESOURCE_DIRECTORY_ENTRY structure
name: If the resource is identified by name this
attribute will contain the name string. None
otherwise. If identified by id, the id is
available at 'struct.Id'
id: the id, also in struct.Id
directory: If this entry has a lower level directory
this attribute will point to the
ResourceDirData instance representing it.
data: If this entry has no further lower directories
and points to the actual resource data, this
attribute will reference the corresponding
ResourceDataEntryData instance.
(Either of the 'directory' or 'data' attribute will exist,
but not both.)
| class ResourceDirEntryData(DataContainer):
"""Holds resource directory entry data.
struct: IMAGE_RESOURCE_DIRECTORY_ENTRY structure
name: If the resource is identified by name this
attribute will contain the name string. None
otherwise. If identified by id, the id is
available at 'struct.Id'
id: the id, also in struct.Id
directory: If this entry has a lower level directory
this attribute will point to the
ResourceDirData instance representing it.
data: If this entry has no further lower directories
and points to the actual resource data, this
attribute will reference the corresponding
ResourceDataEntryData instance.
(Either of the 'directory' or 'data' attribute will exist,
but not both.)
"""
| (**args) | [
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|
35,749 | pefile | SectionStructure | Convenience section handling class. | class SectionStructure(Structure):
"""Convenience section handling class."""
def __init__(self, *argl, **argd):
if "pe" in argd:
self.pe = argd["pe"]
del argd["pe"]
self.PointerToRawData = None
self.VirtualAddress = None
self.SizeOfRawData = None
self.Misc_VirtualSize = None
Structure.__init__(self, *argl, **argd)
self.PointerToRawData_adj = None
self.VirtualAddress_adj = None
self.section_min_addr = None
self.section_max_addr = None
def get_PointerToRawData_adj(self):
if self.PointerToRawData_adj is None:
if self.PointerToRawData is not None:
self.PointerToRawData_adj = self.pe.adjust_FileAlignment(
self.PointerToRawData, self.pe.OPTIONAL_HEADER.FileAlignment
)
return self.PointerToRawData_adj
def get_VirtualAddress_adj(self):
if self.VirtualAddress_adj is None:
if self.VirtualAddress is not None:
self.VirtualAddress_adj = self.pe.adjust_SectionAlignment(
self.VirtualAddress,
self.pe.OPTIONAL_HEADER.SectionAlignment,
self.pe.OPTIONAL_HEADER.FileAlignment,
)
return self.VirtualAddress_adj
def get_data(self, start=None, length=None, ignore_padding=False):
"""Get data chunk from a section.
Allows to query data from the section by passing the
addresses where the PE file would be loaded by default.
It is then possible to retrieve code and data by their real
addresses as they would be if loaded.
Note that sections on disk can include padding that would
not be loaded to memory. That is the case if `section.SizeOfRawData`
is greater than `section.Misc_VirtualSize`, and that means
that data past `section.Misc_VirtualSize` is padding.
In case you are not interested in this padding, passing
`ignore_padding=True` will truncate the result in order
not to return the padding (if any).
Returns bytes() under Python 3.x and set() under Python 2.7
"""
if start is None:
offset = self.get_PointerToRawData_adj()
else:
offset = (
start - self.get_VirtualAddress_adj()
) + self.get_PointerToRawData_adj()
if length is not None:
end = offset + length
elif self.SizeOfRawData is not None:
end = offset + self.SizeOfRawData
else:
end = offset
if ignore_padding and end is not None and offset is not None:
end = min(end, offset + self.Misc_VirtualSize)
# PointerToRawData is not adjusted here as we might want to read any possible
# extra bytes that might get cut off by aligning the start (and hence cutting
# something off the end)
if self.PointerToRawData is not None and self.SizeOfRawData is not None:
if end > self.PointerToRawData + self.SizeOfRawData:
end = self.PointerToRawData + self.SizeOfRawData
return self.pe.__data__[offset:end]
def __setattr__(self, name, val):
if name == "Characteristics":
section_flags = retrieve_flags(SECTION_CHARACTERISTICS, "IMAGE_SCN_")
# Set the section's flags according to the Characteristics member
set_flags(self, val, section_flags)
elif "IMAGE_SCN_" in name and hasattr(self, name):
if val:
self.__dict__["Characteristics"] |= SECTION_CHARACTERISTICS[name]
else:
self.__dict__["Characteristics"] ^= SECTION_CHARACTERISTICS[name]
self.__dict__[name] = val
def get_rva_from_offset(self, offset):
return offset - self.get_PointerToRawData_adj() + self.get_VirtualAddress_adj()
def get_offset_from_rva(self, rva):
return rva - self.get_VirtualAddress_adj() + self.get_PointerToRawData_adj()
def contains_offset(self, offset):
"""Check whether the section contains the file offset provided."""
if self.PointerToRawData is None:
# bss and other sections containing only uninitialized data must have 0
# and do not take space in the file
return False
PointerToRawData_adj = self.get_PointerToRawData_adj()
return (
PointerToRawData_adj <= offset < PointerToRawData_adj + self.SizeOfRawData
)
def contains_rva(self, rva):
"""Check whether the section contains the address provided."""
# speedup
if self.section_min_addr is not None and self.section_max_addr is not None:
return self.section_min_addr <= rva < self.section_max_addr
VirtualAddress_adj = self.get_VirtualAddress_adj()
# Check if the SizeOfRawData is realistic. If it's bigger than the size of
# the whole PE file minus the start address of the section it could be
# either truncated or the SizeOfRawData contains a misleading value.
# In either of those cases we take the VirtualSize
#
if len(self.pe.__data__) - self.get_PointerToRawData_adj() < self.SizeOfRawData:
# PECOFF documentation v8 says:
# VirtualSize: The total size of the section when loaded into memory.
# If this value is greater than SizeOfRawData, the section is zero-padded.
# This field is valid only for executable images and should be set to zero
# for object files.
#
size = self.Misc_VirtualSize
else:
size = max(self.SizeOfRawData, self.Misc_VirtualSize)
# Check whether there's any section after the current one that starts before
# the calculated end for the current one. If so, cut the current section's size
# to fit in the range up to where the next section starts.
if (
self.next_section_virtual_address is not None
and self.next_section_virtual_address > self.VirtualAddress
and VirtualAddress_adj + size > self.next_section_virtual_address
):
size = self.next_section_virtual_address - VirtualAddress_adj
self.section_min_addr = VirtualAddress_adj
self.section_max_addr = VirtualAddress_adj + size
return VirtualAddress_adj <= rva < VirtualAddress_adj + size
def contains(self, rva):
return self.contains_rva(rva)
def get_entropy(self):
"""Calculate and return the entropy for the section."""
return self.entropy_H(self.get_data())
def get_hash_sha1(self):
"""Get the SHA-1 hex-digest of the section's data."""
if sha1 is not None:
return sha1(self.get_data()).hexdigest()
def get_hash_sha256(self):
"""Get the SHA-256 hex-digest of the section's data."""
if sha256 is not None:
return sha256(self.get_data()).hexdigest()
def get_hash_sha512(self):
"""Get the SHA-512 hex-digest of the section's data."""
if sha512 is not None:
return sha512(self.get_data()).hexdigest()
def get_hash_md5(self):
"""Get the MD5 hex-digest of the section's data."""
if md5 is not None:
return md5(self.get_data()).hexdigest()
def entropy_H(self, data):
"""Calculate the entropy of a chunk of data."""
if not data:
return 0.0
occurences = Counter(bytearray(data))
entropy = 0
for x in occurences.values():
p_x = float(x) / len(data)
entropy -= p_x * math.log(p_x, 2)
return entropy
| (*argl, **argd) | [
0.05845419690012932,
-0.023966221138834953,
-0.030799316242337227,
0.024308884516358376,
0.006263669580221176,
-0.021124137565493584,
-0.05434224754571915,
-0.008939460851252079,
-0.05204439163208008,
-0.05861544981598854,
0.014149947091937065,
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|
35,750 | pefile | __get_format__ | null | def __get_format__(self) -> str:
return self.__format_str__
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|
35,751 | pefile | __init__ | null | def __init__(self, *argl, **argd):
if "pe" in argd:
self.pe = argd["pe"]
del argd["pe"]
self.PointerToRawData = None
self.VirtualAddress = None
self.SizeOfRawData = None
self.Misc_VirtualSize = None
Structure.__init__(self, *argl, **argd)
self.PointerToRawData_adj = None
self.VirtualAddress_adj = None
self.section_min_addr = None
self.section_max_addr = None
| (self, *argl, **argd) | [
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|
35,752 | pefile | __pack__ | null | def __pack__(self):
new_values = []
for idx, val in enumerate(self.__unpacked_data_elms__):
new_val = None
for key in self.__keys__[idx]:
new_val = getattr(self, key)
# In the case of unions, when the first changed value
# is picked the loop is exited
if new_val != val:
break
new_values.append(new_val)
return struct.pack(self.__format_str__, *new_values)
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|
35,753 | pefile | __repr__ | null | def __repr__(self):
return "<Structure: %s>" % (
" ".join([" ".join(s.split()) for s in self.dump()])
)
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|
35,754 | pefile | __setattr__ | null | def __setattr__(self, name, val):
if name == "Characteristics":
section_flags = retrieve_flags(SECTION_CHARACTERISTICS, "IMAGE_SCN_")
# Set the section's flags according to the Characteristics member
set_flags(self, val, section_flags)
elif "IMAGE_SCN_" in name and hasattr(self, name):
if val:
self.__dict__["Characteristics"] |= SECTION_CHARACTERISTICS[name]
else:
self.__dict__["Characteristics"] ^= SECTION_CHARACTERISTICS[name]
self.__dict__[name] = val
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|
35,755 | pefile | __str__ | null | def __str__(self):
return "\n".join(self.dump())
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|
35,756 | pefile | __unpack__ | null | def __unpack__(self, data):
data = b(data)
if len(data) > self.__format_length__:
data = data[: self.__format_length__]
# OC Patch:
# Some malware have incorrect header lengths.
# Fail gracefully if this occurs
# Buggy malware: a29b0118af8b7408444df81701ad5a7f
#
elif len(data) < self.__format_length__:
raise PEFormatError("Data length less than expected header length.")
if count_zeroes(data) == len(data):
self.__all_zeroes__ = True
self.__unpacked_data_elms__ = struct.unpack(self.__format_str__, data)
for idx, val in enumerate(self.__unpacked_data_elms__):
for key in self.__keys__[idx]:
setattr(self, key, val)
| (self, data) | [
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|
35,757 | pefile | all_zeroes | Returns true is the unpacked data is all zeros. | def all_zeroes(self):
"""Returns true is the unpacked data is all zeros."""
return self.__all_zeroes__
| (self) | [
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|
35,758 | pefile | contains | null | def contains(self, rva):
return self.contains_rva(rva)
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|
35,759 | pefile | contains_offset | Check whether the section contains the file offset provided. | def contains_offset(self, offset):
"""Check whether the section contains the file offset provided."""
if self.PointerToRawData is None:
# bss and other sections containing only uninitialized data must have 0
# and do not take space in the file
return False
PointerToRawData_adj = self.get_PointerToRawData_adj()
return (
PointerToRawData_adj <= offset < PointerToRawData_adj + self.SizeOfRawData
)
| (self, offset) | [
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|
35,760 | pefile | contains_rva | Check whether the section contains the address provided. | def contains_rva(self, rva):
"""Check whether the section contains the address provided."""
