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.gitattributes

@@ -218,3 +218,4 @@ env-llmeval/lib/python3.10/site-packages/sklearn/_loss/_loss.cpython-310-x86_64-
 env-llmeval/lib/python3.10/site-packages/aiohttp/_http_parser.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 env-llmeval/lib/python3.10/site-packages/numpy.libs/libgfortran-040039e1.so.5.0.0 filter=lfs diff=lfs merge=lfs -text
 env-llmeval/lib/python3.10/site-packages/numpy.libs/libopenblas64_p-r0-0cf96a72.3.23.dev.so filter=lfs diff=lfs merge=lfs -text
+env-llmeval/lib/python3.10/site-packages/numexpr/interpreter.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

env-llmeval/lib/python3.10/site-packages/numexpr/interpreter.cpython-310-x86_64-linux-gnu.so

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bcb0c6481fb539a0d205a98b8ad9860f3226a3b80ec3dd81200970415199cab5
+size 1150680

env-llmeval/lib/python3.10/site-packages/requests/__version__.py

@@ -0,0 +1,14 @@
+# .-. .-. .-. . . .-. .-. .-. .-.
+# |(  |-  |.| | | |-  `-.  |  `-.
+# ' ' `-' `-`.`-' `-' `-'  '  `-'
+
+__title__ = "requests"
+__description__ = "Python HTTP for Humans."
+__url__ = "https://requests.readthedocs.io"
+__version__ = "2.31.0"
+__build__ = 0x023100
+__author__ = "Kenneth Reitz"
+__author_email__ = "[email protected]"
+__license__ = "Apache 2.0"
+__copyright__ = "Copyright Kenneth Reitz"
+__cake__ = "\u2728 \U0001f370 \u2728"

env-llmeval/lib/python3.10/site-packages/requests/_internal_utils.py

@@ -0,0 +1,50 @@
+"""
+requests._internal_utils
+~~~~~~~~~~~~~~
+
+Provides utility functions that are consumed internally by Requests
+which depend on extremely few external helpers (such as compat)
+"""
+import re
+
+from .compat import builtin_str
+
+_VALID_HEADER_NAME_RE_BYTE = re.compile(rb"^[^:\s][^:\r\n]*$")
+_VALID_HEADER_NAME_RE_STR = re.compile(r"^[^:\s][^:\r\n]*$")
+_VALID_HEADER_VALUE_RE_BYTE = re.compile(rb"^\S[^\r\n]*$|^$")
+_VALID_HEADER_VALUE_RE_STR = re.compile(r"^\S[^\r\n]*$|^$")
+
+_HEADER_VALIDATORS_STR = (_VALID_HEADER_NAME_RE_STR, _VALID_HEADER_VALUE_RE_STR)
+_HEADER_VALIDATORS_BYTE = (_VALID_HEADER_NAME_RE_BYTE, _VALID_HEADER_VALUE_RE_BYTE)
+HEADER_VALIDATORS = {
+    bytes: _HEADER_VALIDATORS_BYTE,
+    str: _HEADER_VALIDATORS_STR,
+}
+
+
+def to_native_string(string, encoding="ascii"):
+    """Given a string object, regardless of type, returns a representation of
+    that string in the native string type, encoding and decoding where
+    necessary. This assumes ASCII unless told otherwise.
+    """
+    if isinstance(string, builtin_str):
+        out = string
+    else:
+        out = string.decode(encoding)
+
+    return out
+
+
+def unicode_is_ascii(u_string):
+    """Determine if unicode string only contains ASCII characters.
+
+    :param str u_string: unicode string to check. Must be unicode
+        and not Python 2 `str`.
+    :rtype: bool
+    """
+    assert isinstance(u_string, str)
+    try:
+        u_string.encode("ascii")
+        return True
+    except UnicodeEncodeError:
+        return False

env-llmeval/lib/python3.10/site-packages/requests/api.py

@@ -0,0 +1,157 @@
+"""
+requests.api
+~~~~~~~~~~~~
+
+This module implements the Requests API.
+
+:copyright: (c) 2012 by Kenneth Reitz.
+:license: Apache2, see LICENSE for more details.
+"""
+
+from . import sessions
+
+
+def request(method, url, **kwargs):
+    """Constructs and sends a :class:`Request <Request>`.
+
+    :param method: method for the new :class:`Request` object: ``GET``, ``OPTIONS``, ``HEAD``, ``POST``, ``PUT``, ``PATCH``, or ``DELETE``.
+    :param url: URL for the new :class:`Request` object.
+    :param params: (optional) Dictionary, list of tuples or bytes to send
+        in the query string for the :class:`Request`.
+    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+        object to send in the body of the :class:`Request`.
+    :param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
+    :param headers: (optional) Dictionary of HTTP Headers to send with the :class:`Request`.
+    :param cookies: (optional) Dict or CookieJar object to send with the :class:`Request`.
+    :param files: (optional) Dictionary of ``'name': file-like-objects`` (or ``{'name': file-tuple}``) for multipart encoding upload.
+        ``file-tuple`` can be a 2-tuple ``('filename', fileobj)``, 3-tuple ``('filename', fileobj, 'content_type')``
+        or a 4-tuple ``('filename', fileobj, 'content_type', custom_headers)``, where ``'content-type'`` is a string
+        defining the content type of the given file and ``custom_headers`` a dict-like object containing additional headers
+        to add for the file.
+    :param auth: (optional) Auth tuple to enable Basic/Digest/Custom HTTP Auth.
+    :param timeout: (optional) How many seconds to wait for the server to send data
+        before giving up, as a float, or a :ref:`(connect timeout, read
+        timeout) <timeouts>` tuple.
+    :type timeout: float or tuple
+    :param allow_redirects: (optional) Boolean. Enable/disable GET/OPTIONS/POST/PUT/PATCH/DELETE/HEAD redirection. Defaults to ``True``.
+    :type allow_redirects: bool
+    :param proxies: (optional) Dictionary mapping protocol to the URL of the proxy.
+    :param verify: (optional) Either a boolean, in which case