# speedup
if self.section_min_addr is not None and self.section_max_addr is not None:
return self.section_min_addr <= rva < self.section_max_addr
VirtualAddress_adj = self.get_VirtualAddress_adj()
# Check if the SizeOfRawData is realistic. If it's bigger than the size of
# the whole PE file minus the start address of the section it could be
# either truncated or the SizeOfRawData contains a misleading value.
# In either of those cases we take the VirtualSize
#
if len(self.pe.__data__) - self.get_PointerToRawData_adj() < self.SizeOfRawData:
# PECOFF documentation v8 says:
# VirtualSize: The total size of the section when loaded into memory.
# If this value is greater than SizeOfRawData, the section is zero-padded.
# This field is valid only for executable images and should be set to zero
# for object files.
#
size = self.Misc_VirtualSize
else:
size = max(self.SizeOfRawData, self.Misc_VirtualSize)
# Check whether there's any section after the current one that starts before
# the calculated end for the current one. If so, cut the current section's size
# to fit in the range up to where the next section starts.
if (
self.next_section_virtual_address is not None
and self.next_section_virtual_address > self.VirtualAddress
and VirtualAddress_adj + size > self.next_section_virtual_address
):
size = self.next_section_virtual_address - VirtualAddress_adj
self.section_min_addr = VirtualAddress_adj
self.section_max_addr = VirtualAddress_adj + size
return VirtualAddress_adj <= rva < VirtualAddress_adj + size
| (self, rva) | [
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|
35,761 | pefile | dump | Returns a string representation of the structure. | def dump(self, indentation=0):
"""Returns a string representation of the structure."""
dump = []
dump.append("[{0}]".format(self.name))
printable_bytes = [
ord(i) for i in string.printable if i not in string.whitespace
]
# Refer to the __set_format__ method for an explanation
# of the following construct.
for keys in self.__keys__:
for key in keys:
val = getattr(self, key)
if isinstance(val, (int, long)):
if key.startswith("Signature_"):
val_str = "{:<8X}".format(val)
else:
val_str = "0x{:<8X}".format(val)
if key == "TimeDateStamp" or key == "dwTimeStamp":
try:
val_str += " [%s UTC]" % time.asctime(time.gmtime(val))
except ValueError:
val_str += " [INVALID TIME]"
else:
val_str = bytearray(val)
if key.startswith("Signature"):
val_str = "".join(
["{:02X}".format(i) for i in val_str.rstrip(b"\x00")]
)
else:
val_str = "".join(
[
chr(i)
if (i in printable_bytes)
else "\\x{0:02x}".format(i)
for i in val_str.rstrip(b"\x00")
]
)
dump.append(
"0x%-8X 0x%-3X %-30s %s"
% (
self.__field_offsets__[key] + self.__file_offset__,
self.__field_offsets__[key],
key + ":",
val_str,
)
)
return dump
| (self, indentation=0) | [
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|
35,762 | pefile | dump_dict | Returns a dictionary representation of the structure. | def dump_dict(self):
"""Returns a dictionary representation of the structure."""
dump_dict = {}
dump_dict["Structure"] = self.name
# Refer to the __set_format__ method for an explanation
# of the following construct.
for keys in self.__keys__:
for key in keys:
val = getattr(self, key)
if isinstance(val, (int, long)):
if key == "TimeDateStamp" or key == "dwTimeStamp":
try:
val = "0x%-8X [%s UTC]" % (
val,
time.asctime(time.gmtime(val)),
)
except ValueError:
val = "0x%-8X [INVALID TIME]" % val
else:
val = "".join(
chr(d) if chr(d) in string.printable else "\\x%02x" % d
for d in [ord(c) if not isinstance(c, int) else c for c in val]
)
dump_dict[key] = {
"FileOffset": self.__field_offsets__[key] + self.__file_offset__,
"Offset": self.__field_offsets__[key],
"Value": val,
}
return dump_dict
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|
35,763 | pefile | entropy_H | Calculate the entropy of a chunk of data. | def entropy_H(self, data):
"""Calculate the entropy of a chunk of data."""
if not data:
return 0.0
occurences = Counter(bytearray(data))
entropy = 0
for x in occurences.values():
p_x = float(x) / len(data)
entropy -= p_x * math.log(p_x, 2)
return entropy
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|
35,764 | pefile | get_PointerToRawData_adj | null | def get_PointerToRawData_adj(self):
if self.PointerToRawData_adj is None:
if self.PointerToRawData is not None:
self.PointerToRawData_adj = self.pe.adjust_FileAlignment(
self.PointerToRawData, self.pe.OPTIONAL_HEADER.FileAlignment
)
return self.PointerToRawData_adj
| (self) | [
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|
35,765 | pefile | get_VirtualAddress_adj | null | def get_VirtualAddress_adj(self):
if self.VirtualAddress_adj is None:
if self.VirtualAddress is not None:
self.VirtualAddress_adj = self.pe.adjust_SectionAlignment(
self.VirtualAddress,
self.pe.OPTIONAL_HEADER.SectionAlignment,
self.pe.OPTIONAL_HEADER.FileAlignment,
)
return self.VirtualAddress_adj
| (self) | [
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|
35,766 | pefile | get_data | Get data chunk from a section.
Allows to query data from the section by passing the
addresses where the PE file would be loaded by default.
It is then possible to retrieve code and data by their real
addresses as they would be if loaded.
Note that sections on disk can include padding that would
not be loaded to memory. That is the case if `section.SizeOfRawData`
is greater than `section.Misc_VirtualSize`, and that means
that data past `section.Misc_VirtualSize` is padding.
In case you are not interested in this padding, passing
`ignore_padding=True` will truncate the result in order
not to return the padding (if any).
Returns bytes() under Python 3.x and set() under Python 2.7
| def get_data(self, start=None, length=None, ignore_padding=False):
"""Get data chunk from a section.
Allows to query data from the section by passing the
addresses where the PE file would be loaded by default.
It is then possible to retrieve code and data by their real
addresses as they would be if loaded.
Note that sections on disk can include padding that would
not be loaded to memory. That is the case if `section.SizeOfRawData`
is greater than `section.Misc_VirtualSize`, and that means
that data past `section.Misc_VirtualSize` is padding.
In case you are not interested in this padding, passing
`ignore_padding=True` will truncate the result in order
not to return the padding (if any).
Returns bytes() under Python 3.x and set() under Python 2.7
"""
if start is None:
offset = self.get_PointerToRawData_adj()
else:
offset = (
start - self.get_VirtualAddress_adj()
) + self.get_PointerToRawData_adj()
if length is not None:
end = offset + length
elif self.SizeOfRawData is not None:
end = offset + self.SizeOfRawData
else:
end = offset
if ignore_padding and end is not None and offset is not None:
end = min(end, offset + self.Misc_VirtualSize)
# PointerToRawData is not adjusted here as we might want to read any possible
# extra bytes that might get cut off by aligning the start (and hence cutting
# something off the end)
if self.PointerToRawData is not None and self.SizeOfRawData is not None:
if end > self.PointerToRawData + self.SizeOfRawData:
end = self.PointerToRawData + self.SizeOfRawData
return self.pe.__data__[offset:end]
| (self, start=None, length=None, ignore_padding=False) | [
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|
35,767 | pefile | get_entropy | Calculate and return the entropy for the section. | def get_entropy(self):
"""Calculate and return the entropy for the section."""
return self.entropy_H(self.get_data())
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|
35,768 | pefile | get_field_absolute_offset | Return the offset within the field for the requested field in the structure. | def get_field_absolute_offset(self, field_name):
"""Return the offset within the field for the requested field in the structure."""
return self.__file_offset__ + self.__field_offsets__[field_name]
| (self, field_name) | [
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|
35,769 | pefile | get_field_relative_offset | Return the offset within the structure for the requested field. | def get_field_relative_offset(self, field_name):
"""Return the offset within the structure for the requested field."""
return self.__field_offsets__[field_name]
| (self, field_name) | [
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|
35,770 | pefile | get_file_offset | null | def get_file_offset(self):
return self.__file_offset__
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|
35,771 | pefile | get_hash_md5 | Get the MD5 hex-digest of the section's data. | def get_hash_md5(self):
"""Get the MD5 hex-digest of the section's data."""
if md5 is not None:
return md5(self.get_data()).hexdigest()
| (self) | [
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|
35,772 | pefile | get_hash_sha1 | Get the SHA-1 hex-digest of the section's data. | def get_hash_sha1(self):
"""Get the SHA-1 hex-digest of the section's data."""
if sha1 is not None:
return sha1(self.get_data()).hexdigest()
| (self) | [
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|
35,773 | pefile | get_hash_sha256 | Get the SHA-256 hex-digest of the section's data. | def get_hash_sha256(self):
"""Get the SHA-256 hex-digest of the section's data."""
if sha256 is not None:
return sha256(self.get_data()).hexdigest()
| (self) | [
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|
35,774 | pefile | get_hash_sha512 | Get the SHA-512 hex-digest of the section's data. | def get_hash_sha512(self):
"""Get the SHA-512 hex-digest of the section's data."""
if sha512 is not None:
return sha512(self.get_data()).hexdigest()
| (self) | [
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|
35,775 | pefile | get_offset_from_rva | null | def get_offset_from_rva(self, rva):
return rva - self.get_VirtualAddress_adj() + self.get_PointerToRawData_adj()
| (self, rva) | [
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|
35,776 | pefile | get_rva_from_offset | null | def get_rva_from_offset(self, offset):
return offset - self.get_PointerToRawData_adj() + self.get_VirtualAddress_adj()
| (self, offset) | [
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|
35,777 | pefile | set_file_offset | null | def set_file_offset(self, offset):
self.__file_offset__ = offset
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|
35,778 | pefile | sizeof | Return size of the structure. | def sizeof(self):
"""Return size of the structure."""
return self.__format_length__
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]
|
35,779 | pefile | Structure | Prepare structure object to extract members from data.
Format is a list containing definitions for the elements
of the structure.
| class Structure:
"""Prepare structure object to extract members from data.
Format is a list containing definitions for the elements
of the structure.
"""
def __init__(self, format, name=None, file_offset=None):
# Format is forced little endian, for big endian non Intel platforms
self.__format_str__ = "<"
self.__keys__ = []
self.__format_length__ = 0
self.__field_offsets__ = {}
self.__unpacked_data_elms__ = []
d = format[1]
# need a tuple to be hashable in set_format using lru cache
if not isinstance(d, tuple):
d = tuple(d)
(
self.__format_str__,
self.__unpacked_data_elms__,
self.__field_offsets__,
self.__keys__,
self.__format_length__,
) = set_format(d)
self.__all_zeroes__ = False
self.__file_offset__ = file_offset
if name:
self.name = name
else:
self.name = format[0]
def __get_format__(self) -> str:
return self.__format_str__
def get_field_absolute_offset(self, field_name):
"""Return the offset within the field for the requested field in the structure."""
return self.__file_offset__ + self.__field_offsets__[field_name]
def get_field_relative_offset(self, field_name):
"""Return the offset within the structure for the requested field."""
return self.__field_offsets__[field_name]
def get_file_offset(self):
return self.__file_offset__
def set_file_offset(self, offset):
self.__file_offset__ = offset
def all_zeroes(self):
"""Returns true is the unpacked data is all zeros."""
return self.__all_zeroes__
def sizeof(self):
"""Return size of the structure."""
return self.__format_length__
def __unpack__(self, data):
data = b(data)
if len(data) > self.__format_length__:
data = data[: self.__format_length__]
# OC Patch:
# Some malware have incorrect header lengths.
# Fail gracefully if this occurs
# Buggy malware: a29b0118af8b7408444df81701ad5a7f
#
elif len(data) < self.__format_length__:
raise PEFormatError("Data length less than expected header length.")
if count_zeroes(data) == len(data):
self.__all_zeroes__ = True
self.__unpacked_data_elms__ = struct.unpack(self.__format_str__, data)
for idx, val in enumerate(self.__unpacked_data_elms__):
for key in self.__keys__[idx]:
setattr(self, key, val)
def __pack__(self):
new_values = []
for idx, val in enumerate(self.__unpacked_data_elms__):
new_val = None
for key in self.__keys__[idx]:
new_val = getattr(self, key)
# In the case of unions, when the first changed value
# is picked the loop is exited
if new_val != val:
break
new_values.append(new_val)
return struct.pack(self.__format_str__, *new_values)
def __str__(self):
return "\n".join(self.dump())
def __repr__(self):
return "<Structure: %s>" % (
" ".join([" ".join(s.split()) for s in self.dump()])
)
def dump(self, indentation=0):
"""Returns a string representation of the structure."""
dump = []
dump.append("[{0}]".format(self.name))
printable_bytes = [
ord(i) for i in string.printable if i not in string.whitespace
]
# Refer to the __set_format__ method for an explanation
# of the following construct.
for keys in self.__keys__:
for key in keys:
val = getattr(self, key)
if isinstance(val, (int, long)):
if key.startswith("Signature_"):
val_str = "{:<8X}".format(val)
else:
val_str = "0x{:<8X}".format(val)
if key == "TimeDateStamp" or key == "dwTimeStamp":
try:
val_str += " [%s UTC]" % time.asctime(time.gmtime(val))
except ValueError:
val_str += " [INVALID TIME]"
else:
val_str = bytearray(val)
if key.startswith("Signature"):
val_str = "".join(
["{:02X}".format(i) for i in val_str.rstrip(b"\x00")]
)
else:
val_str = "".join(
[
chr(i)
if (i in printable_bytes)
else "\\x{0:02x}".format(i)
for i in val_str.rstrip(b"\x00")
]
)
dump.append(
"0x%-8X 0x%-3X %-30s %s"
% (
self.__field_offsets__[key] + self.__file_offset__,
self.__field_offsets__[key],
key + ":",
val_str,
)
)
return dump
def dump_dict(self):
"""Returns a dictionary representation of the structure."""
dump_dict = {}
dump_dict["Structure"] = self.name
# Refer to the __set_format__ method for an explanation
# of the following construct.
for keys in self.__keys__:
for key in keys:
val = getattr(self, key)
if isinstance(val, (int, long)):
if key == "TimeDateStamp" or key == "dwTimeStamp":
try:
val = "0x%-8X [%s UTC]" % (
val,
time.asctime(time.gmtime(val)),
)
except ValueError:
val = "0x%-8X [INVALID TIME]" % val
else:
val = "".join(
chr(d) if chr(d) in string.printable else "\\x%02x" % d
for d in [ord(c) if not isinstance(c, int) else c for c in val]
)
dump_dict[key] = {
"FileOffset": self.__field_offsets__[key] + self.__file_offset__,
"Offset": self.__field_offsets__[key],
"Value": val,
}
return dump_dict
| (format, name=None, file_offset=None) | [
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|
35,781 | pefile | __init__ | null | def __init__(self, format, name=None, file_offset=None):
# Format is forced little endian, for big endian non Intel platforms
self.__format_str__ = "<"
self.__keys__ = []
self.__format_length__ = 0
self.__field_offsets__ = {}
self.__unpacked_data_elms__ = []
d = format[1]
# need a tuple to be hashable in set_format using lru cache
if not isinstance(d, tuple):
d = tuple(d)
(
self.__format_str__,
self.__unpacked_data_elms__,
self.__field_offsets__,
self.__keys__,
self.__format_length__,
) = set_format(d)
self.__all_zeroes__ = False
self.__file_offset__ = file_offset
if name:
self.name = name
else:
self.name = format[0]
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|
35,794 | pefile | StructureWithBitfields |
Extends Structure's functionality with support for bitfields such as:
('B:4,LowerHalf', 'B:4,UpperHalf')
To this end, two lists are maintained:
* self.__keys__ that contains compound fields, for example
('B,~LowerHalfUpperHalf'), and is used during packing/unpaking
* self.__keys_ext__ containing a separate key for each field (ex., LowerHalf,
UpperHalf) to simplify implementation of dump()
This way the implementation of unpacking/packing and dump() from Structure can be
reused.
In addition, we create a dictionary:
<comound_field_index_in_keys> -->
(data type, [ (subfield name, length in bits)+ ] )
that facilitates bitfield paking and unpacking.
With lru_cache() creating only once instance per format string, the memory
overhead is negligible.
| class StructureWithBitfields(Structure):
"""
Extends Structure's functionality with support for bitfields such as:
('B:4,LowerHalf', 'B:4,UpperHalf')
To this end, two lists are maintained:
* self.__keys__ that contains compound fields, for example
('B,~LowerHalfUpperHalf'), and is used during packing/unpaking
* self.__keys_ext__ containing a separate key for each field (ex., LowerHalf,
UpperHalf) to simplify implementation of dump()
This way the implementation of unpacking/packing and dump() from Structure can be
reused.
In addition, we create a dictionary:
<comound_field_index_in_keys> -->
(data type, [ (subfield name, length in bits)+ ] )
that facilitates bitfield paking and unpacking.
With lru_cache() creating only once instance per format string, the memory
overhead is negligible.
"""
BTF_NAME_IDX = 0
BTF_BITCNT_IDX = 1
CF_TYPE_IDX = 0
CF_SUBFLD_IDX = 1
def __init__(self, format, name=None, file_offset=None):
(
self.__format_str__,
self.__format_length__,
self.__field_offsets__,
self.__keys__,
self.__keys_ext__,
self.__compound_fields__,
) = set_bitfields_format(format)
# create our own unpacked_data_elms to ensure they are not shared among
# StructureWithBitfields instances with the same format string
self.__unpacked_data_elms__ = [None for i in range(self.__format_length__)]
self.__all_zeroes__ = False
self.__file_offset__ = file_offset
self.name = name if name != None else format[0]
def __unpack__(self, data):
# calling the original routine to deal with special cases/spurious data
# structures
super(StructureWithBitfields, self).__unpack__(data)
self._unpack_bitfield_attributes()
def __pack__(self):
self._pack_bitfield_attributes()
try:
data = super(StructureWithBitfields, self).__pack__()
finally:
self._unpack_bitfield_attributes()
return data
def dump(self, indentation=0):
tk = self.__keys__
self.__keys__ = self.__keys_ext__
try:
ret = super(StructureWithBitfields, self).dump(indentation)
finally:
self.__keys__ = tk
return ret
def dump_dict(self):
tk = self.__keys__
self.__keys__ = self.__keys_ext__
try:
ret = super(StructureWithBitfields, self).dump_dict()
finally:
self.__keys__ = tk
return ret
def _unpack_bitfield_attributes(self):
"""Replace compound attributes corresponding to bitfields with separate
sub-fields.
"""
for i in self.__compound_fields__.keys():
cf_name = self.__keys__[i][0]
cval = getattr(self, cf_name)
delattr(self, cf_name)
offst = 0
for sf in self.__compound_fields__[i][StructureWithBitfields.CF_SUBFLD_IDX]:
mask = (1 << sf[StructureWithBitfields.BTF_BITCNT_IDX]) - 1
mask <<= offst
setattr(
self,
sf[StructureWithBitfields.BTF_NAME_IDX],
(cval & mask) >> offst,
)
offst += sf[StructureWithBitfields.BTF_BITCNT_IDX]
def _pack_bitfield_attributes(self):
"""Pack attributes into a compound bitfield"""
for i in self.__compound_fields__.keys():
cf_name = self.__keys__[i][0]
offst, acc_val = 0, 0
for sf in self.__compound_fields__[i][StructureWithBitfields.CF_SUBFLD_IDX]:
mask = (1 << sf[StructureWithBitfields.BTF_BITCNT_IDX]) - 1
field_val = (
getattr(self, sf[StructureWithBitfields.BTF_NAME_IDX]) & mask
)
acc_val |= field_val << offst
offst += sf[StructureWithBitfields.BTF_BITCNT_IDX]
setattr(self, cf_name, acc_val)
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|
35,796 | pefile | __init__ | null | def __init__(self, format, name=None, file_offset=None):
(
self.__format_str__,
self.__format_length__,
self.__field_offsets__,
self.__keys__,
self.__keys_ext__,
self.__compound_fields__,
) = set_bitfields_format(format)
# create our own unpacked_data_elms to ensure they are not shared among
# StructureWithBitfields instances with the same format string
self.__unpacked_data_elms__ = [None for i in range(self.__format_length__)]
self.__all_zeroes__ = False
self.__file_offset__ = file_offset
self.name = name if name != None else format[0]
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|
35,797 | pefile | __pack__ | null | def __pack__(self):
self._pack_bitfield_attributes()
try:
data = super(StructureWithBitfields, self).__pack__()
finally:
self._unpack_bitfield_attributes()
return data
| (self) | [
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|
35,800 | pefile | __unpack__ | null | def __unpack__(self, data):
# calling the original routine to deal with special cases/spurious data
# structures
super(StructureWithBitfields, self).__unpack__(data)
self._unpack_bitfield_attributes()
| (self, data) | [
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|
35,801 | pefile | _pack_bitfield_attributes | Pack attributes into a compound bitfield | def _pack_bitfield_attributes(self):
"""Pack attributes into a compound bitfield"""
for i in self.__compound_fields__.keys():
cf_name = self.__keys__[i][0]
offst, acc_val = 0, 0
for sf in self.__compound_fields__[i][StructureWithBitfields.CF_SUBFLD_IDX]:
mask = (1 << sf[StructureWithBitfields.BTF_BITCNT_IDX]) - 1
field_val = (
getattr(self, sf[StructureWithBitfields.BTF_NAME_IDX]) & mask
)
acc_val |= field_val << offst
offst += sf[StructureWithBitfields.BTF_BITCNT_IDX]
setattr(self, cf_name, acc_val)
| (self) | [
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|
35,802 | pefile | _unpack_bitfield_attributes | Replace compound attributes corresponding to bitfields with separate
sub-fields.
| def _unpack_bitfield_attributes(self):
"""Replace compound attributes corresponding to bitfields with separate
sub-fields.
"""
for i in self.__compound_fields__.keys():
cf_name = self.__keys__[i][0]
cval = getattr(self, cf_name)
delattr(self, cf_name)
offst = 0
for sf in self.__compound_fields__[i][StructureWithBitfields.CF_SUBFLD_IDX]:
mask = (1 << sf[StructureWithBitfields.BTF_BITCNT_IDX]) - 1
mask <<= offst
setattr(
self,
sf[StructureWithBitfields.BTF_NAME_IDX],
(cval & mask) >> offst,
)
offst += sf[StructureWithBitfields.BTF_BITCNT_IDX]
| (self) | [
0.033174339681863785,
-0.012480122968554497,
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0.03518811613321304,
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|
35,804 | pefile | dump | null | def dump(self, indentation=0):
tk = self.__keys__
self.__keys__ = self.__keys_ext__
try:
ret = super(StructureWithBitfields, self).dump(indentation)
finally:
self.__keys__ = tk
return ret
| (self, indentation=0) | [
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|
35,805 | pefile | dump_dict | null | def dump_dict(self):
tk = self.__keys__
self.__keys__ = self.__keys_ext__
try:
ret = super(StructureWithBitfields, self).dump_dict()
finally:
self.__keys__ = tk
return ret
| (self) | [
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|
35,811 | pefile | TlsData | Holds TLS information.
struct: IMAGE_TLS_DIRECTORY structure
| class TlsData(DataContainer):
"""Holds TLS information.
struct: IMAGE_TLS_DIRECTORY structure
"""
| (**args) | [
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|
35,813 | pefile | UnicodeStringWrapperPostProcessor | This class attempts to help the process of identifying strings
that might be plain Unicode or Pascal. A list of strings will be
wrapped on it with the hope the overlappings will help make the
decision about their type. | class UnicodeStringWrapperPostProcessor:
"""This class attempts to help the process of identifying strings
that might be plain Unicode or Pascal. A list of strings will be
wrapped on it with the hope the overlappings will help make the
decision about their type."""
def __init__(self, pe, rva_ptr):
self.pe = pe
self.rva_ptr = rva_ptr
self.string = None
def get_rva(self):
"""Get the RVA of the string."""
return self.rva_ptr
def __str__(self):
"""Return the escaped UTF-8 representation of the string."""
return self.decode("utf-8", "backslashreplace_")
def decode(self, *args):
if not self.string:
return ""
return self.string.decode(*args)
def invalidate(self):
"""Make this instance None, to express it's no known string type."""
self = None
def render_pascal_16(self):
try:
self.string = self.pe.get_string_u_at_rva(
self.rva_ptr + 2, max_length=self.get_pascal_16_length()
)
except PEFormatError:
self.pe.get_warnings().append(
"Failed rendering pascal string, "
"attempting to read from RVA 0x{0:x}".format(self.rva_ptr + 2)
)
def get_pascal_16_length(self):
return self.__get_word_value_at_rva(self.rva_ptr)
def __get_word_value_at_rva(self, rva):
try:
data = self.pe.get_data(rva, 2)
except PEFormatError:
return False
if len(data) < 2:
return False
return struct.unpack("<H", data)[0]
def ask_unicode_16(self, next_rva_ptr):
"""The next RVA is taken to be the one immediately following this one.
Such RVA could indicate the natural end of the string and will be checked
to see if there's a Unicode NULL character there.
"""
if self.__get_word_value_at_rva(next_rva_ptr - 2) == 0:
self.length = next_rva_ptr - self.rva_ptr
return True
return False
def render_unicode_16(self):
try:
self.string = self.pe.get_string_u_at_rva(self.rva_ptr)
except PEFormatError:
self.pe.get_warnings().append(
"Failed rendering unicode string, "
"attempting to read from RVA 0x{0:x}".format(self.rva_ptr)
)
| (pe, rva_ptr) | [
0.02520565316081047,
-0.014177029952406883,
-0.003028728999197483,
0.04713401943445206,
0.029311468824744225,
-0.02353018708527088,
0.02179948426783085,
0.007226602640002966,
0.05328353866934776,
-0.05273118615150452,
0.03951156511902809,
0.07147432118654251,
-0.007350882049649954,
0.014379558153450489,
-0.01626676134765148,
0.034908633679151535,
-0.026512887328863144,
0.04551379010081291,
0.00047956794151104987,
0.02187313139438629,
-0.02916417457163334,
0.04764954745769501,
0.06871256232261658,
0.045476965606212616,
-0.014977939426898956,
-0.010697213001549244,
0.00830368883907795,
-0.04098450392484665,
-0.029919056221842766,
-0.04242061823606491,
0.007493572775274515,
0.005891752429306507,
-0.02190995402634144,
0.03238622844219208,
0.06731327623128891,
-0.020989367738366127,
-0.01084450725466013,
-0.0176384337246418,
0.0002803473034873605,
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|
35,814 | pefile | __get_word_value_at_rva | null | def __get_word_value_at_rva(self, rva):
try:
data = self.pe.get_data(rva, 2)
except PEFormatError:
return False
if len(data) < 2:
return False
return struct.unpack("<H", data)[0]
| (self, rva) | [
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|
35,815 | pefile | __init__ | null | def __init__(self, pe, rva_ptr):
self.pe = pe
self.rva_ptr = rva_ptr
self.string = None
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|
35,816 | pefile | __str__ | Return the escaped UTF-8 representation of the string. | def __str__(self):
"""Return the escaped UTF-8 representation of the string."""
return self.decode("utf-8", "backslashreplace_")
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|
35,817 | pefile | ask_unicode_16 | The next RVA is taken to be the one immediately following this one.
Such RVA could indicate the natural end of the string and will be checked
to see if there's a Unicode NULL character there.
| def ask_unicode_16(self, next_rva_ptr):
"""The next RVA is taken to be the one immediately following this one.
Such RVA could indicate the natural end of the string and will be checked
to see if there's a Unicode NULL character there.
"""
if self.__get_word_value_at_rva(next_rva_ptr - 2) == 0:
self.length = next_rva_ptr - self.rva_ptr
return True
return False
| (self, next_rva_ptr) | [
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|
35,818 | pefile | decode | null | def decode(self, *args):
if not self.string:
return ""
return self.string.decode(*args)
| (self, *args) | [
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|
35,819 | pefile | get_pascal_16_length | null | def get_pascal_16_length(self):
return self.__get_word_value_at_rva(self.rva_ptr)
| (self) | [
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|
35,820 | pefile | get_rva | Get the RVA of the string. | def get_rva(self):
"""Get the RVA of the string."""
return self.rva_ptr
| (self) | [
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|
35,821 | pefile | invalidate | Make this instance None, to express it's no known string type. | def invalidate(self):
"""Make this instance None, to express it's no known string type."""
self = None
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|
35,822 | pefile | render_pascal_16 | null | def render_pascal_16(self):
try:
self.string = self.pe.get_string_u_at_rva(
self.rva_ptr + 2, max_length=self.get_pascal_16_length()
)
except PEFormatError:
self.pe.get_warnings().append(
"Failed rendering pascal string, "
"attempting to read from RVA 0x{0:x}".format(self.rva_ptr + 2)
)
| (self) | [
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|
35,823 | pefile | render_unicode_16 | null | def render_unicode_16(self):
try:
self.string = self.pe.get_string_u_at_rva(self.rva_ptr)
except PEFormatError:
self.pe.get_warnings().append(
"Failed rendering unicode string, "
"attempting to read from RVA 0x{0:x}".format(self.rva_ptr)
)
| (self) | [
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|
35,824 | pefile | UnwindInfo | Handles the complexities of UNWIND_INFO structure:
* variable number of UWIND_CODEs
* optional ExceptionHandler and FunctionEntry fields
| class UnwindInfo(StructureWithBitfields):
"""Handles the complexities of UNWIND_INFO structure:
* variable number of UWIND_CODEs
* optional ExceptionHandler and FunctionEntry fields
"""
def __init__(self, file_offset=0):
super(UnwindInfo, self).__init__(
(
"UNWIND_INFO",
(
"B:3,Version",
"B:5,Flags",
"B,SizeOfProlog",
"B,CountOfCodes",
"B:4,FrameRegister",
"B:4,FrameOffset",
),
),
file_offset=file_offset,
)
self._full_size = super(UnwindInfo, self).sizeof()
self._opt_field_name = None
self._code_info = StructureWithBitfields(
("UNWIND_CODE", ("B,CodeOffset", "B:4,UnwindOp", "B:4,OpInfo")),
file_offset=0,
)
self._chained_entry = None
self._finished_unpacking = False
def unpack_in_stages(self, data):
"""Unpacks the UNWIND_INFO "in two calls", with the first call establishing
a full size of the structure and the second, performing the actual unpacking.
"""
if self._finished_unpacking:
return None
super(UnwindInfo, self).__unpack__(data)
codes_cnt_max = (self.CountOfCodes + 1) & ~1
hdlr_offset = (
super(UnwindInfo, self).sizeof() + codes_cnt_max * self._code_info.sizeof()
)
self._full_size = hdlr_offset + (
0 if self.Flags == 0 else STRUCT_SIZEOF_TYPES["I"]
)
if len(data) < self._full_size:
return None
if self.Version != 1 and self.Version != 2:
return "Unsupported version of UNWIND_INFO at " + hex(self.__file_offset__)
self.UnwindCodes = []
ro = super(UnwindInfo, self).sizeof()
codes_left = self.CountOfCodes
while codes_left > 0:
self._code_info.__unpack__(data[ro : ro + self._code_info.sizeof()])
ucode = PrologEpilogOpsFactory.create(self._code_info)
if ucode is None:
return "Unknown UNWIND_CODE at " + hex(self.__file_offset__ + ro)
len_in_codes = ucode.length_in_code_structures(self._code_info, self)
opc_size = self._code_info.sizeof() * len_in_codes
ucode.initialize(
self._code_info,
data[ro : ro + opc_size],
self,
self.__file_offset__ + ro,
)
ro += opc_size
codes_left -= len_in_codes
self.UnwindCodes.append(ucode)
if self.UNW_FLAG_EHANDLER or self.UNW_FLAG_UHANDLER:
self._opt_field_name = "ExceptionHandler"
if self.UNW_FLAG_CHAININFO:
self._opt_field_name = "FunctionEntry"
if self._opt_field_name != None:
setattr(
self,
self._opt_field_name,
struct.unpack(
"<I", data[hdlr_offset : hdlr_offset + STRUCT_SIZEOF_TYPES["I"]]
)[0],
)
self._finished_unpacking = True
return None
def dump(self, indentation=0):
# Because __keys_ext__ are shared among all the instances with the same
# format string, we have to add and sunsequently remove the optional field
# each time.
# It saves space (as compared to keeping a copy self.__keys_ext__ per
# UnwindInfo instance), but makes our dump() implementation thread-unsafe.
if self._opt_field_name != None:
self.__field_offsets__[self._opt_field_name] = (
self._full_size - STRUCT_SIZEOF_TYPES["I"]
)
self.__keys_ext__.append([self._opt_field_name])
try:
dump = super(UnwindInfo, self).dump(indentation)
finally:
if self._opt_field_name != None:
self.__keys_ext__.pop()
dump.append(
"Flags: "
+ ", ".join([s[0] for s in unwind_info_flags if getattr(self, s[0])])
)
dump.append(
"Unwind codes: "
+ "; ".join([str(c) for c in self.UnwindCodes if c.is_valid()])
)
return dump
def dump_dict(self):
if self._opt_field_name != None:
self.__field_offsets__[self._opt_field_name] = (
self._full_size - STRUCT_SIZEOF_TYPES["I"]
)
self.__keys_ext__.append([self._opt_field_name])
try:
ret = super(UnwindInfo, self).dump_dict()
finally:
if self._opt_field_name != None:
self.__keys_ext__.pop()
return ret
def __setattr__(self, name, val):
if name == "Flags":
set_flags(self, val, unwind_info_flags)
elif "UNW_FLAG_" in name and hasattr(self, name):
if val:
self.__dict__["Flags"] |= UNWIND_INFO_FLAGS[name]
else:
self.__dict__["Flags"] ^= UNWIND_INFO_FLAGS[name]
self.__dict__[name] = val
def sizeof(self):
return self._full_size
def __pack__(self):
data = bytearray(self._full_size)
data[0 : super(UnwindInfo, self).sizeof()] = super(UnwindInfo, self).__pack__()
cur_offset = super(UnwindInfo, self).sizeof()
for uc in self.UnwindCodes:
if cur_offset + uc.struct.sizeof() > self._full_size:
break
data[cur_offset : cur_offset + uc.struct.sizeof()] = uc.struct.__pack__()
cur_offset += uc.struct.sizeof()
if self._opt_field_name != None:
data[
self._full_size - STRUCT_SIZEOF_TYPES["I"] : self._full_size
] = struct.pack("<I", getattr(self, self._opt_field_name))
return data
def get_chained_function_entry(self):
return self._chained_entry
def set_chained_function_entry(self, entry):
if self._chained_entry != None:
raise PEFormatError("Chained function entry cannot be changed")
self._chained_entry = entry
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|
35,826 | pefile | __init__ | null | def __init__(self, file_offset=0):
super(UnwindInfo, self).__init__(
(
"UNWIND_INFO",
(
"B:3,Version",
"B:5,Flags",
"B,SizeOfProlog",
"B,CountOfCodes",
"B:4,FrameRegister",
"B:4,FrameOffset",
),
),
file_offset=file_offset,
)
self._full_size = super(UnwindInfo, self).sizeof()
self._opt_field_name = None
self._code_info = StructureWithBitfields(
("UNWIND_CODE", ("B,CodeOffset", "B:4,UnwindOp", "B:4,OpInfo")),
file_offset=0,
)
self._chained_entry = None
self._finished_unpacking = False
| (self, file_offset=0) | [
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|
35,827 | pefile | __pack__ | null | def __pack__(self):
data = bytearray(self._full_size)
data[0 : super(UnwindInfo, self).sizeof()] = super(UnwindInfo, self).__pack__()
cur_offset = super(UnwindInfo, self).sizeof()
for uc in self.UnwindCodes:
if cur_offset + uc.struct.sizeof() > self._full_size:
break
data[cur_offset : cur_offset + uc.struct.sizeof()] = uc.struct.__pack__()
cur_offset += uc.struct.sizeof()
if self._opt_field_name != None:
data[
self._full_size - STRUCT_SIZEOF_TYPES["I"] : self._full_size
] = struct.pack("<I", getattr(self, self._opt_field_name))
return data
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|
35,829 | pefile | __setattr__ | null | def __setattr__(self, name, val):
if name == "Flags":
set_flags(self, val, unwind_info_flags)
elif "UNW_FLAG_" in name and hasattr(self, name):
if val:
self.__dict__["Flags"] |= UNWIND_INFO_FLAGS[name]
else:
self.__dict__["Flags"] ^= UNWIND_INFO_FLAGS[name]
self.__dict__[name] = val
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|
35,835 | pefile | dump | null | def dump(self, indentation=0):
# Because __keys_ext__ are shared among all the instances with the same
# format string, we have to add and sunsequently remove the optional field
# each time.
# It saves space (as compared to keeping a copy self.__keys_ext__ per
# UnwindInfo instance), but makes our dump() implementation thread-unsafe.
if self._opt_field_name != None:
self.__field_offsets__[self._opt_field_name] = (
self._full_size - STRUCT_SIZEOF_TYPES["I"]
)
self.__keys_ext__.append([self._opt_field_name])
try:
dump = super(UnwindInfo, self).dump(indentation)
finally:
if self._opt_field_name != None:
self.__keys_ext__.pop()
dump.append(
"Flags: "
+ ", ".join([s[0] for s in unwind_info_flags if getattr(self, s[0])])
)
dump.append(
"Unwind codes: "
+ "; ".join([str(c) for c in self.UnwindCodes if c.is_valid()])
)
return dump
| (self, indentation=0) | [
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|
35,836 | pefile | dump_dict | null | def dump_dict(self):
if self._opt_field_name != None:
self.__field_offsets__[self._opt_field_name] = (
self._full_size - STRUCT_SIZEOF_TYPES["I"]
)
self.__keys_ext__.append([self._opt_field_name])
try:
ret = super(UnwindInfo, self).dump_dict()
finally:
if self._opt_field_name != None:
self.__keys_ext__.pop()
return ret
| (self) | [
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|
35,837 | pefile | get_chained_function_entry | null | def get_chained_function_entry(self):
return self._chained_entry
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|
35,841 | pefile | set_chained_function_entry | null | def set_chained_function_entry(self, entry):
if self._chained_entry != None:
raise PEFormatError("Chained function entry cannot be changed")
self._chained_entry = entry
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|
35,843 | pefile | sizeof | null | def sizeof(self):
return self._full_size
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]
|
35,844 | pefile | unpack_in_stages | Unpacks the UNWIND_INFO "in two calls", with the first call establishing
a full size of the structure and the second, performing the actual unpacking.
| def unpack_in_stages(self, data):
"""Unpacks the UNWIND_INFO "in two calls", with the first call establishing
a full size of the structure and the second, performing the actual unpacking.
"""
if self._finished_unpacking:
return None
super(UnwindInfo, self).__unpack__(data)
codes_cnt_max = (self.CountOfCodes + 1) & ~1
hdlr_offset = (
super(UnwindInfo, self).sizeof() + codes_cnt_max * self._code_info.sizeof()
)
self._full_size = hdlr_offset + (
0 if self.Flags == 0 else STRUCT_SIZEOF_TYPES["I"]
)
if len(data) < self._full_size:
return None
if self.Version != 1 and self.Version != 2:
return "Unsupported version of UNWIND_INFO at " + hex(self.__file_offset__)
self.UnwindCodes = []
ro = super(UnwindInfo, self).sizeof()
codes_left = self.CountOfCodes
while codes_left > 0:
self._code_info.__unpack__(data[ro : ro + self._code_info.sizeof()])
ucode = PrologEpilogOpsFactory.create(self._code_info)
if ucode is None:
return "Unknown UNWIND_CODE at " + hex(self.__file_offset__ + ro)
len_in_codes = ucode.length_in_code_structures(self._code_info, self)
opc_size = self._code_info.sizeof() * len_in_codes
ucode.initialize(
self._code_info,
data[ro : ro + opc_size],
self,
self.__file_offset__ + ro,
)
ro += opc_size
codes_left -= len_in_codes
self.UnwindCodes.append(ucode)
if self.UNW_FLAG_EHANDLER or self.UNW_FLAG_UHANDLER:
self._opt_field_name = "ExceptionHandler"
if self.UNW_FLAG_CHAININFO:
self._opt_field_name = "FunctionEntry"
if self._opt_field_name != None:
setattr(
self,
self._opt_field_name,
struct.unpack(
"<I", data[hdlr_offset : hdlr_offset + STRUCT_SIZEOF_TYPES["I"]]
)[0],
)
self._finished_unpacking = True
return None
| (self, data) | [
0.013556879945099354,
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0.00712271174415946,
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|
35,845 | pefile | b | null | def b(x):
if isinstance(x, bytes):
return x
elif isinstance(x, bytearray):
return bytes(x)
else:
return codecs.encode(x, "cp1252")
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|
35,849 | pefile | count_zeroes | null | def count_zeroes(data):
return data.count(0)
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]
|
35,851 | pefile | get_sublang_name_for_lang | null | def get_sublang_name_for_lang(lang_value, sublang_value):
lang_name = LANG.get(lang_value, "*unknown*")
for sublang_name in SUBLANG.get(sublang_value, []):
# if the main language is a substring of sublang's name, then
# return that
if lang_name in sublang_name:
return sublang_name
# otherwise return the first sublang name
return SUBLANG.get(sublang_value, ["*unknown*"])[0]
| (lang_value, sublang_value) | [
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|
35,852 | pefile | is_valid_dos_filename | null | def is_valid_dos_filename(s):
if s is None or not isinstance(s, (str, bytes, bytearray)):
return False
# Allow path separators as import names can contain directories.
allowed = allowed_filename + b"\\/"
return all(c in allowed for c in set(s))
| (s) | [
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|
35,854 | pefile | lru_cache | null | def lru_cache(maxsize=128, typed=False, copy=False):
if not copy:
return functools.lru_cache(maxsize, typed)
def decorator(f):
cached_func = functools.lru_cache(maxsize, typed)(f)
@functools.wraps(f)
def wrapper(*args, **kwargs):
# return copymod.deepcopy(cached_func(*args, **kwargs))
return copymod.copy(cached_func(*args, **kwargs))
return wrapper
return decorator
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]
|
35,855 | pefile | main | null | def main():
import sys
usage = """\
pefile.py <filename>
pefile.py exports <filename>"""
if not sys.argv[1:]:
print(usage)
elif sys.argv[1] == "exports":
if not sys.argv[2:]:
sys.exit("error: <filename> required")
pe = PE(sys.argv[2])
for exp in pe.DIRECTORY_ENTRY_EXPORT.symbols:
print(
hex(pe.OPTIONAL_HEADER.ImageBase + exp.address), exp.name, exp.ordinal
)
else:
print(PE(sys.argv[1]).dump_info())
| () | [
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|
35,860 | pefile | parse_strings | null | def parse_strings(data, counter, l):
i = 0
error_count = 0
while i < len(data):
data_slice = data[i : i + 2]
if len(data_slice) < 2:
break
len_ = struct.unpack("<h", data_slice)[0]
i += 2
if len_ != 0 and 0 <= len_ * 2 <= len(data):
try:
l[counter] = b(data[i : i + len_ * 2]).decode("utf-16le")
except UnicodeDecodeError:
error_count += 1
pass
if error_count >= 3:
break
i += len_ * 2
counter += 1
| (data, counter, l) | [
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]
|
35,861 | pefile | power_of_two | null | def power_of_two(val):
return val != 0 and (val & (val - 1)) == 0
| (val) | [
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|
35,862 | pefile | retrieve_flags | Read the flags from a dictionary and return them in a usable form.
Will return a list of (flag, value) for all flags in "flag_dict"
matching the filter "flag_filter".
| def retrieve_flags(flag_dict, flag_filter):
"""Read the flags from a dictionary and return them in a usable form.
Will return a list of (flag, value) for all flags in "flag_dict"
matching the filter "flag_filter".
"""
return [
(flag, flag_dict[flag])
for flag in flag_dict.keys()
if isinstance(flag, (str, bytes)) and flag.startswith(flag_filter)
]
| (flag_dict, flag_filter) | [
0.042346637696027756,
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]
|
35,863 | pefile | set_flags | Will process the flags and set attributes in the object accordingly.
The object "obj" will gain attributes named after the flags provided in
"flags" and valued True/False, matching the results of applying each
flag value from "flags" to flag_field.
| def set_flags(obj, flag_field, flags):
"""Will process the flags and set attributes in the object accordingly.
The object "obj" will gain attributes named after the flags provided in
"flags" and valued True/False, matching the results of applying each
flag value from "flags" to flag_field.
"""
for flag, value in flags:
if value & flag_field:
obj.__dict__[flag] = True
else:
obj.__dict__[flag] = False
| (obj, flag_field, flags) | [
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]
|
35,868 | pefile | two_way_dict | null | def two_way_dict(pairs):
return dict([(e[1], e[0]) for e in pairs] + pairs)
| (pairs) | [
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|
35,870 | text_generation.client | AsyncClient | Asynchronous Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import AsyncClient
>>> client = AsyncClient("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
| class AsyncClient:
"""Asynchronous Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import AsyncClient
>>> client = AsyncClient("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout)
async def chat(
self,
messages: List[Message],
repetition_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[List[float]] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[float] = None,
stream: bool = False,
seed: Optional[int] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
tools: Optional[List[Tool]] = None,
tool_choice: Optional[str] = None,
) -> Union[ChatComplete, AsyncIterator[ChatCompletionChunk]]:
"""
Given a list of messages, generate a response asynchronously
Args:
messages (`List[Message]`):
List of messages
repetition_penalty (`float`):
The parameter for frequency penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 0.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
logit_bias (`List[float]`):
Adjust the likelihood of specified tokens
logprobs (`bool`):
Include log probabilities in the response
top_logprobs (`int`):
Include the `n` most likely tokens at each step
max_tokens (`int`):
Maximum number of generated tokens
n (`int`):
Generate `n` completions
presence_penalty (`float`):
The parameter for presence penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
stream (`bool`):
Stream the response
seed (`int`):
Random sampling seed
temperature (`float`):
The value used to module the logits distribution.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation
tools (`List[Tool]`):
List of tools to use
tool_choice (`str`):
The tool to use
"""
request = ChatRequest(
model="tgi",
messages=messages,
repetition_penalty=repetition_penalty,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
logprobs=logprobs,
top_logprobs=top_logprobs,
max_tokens=max_tokens,
n=n,
presence_penalty=presence_penalty,
stream=stream,
seed=seed,
temperature=temperature,
top_p=top_p,
tools=tools,
tool_choice=tool_choice,
)
if not stream:
return await self._chat_single_response(request)
else:
return self._chat_stream_response(request)
async def _chat_single_response(self, request):
async with ClientSession(
headers=self.headers, cookies=self.cookies, timeout=self.timeout
) as session:
async with session.post(
f"{self.base_url}/v1/chat/completions", json=request.dict()
) as resp:
payload = await resp.json()
if resp.status != 200:
raise parse_error(resp.status, payload)
return ChatComplete(**payload)
async def _chat_stream_response(self, request):
async with ClientSession(
headers=self.headers, cookies=self.cookies, timeout=self.timeout
) as session:
async with session.post(
f"{self.base_url}/v1/chat/completions", json=request.dict()
) as resp:
async for byte_payload in resp.content:
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
if payload.startswith("data:"):
json_payload = json.loads(payload.lstrip("data:").rstrip("\n"))
try:
response = ChatCompletionChunk(**json_payload)
yield response
except ValidationError:
raise parse_error(resp.status, json_payload)
async def generate(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
best_of: Optional[int] = None,
repetition_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
decoder_input_details: bool = False,
top_n_tokens: Optional[int] = None,
grammar: Optional[Grammar] = None,
) -> Response:
"""
Given a prompt, generate the following text asynchronously
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
| (base_url: str, headers: Optional[Dict[str, str]] = None, cookies: Optional[Dict[str, str]] = None, timeout: int = 10) | [
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|
35,871 | text_generation.client | __init__ |
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
| def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout)
| (self, base_url: str, headers: Optional[Dict[str, str]] = None, cookies: Optional[Dict[str, str]] = None, timeout: int = 10) | [
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|
35,872 | text_generation.client | _chat_single_response | null | def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout)
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|
35,874 | text_generation.client | chat |
Given a list of messages, generate a response asynchronously
Args:
messages (`List[Message]`):
List of messages
repetition_penalty (`float`):
The parameter for frequency penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 0.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
logit_bias (`List[float]`):
Adjust the likelihood of specified tokens
logprobs (`bool`):
Include log probabilities in the response
top_logprobs (`int`):
Include the `n` most likely tokens at each step
max_tokens (`int`):
Maximum number of generated tokens
n (`int`):
Generate `n` completions
presence_penalty (`float`):
The parameter for presence penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
stream (`bool`):
Stream the response
seed (`int`):
Random sampling seed
temperature (`float`):
The value used to module the logits distribution.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation
tools (`List[Tool]`):
List of tools to use
tool_choice (`str`):
The tool to use
| def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout)
| (self, messages: List[text_generation.types.Message], repetition_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, logit_bias: Optional[List[float]] = None, logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, max_tokens: Optional[int] = None, n: Optional[int] = None, presence_penalty: Optional[float] = None, stream: bool = False, seed: Optional[int] = None, temperature: Optional[float] = None, top_p: Optional[float] = None, tools: Optional[List[text_generation.types.Tool]] = None, tool_choice: Optional[str] = None) -> Union[text_generation.types.ChatComplete, AsyncIterator[text_generation.types.ChatCompletionChunk]] | [
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|
35,875 | text_generation.client | generate |
Given a prompt, generate the following text asynchronously
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
decoder_input_details (`bool`):
Return the decoder input token logprobs and ids
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
grammar (`Grammar`):
Whether to use a grammar for the generation and the grammar to use. Grammars will constrain the generation
of the text to match a regular expression or JSON schema.
Returns:
Response: generated response
| def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout)
| (self, prompt: str, do_sample: bool = False, max_new_tokens: int = 20, best_of: Optional[int] = None, repetition_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, return_full_text: bool = False, seed: Optional[int] = None, stop_sequences: Optional[List[str]] = None, temperature: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, truncate: Optional[int] = None, typical_p: Optional[float] = None, watermark: bool = False, decoder_input_details: bool = False, top_n_tokens: Optional[int] = None, grammar: Optional[text_generation.types.Grammar] = None) -> text_generation.types.Response | [
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|
35,876 | text_generation.client | generate_stream |
Given a prompt, generate the following stream of tokens asynchronously
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
grammar (`Grammar`):
Whether to use a grammar for the generation and the grammar to use. Grammars will constrain the generation
of the text to match a regular expression or JSON schema.
Returns:
AsyncIterator[StreamResponse]: stream of generated tokens
| def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = ClientTimeout(timeout)
| (self, prompt: str, do_sample: bool = False, max_new_tokens: int = 20, repetition_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, return_full_text: bool = False, seed: Optional[int] = None, stop_sequences: Optional[List[str]] = None, temperature: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, truncate: Optional[int] = None, typical_p: Optional[float] = None, watermark: bool = False, top_n_tokens: Optional[int] = None, grammar: Optional[text_generation.types.Grammar] = None) -> AsyncIterator[text_generation.types.StreamResponse] | [
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|
35,877 | text_generation.client | Client | Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import Client
>>> client = Client("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
| class Client:
"""Client to make calls to a text-generation-inference instance
Example:
```python
>>> from text_generation import Client
>>> client = Client("https://api-inference.huggingface.co/models/bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = timeout
def chat(
self,
messages: List[Message],
repetition_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[List[float]] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[float] = None,
stream: bool = False,
seed: Optional[int] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
tools: Optional[List[Tool]] = None,
tool_choice: Optional[str] = None,
):
"""
Given a list of messages, generate a response asynchronously
Args:
messages (`List[Message]`):
List of messages
repetition_penalty (`float`):
The parameter for repetition penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 0.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
logit_bias (`List[float]`):
Adjust the likelihood of specified tokens
logprobs (`bool`):
Include log probabilities in the response
top_logprobs (`int`):
Include the `n` most likely tokens at each step
max_tokens (`int`):
Maximum number of generated tokens
n (`int`):
Generate `n` completions
presence_penalty (`float`):
The parameter for presence penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
stream (`bool`):
Stream the response
seed (`int`):
Random sampling seed
temperature (`float`):
The value used to module the logits distribution.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation
tools (`List[Tool]`):
List of tools to use
tool_choice (`str`):
The tool to use
"""
request = ChatRequest(
model="tgi",
messages=messages,
repetition_penalty=repetition_penalty,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
logprobs=logprobs,
top_logprobs=top_logprobs,
max_tokens=max_tokens,
n=n,
presence_penalty=presence_penalty,
stream=stream,
seed=seed,
temperature=temperature,
top_p=top_p,
tools=tools,
tool_choice=tool_choice,
)
if not stream:
resp = requests.post(
f"{self.base_url}/v1/chat/completions",
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
return ChatComplete(**payload)
else:
return self._chat_stream_response(request)
def _chat_stream_response(self, request):
resp = requests.post(
f"{self.base_url}/v1/chat/completions",
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
stream=True,
)
# iterate and print stream
for byte_payload in resp.iter_lines():
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
if payload.startswith("data:"):
json_payload = json.loads(payload.lstrip("data:").rstrip("\n"))
try:
response = ChatCompletionChunk(**json_payload)
yield response
except ValidationError:
raise parse_error(resp.status, json_payload)
def generate(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
best_of: Optional[int] = None,
repetition_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
decoder_input_details: bool = False,
top_n_tokens: Optional[int] = None,
grammar: Optional[Grammar] = None,
) -> Response:
"""
Given a prompt, generate the following text
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
| (base_url: str, headers: Optional[Dict[str, str]] = None, cookies: Optional[Dict[str, str]] = None, timeout: int = 10) | [
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|
35,878 | text_generation.client | __init__ |
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
| def __init__(
self,
base_url: str,
headers: Optional[Dict[str, str]] = None,
cookies: Optional[Dict[str, str]] = None,
timeout: int = 10,
):
"""
Args:
base_url (`str`):
text-generation-inference instance base url
headers (`Optional[Dict[str, str]]`):
Additional headers
cookies (`Optional[Dict[str, str]]`):
Cookies to include in the requests
timeout (`int`):
Timeout in seconds
"""
self.base_url = base_url
self.headers = headers
self.cookies = cookies
self.timeout = timeout
| (self, base_url: str, headers: Optional[Dict[str, str]] = None, cookies: Optional[Dict[str, str]] = None, timeout: int = 10) | [
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|
35,879 | text_generation.client | _chat_stream_response | null | def _chat_stream_response(self, request):
resp = requests.post(
f"{self.base_url}/v1/chat/completions",
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
stream=True,
)
# iterate and print stream
for byte_payload in resp.iter_lines():
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
if payload.startswith("data:"):
json_payload = json.loads(payload.lstrip("data:").rstrip("\n"))
try:
response = ChatCompletionChunk(**json_payload)
yield response
except ValidationError:
raise parse_error(resp.status, json_payload)
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|
35,880 | text_generation.client | chat |
Given a list of messages, generate a response asynchronously
Args:
messages (`List[Message]`):
List of messages
repetition_penalty (`float`):
The parameter for repetition penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 0.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
logit_bias (`List[float]`):
Adjust the likelihood of specified tokens
logprobs (`bool`):
Include log probabilities in the response
top_logprobs (`int`):
Include the `n` most likely tokens at each step
max_tokens (`int`):
Maximum number of generated tokens
n (`int`):
Generate `n` completions
presence_penalty (`float`):
The parameter for presence penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
stream (`bool`):
Stream the response
seed (`int`):
Random sampling seed
temperature (`float`):
The value used to module the logits distribution.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation
tools (`List[Tool]`):
List of tools to use
tool_choice (`str`):
The tool to use
| def chat(
self,
messages: List[Message],
repetition_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[List[float]] = None,
logprobs: Optional[bool] = None,
top_logprobs: Optional[int] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[float] = None,
stream: bool = False,
seed: Optional[int] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
tools: Optional[List[Tool]] = None,
tool_choice: Optional[str] = None,
):
"""
Given a list of messages, generate a response asynchronously
Args:
messages (`List[Message]`):
List of messages
repetition_penalty (`float`):
The parameter for repetition penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 0.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
logit_bias (`List[float]`):
Adjust the likelihood of specified tokens
logprobs (`bool`):
Include log probabilities in the response
top_logprobs (`int`):
Include the `n` most likely tokens at each step
max_tokens (`int`):
Maximum number of generated tokens
n (`int`):
Generate `n` completions
presence_penalty (`float`):
The parameter for presence penalty. 0.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
stream (`bool`):
Stream the response
seed (`int`):
Random sampling seed
temperature (`float`):
The value used to module the logits distribution.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation
tools (`List[Tool]`):
List of tools to use
tool_choice (`str`):
The tool to use
"""
request = ChatRequest(
model="tgi",
messages=messages,
repetition_penalty=repetition_penalty,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
logprobs=logprobs,
top_logprobs=top_logprobs,
max_tokens=max_tokens,
n=n,
presence_penalty=presence_penalty,
stream=stream,
seed=seed,
temperature=temperature,
top_p=top_p,
tools=tools,
tool_choice=tool_choice,
)
if not stream:
resp = requests.post(
f"{self.base_url}/v1/chat/completions",
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
return ChatComplete(**payload)
else:
return self._chat_stream_response(request)
| (self, messages: List[text_generation.types.Message], repetition_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, logit_bias: Optional[List[float]] = None, logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, max_tokens: Optional[int] = None, n: Optional[int] = None, presence_penalty: Optional[float] = None, stream: bool = False, seed: Optional[int] = None, temperature: Optional[float] = None, top_p: Optional[float] = None, tools: Optional[List[text_generation.types.Tool]] = None, tool_choice: Optional[str] = None) | [
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|
35,881 | text_generation.client | generate |
Given a prompt, generate the following text
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
decoder_input_details (`bool`):
Return the decoder input token logprobs and ids
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
grammar (`Grammar`):
Whether to use a grammar for the generation and the grammar to use. Grammars will constrain the generation
of the text to match a regular expression or JSON schema.
Returns:
Response: generated response
| def generate(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
best_of: Optional[int] = None,
repetition_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
decoder_input_details: bool = False,
top_n_tokens: Optional[int] = None,
grammar: Optional[Grammar] = None,
) -> Response:
"""
Given a prompt, generate the following text
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
best_of (`int`):
Generate best_of sequences and return the one if the highest token logprobs
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
decoder_input_details (`bool`):
Return the decoder input token logprobs and ids
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
grammar (`Grammar`):
Whether to use a grammar for the generation and the grammar to use. Grammars will constrain the generation
of the text to match a regular expression or JSON schema.
Returns:
Response: generated response
"""
# Validate parameters
parameters = Parameters(
best_of=best_of,
details=True,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
frequency_penalty=frequency_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
decoder_input_details=decoder_input_details,
top_n_tokens=top_n_tokens,
grammar=grammar,
)
request = Request(inputs=prompt, stream=False, parameters=parameters)
resp = requests.post(
self.base_url,
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
)
payload = resp.json()
if resp.status_code != 200:
raise parse_error(resp.status_code, payload)
return Response(**payload[0])
| (self, prompt: str, do_sample: bool = False, max_new_tokens: int = 20, best_of: Optional[int] = None, repetition_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, return_full_text: bool = False, seed: Optional[int] = None, stop_sequences: Optional[List[str]] = None, temperature: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, truncate: Optional[int] = None, typical_p: Optional[float] = None, watermark: bool = False, decoder_input_details: bool = False, top_n_tokens: Optional[int] = None, grammar: Optional[text_generation.types.Grammar] = None) -> text_generation.types.Response | [
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|
35,882 | text_generation.client | generate_stream |
Given a prompt, generate the following stream of tokens
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
grammar (`Grammar`):
Whether to use a grammar for the generation and the grammar to use. Grammars will constrain the generation
of the text to match a regular expression or JSON schema.
Returns:
Iterator[StreamResponse]: stream of generated tokens
| def generate_stream(
self,
prompt: str,
do_sample: bool = False,
max_new_tokens: int = 20,
repetition_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
return_full_text: bool = False,
seed: Optional[int] = None,
stop_sequences: Optional[List[str]] = None,
temperature: Optional[float] = None,
top_k: Optional[int] = None,
top_p: Optional[float] = None,
truncate: Optional[int] = None,
typical_p: Optional[float] = None,
watermark: bool = False,
top_n_tokens: Optional[int] = None,
grammar: Optional[Grammar] = None,
) -> Iterator[StreamResponse]:
"""
Given a prompt, generate the following stream of tokens
Args:
prompt (`str`):
Input text
do_sample (`bool`):
Activate logits sampling
max_new_tokens (`int`):
Maximum number of generated tokens
repetition_penalty (`float`):
The parameter for repetition penalty. 1.0 means no penalty. See [this
paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
frequency_penalty (`float`):
The parameter for frequency penalty. 1.0 means no penalty
Penalize new tokens based on their existing frequency in the text so far,
decreasing the model's likelihood to repeat the same line verbatim.
return_full_text (`bool`):
Whether to prepend the prompt to the generated text
seed (`int`):
Random sampling seed
stop_sequences (`List[str]`):
Stop generating tokens if a member of `stop_sequences` is generated
temperature (`float`):
The value used to module the logits distribution.
top_k (`int`):
The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p (`float`):
If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
higher are kept for generation.
truncate (`int`):
Truncate inputs tokens to the given size
typical_p (`float`):
Typical Decoding mass
See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
watermark (`bool`):
Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
top_n_tokens (`int`):
Return the `n` most likely tokens at each step
grammar (`Grammar`):
Whether to use a grammar for the generation and the grammar to use. Grammars will constrain the generation
of the text to match a regular expression or JSON schema.
Returns:
Iterator[StreamResponse]: stream of generated tokens
"""
# Validate parameters
parameters = Parameters(
best_of=None,
details=True,
decoder_input_details=False,
do_sample=do_sample,
max_new_tokens=max_new_tokens,
repetition_penalty=repetition_penalty,
frequency_penalty=frequency_penalty,
return_full_text=return_full_text,
seed=seed,
stop=stop_sequences if stop_sequences is not None else [],
temperature=temperature,
top_k=top_k,
top_p=top_p,
truncate=truncate,
typical_p=typical_p,
watermark=watermark,
top_n_tokens=top_n_tokens,
grammar=grammar,
)
request = Request(inputs=prompt, stream=True, parameters=parameters)
resp = requests.post(
self.base_url,
json=request.dict(),
headers=self.headers,
cookies=self.cookies,
timeout=self.timeout,
stream=True,
)
if resp.status_code != 200:
raise parse_error(resp.status_code, resp.json())
# Parse ServerSentEvents
for byte_payload in resp.iter_lines():
# Skip line
if byte_payload == b"\n":
continue
payload = byte_payload.decode("utf-8")
# Event data
if payload.startswith("data:"):
# Decode payload
json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
# Parse payload
try:
response = StreamResponse(**json_payload)
except ValidationError:
# If we failed to parse the payload, then it is an error payload
raise parse_error(resp.status_code, json_payload)
yield response
| (self, prompt: str, do_sample: bool = False, max_new_tokens: int = 20, repetition_penalty: Optional[float] = None, frequency_penalty: Optional[float] = None, return_full_text: bool = False, seed: Optional[int] = None, stop_sequences: Optional[List[str]] = None, temperature: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, truncate: Optional[int] = None, typical_p: Optional[float] = None, watermark: bool = False, top_n_tokens: Optional[int] = None, grammar: Optional[text_generation.types.Grammar] = None) -> Iterator[text_generation.types.StreamResponse] | [
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|
35,883 | text_generation.inference_api | InferenceAPIAsyncClient | Aynschronous Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIAsyncClient
>>> client = InferenceAPIAsyncClient("bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
| class InferenceAPIAsyncClient(AsyncClient):
"""Aynschronous Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIAsyncClient
>>> client = InferenceAPIAsyncClient("bigscience/bloomz")
>>> response = await client.generate("Why is the sky blue?")
>>> response.generated_text
' Rayleigh scattering'
>>> result = ""
>>> async for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIAsyncClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
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|
35,884 | text_generation.inference_api | __init__ |
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
| def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIAsyncClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
| (self, repo_id: str, token: Optional[str] = None, timeout: int = 10) | [
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|
35,890 | text_generation.inference_api | InferenceAPIClient | Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIClient
>>> client = InferenceAPIClient("bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
| class InferenceAPIClient(Client):
"""Client to make calls to the HuggingFace Inference API.
Only supports a subset of the available text-generation or text2text-generation models that are served using
text-generation-inference
Example:
```python
>>> from text_generation import InferenceAPIClient
>>> client = InferenceAPIClient("bigscience/bloomz")
>>> client.generate("Why is the sky blue?").generated_text
' Rayleigh scattering'
>>> result = ""
>>> for response in client.generate_stream("Why is the sky blue?"):
>>> if not response.token.special:
>>> result += response.token.text
>>> result
' Rayleigh scattering'
```
"""
def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
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|
35,891 | text_generation.inference_api | __init__ |
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
| def __init__(self, repo_id: str, token: Optional[str] = None, timeout: int = 10):
"""
Init headers and API information
Args:
repo_id (`str`):
Id of repository (e.g. `bigscience/bloom`).
token (`str`, `optional`):
The API token to use as HTTP bearer authorization. This is not
the authentication token. You can find the token in
https://huggingface.co/settings/token. Alternatively, you can
find both your organizations and personal API tokens using
`HfApi().whoami(token)`.
timeout (`int`):
Timeout in seconds
"""
headers = build_hf_headers(
token=token, library_name="text-generation", library_version=__version__
)
# Text Generation Inference client only supports a subset of the available hub models
if not check_model_support(repo_id, headers):
raise NotSupportedError(repo_id)
base_url = f"{INFERENCE_ENDPOINT}/models/{repo_id}"
super(InferenceAPIClient, self).__init__(
base_url, headers=headers, timeout=timeout
)
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|
35,901 | col_spanish.encrypt | create_dictionary |
Recieves two lists and create a dictionary, both of the lists should have
the same length.
Params:
------
key_list : list : list that will work as the keys inside the dictionary
value_list : list : list that contains the values that we want to call with the keys
Usage:
------
>>> from col_spanish import create_dictionary
>>> key_list = ['k','h','l','p']
>>> value_list = ['8','d','g','a']
>>> create_dictionary(key_list, value_list)
| def create_dictionary(key_list:list, value_list:list):
"""
Recieves two lists and create a dictionary, both of the lists should have
the same length.
Params:
------
key_list : list : list that will work as the keys inside the dictionary
value_list : list : list that contains the values that we want to call with the keys
Usage:
------
>>> from col_spanish import create_dictionary
>>> key_list = ['k','h','l','p']
>>> value_list = ['8','d','g','a']
>>> create_dictionary(key_list, value_list)
"""
# check that the two list have the same length and are not empty
if len(key_list) < 1 or key_list is None:
return 'key_list param should be a list with a length bigger than 1'
if len(value_list) < 1 or value_list is None:
return 'value_list param should be a list with a length bigger than 1'
key_dictionary = {}
for idx, character in enumerate(value_list):
dict_append = {f'{key_list[idx]}':f'{character}'}
key_dictionary.update(dict_append)
return key_dictionary
| (key_list: list, value_list: list) | [
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|
35,902 | col_spanish.col_spanish | del_accent |
Function to delete accents from the text
Params:
------
text: str : text to be clean
Usage:
------
>>> from col_spanish import del_acce
>>> text = 'hola, este es un texto! que necesita? remover signos de puntuacion!!'
>>> del_acce(text)
| def del_accent(text):
"""
Function to delete accents from the text
Params:
------
text: str : text to be clean
Usage:
------
>>> from col_spanish import del_acce
>>> text = 'hola, este es un texto! que necesita? remover signos de puntuacion!!'
>>> del_acce(text)
"""
new_text = ""
words = text.split()
for w in words:
wl = w.lower()
# delete accent mark
for accent, new_value in delete_accent_dict.items():
wl = wl.replace(accent, new_value)
# add the words without punctuation marks to the final text
new_text += f"{wl} "
return new_text[:-1]
| (text) | [
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|
35,903 | col_spanish.col_spanish | del_punctuation |
Function to delete all the punctuation marks that are inside a text
Params:
------
text: str : text to be clean
Usage:
------
>>> from col_spanish import del_punctuation
>>> text = 'hola, este es un texto! que necesita? remover signos de puntuacion!!'
>>> del_punctuation(text)
| def del_punctuation(text):
"""
Function to delete all the punctuation marks that are inside a text
Params:
------
text: str : text to be clean
Usage:
------
>>> from col_spanish import del_punctuation
>>> text = 'hola, este es un texto! que necesita? remover signos de puntuacion!!'
>>> del_punctuation(text)
"""
new_text = ""
words = text.split()
for wl in words:
# delete punctuation marks to keep only words
for punctuation, new_value in delete_punctuation_dict.items():
wl = wl.replace(punctuation, new_value)
# add the words without punctuation marks to the final text
new_text += f"{wl} "
return new_text[:-1]
| (text) | [
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|
35,905 | col_spanish.encrypt | generate_pass |
This function create a safe encrypted version of any password that you have.
Params:
------
* option : int : 0. only letters, 1. only numbers and 2. any character
* sentence : str : a sentence that will be the key to lock and unlock the password
* password : str : password to encrypt
* encrypt : int : 0. encrypt, 1. decrypt
Usage:
------
>>> from col_spanish import generate_pass
>>> generate_pass(2, 'sentence', 'password', 0)
| def generate_pass(option:int, sentence:str, password:str, encrypt:int):
"""
This function create a safe encrypted version of any password that you have.
Params:
------
* option : int : 0. only letters, 1. only numbers and 2. any character
* sentence : str : a sentence that will be the key to lock and unlock the password
* password : str : password to encrypt
* encrypt : int : 0. encrypt, 1. decrypt
Usage:
------
>>> from col_spanish import generate_pass
>>> generate_pass(2, 'sentence', 'password', 0)
"""
# checking the options is inside the available options
if int(option) in [0,1,2]:
pass
else:
return "the option is not inside the valide options. 0,1,2"
# checking that there is a sentence to create the password encription
if len(sentence) < 1:
return "you need to write a sentence to be able to encrypt the password"
# checking that there is a password to encrypt
if len(password) < 1:
return "you need to write a password to encrypt"
# checking the encrypt param is inside the valide options
if int(encrypt) in [0,1]:
pass
else:
return "the encrypt value is not inside the valid options. 0,1"
# delete blanck spaces
sentence = sentence.replace(' ','')
password = password.replace(' ','')
# checking the option is according to the params
if int(option):
for character in sentence:
if character not in dict_lists[f'{option}']:
return "the option is not adecuate for the sentence you wrote"
for character in password:
if character not in dict_lists[f'{option}']:
return "the option is not adecuate for the password"
# delete duplicate inside the sentence to create the alphabet for the encryption
set_sentence = set_characters(sentence)
# create the new alphabet
for character in dict_lists[f'{option}']:
if character not in set_sentence:
set_sentence.append(character)
# create the encrypted or decrypted password
if int(encrypt) == 0:
# create a dictionary with the keys to encrypt the sentence/password
key_dictionary = create_dictionary(dict_lists[f'{option}'], set_sentence)
encrypted_password = ''
for character in password:
encrypted_password = f'{encrypted_password}{key_dictionary[character]}'
return encrypted_password
if int(encrypt) == 1:
# create a dictionary with the keys to encrypt the sentence/password
key_dictionary = create_dictionary(set_sentence,dict_lists[f'{option}'])
decrypted_password = ''
for character in password:
decrypted_password = f'{decrypted_password}{key_dictionary[character]}'
return decrypted_password
| (option: int, sentence: str, password: str, encrypt: int) | [
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|
35,906 | col_spanish.encrypt | set_characters |
delete duplicate characters inside the sentence. save it in the same order,
as it appears in the sentence not in an aleatory order as set()
Params:
------
*sentence : string : sentence where the duplicates will be eliminated
Usage:
------
>>> from col_spanish import set_characters
>>> set_characters('sentence')
| def set_characters(sentence):
"""
delete duplicate characters inside the sentence. save it in the same order,
as it appears in the sentence not in an aleatory order as set()
Params:
------
*sentence : string : sentence where the duplicates will be eliminated
Usage:
------
>>> from col_spanish import set_characters
>>> set_characters('sentence')
"""
sentence = sentence.replace(" ","")
sentence_list = []
for character in sentence:
if character not in sentence_list:
sentence_list.append(character)
return sentence_list
| (sentence) | [
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|
35,907 | cmarkgfm.cmark | Options | null | class Options(object):
CMARK_OPT_DEFAULT = _cmark.lib.CMARK_OPT_DEFAULT
CMARK_OPT_SOURCEPOS = _cmark.lib.CMARK_OPT_SOURCEPOS
CMARK_OPT_HARDBREAKS = _cmark.lib.CMARK_OPT_HARDBREAKS
CMARK_OPT_UNSAFE = _cmark.lib.CMARK_OPT_UNSAFE
CMARK_OPT_NOBREAKS = _cmark.lib.CMARK_OPT_NOBREAKS
CMARK_OPT_NORMALIZE = _cmark.lib.CMARK_OPT_NORMALIZE
CMARK_OPT_VALIDATE_UTF8 = _cmark.lib.CMARK_OPT_VALIDATE_UTF8
CMARK_OPT_SMART = _cmark.lib.CMARK_OPT_SMART
CMARK_OPT_GITHUB_PRE_LANG = _cmark.lib.CMARK_OPT_GITHUB_PRE_LANG
CMARK_OPT_LIBERAL_HTML_TAG = _cmark.lib.CMARK_OPT_LIBERAL_HTML_TAG
CMARK_OPT_FOOTNOTES = _cmark.lib.CMARK_OPT_FOOTNOTES
CMARK_OPT_STRIKETHROUGH_DOUBLE_TILDE = (
_cmark.lib.CMARK_OPT_STRIKETHROUGH_DOUBLE_TILDE)
CMARK_OPT_TABLE_PREFER_STYLE_ATTRIBUTES = (
_cmark.lib.CMARK_OPT_TABLE_PREFER_STYLE_ATTRIBUTES)
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|
35,910 | cmarkgfm.cmark | github_flavored_markdown_to_html | Render the given GitHub-flavored Makrdown to HTML.
This is a small wrapper over :func:`markdown_to_html_with_extensions`.
The GitHub extensions and the option CMARK_OPT_GITHUB_PRE_LANG are applied.
Args:
text (str): The Markdown text to render to HTML.
options (int): The cmark options.
Returns:
str: The HTML rendered from Markdown.
| def github_flavored_markdown_to_html(text, options=0):
"""Render the given GitHub-flavored Makrdown to HTML.
This is a small wrapper over :func:`markdown_to_html_with_extensions`.
The GitHub extensions and the option CMARK_OPT_GITHUB_PRE_LANG are applied.
Args:
text (str): The Markdown text to render to HTML.
options (int): The cmark options.
Returns:
str: The HTML rendered from Markdown.
"""
# Force some more options; see
# <https://github.com/theacodes/cmarkgfm/issues/37#issuecomment-852925142>
options = (
options |
Options.CMARK_OPT_GITHUB_PRE_LANG
)
return markdown_to_html_with_extensions(
text, options=options,
extensions=[
'table', 'autolink', 'tagfilter', 'strikethrough', 'tasklist'
])
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|
35,911 | cmarkgfm.cmark | markdown_to_html | Render the given Markdown text to HTML.
This is a direct interface to ``cmark_markdown_to_html``.
Args:
text (str): The Markdown text to render to HTML.
options (int): The cmark options.
Returns:
str: The HTML rendered from Markdown.
| def markdown_to_html(text, options=0):
"""Render the given Markdown text to HTML.
This is a direct interface to ``cmark_markdown_to_html``.
Args:
text (str): The Markdown text to render to HTML.
options (int): The cmark options.
Returns:
str: The HTML rendered from Markdown.
"""
encoded_text = text.encode('utf-8')
raw_result = _cmark.lib.cmark_markdown_to_html(
encoded_text, len(encoded_text), options)
return _cmark.ffi.string(raw_result).decode('utf-8')
| (text, options=0) | [
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-0.06130402535200119,
-0.005468391347676516,
-0.04810008034110069,
-0.02350592240691185,
0.022417685016989708,
0.06348049640655518,
0.04664909839630127,
-0.05089322105050087,
-0.009254549629986286,
0.016423311084508896,
0.05303342267870903,
0.01388409174978733,
0.023415235802531242,
0.04022849723696709,
-0.015053946524858475,
-0.04157065600156784,
-0.04291281849145889,
-0.022617194801568985,
0.025464748963713646
]
|
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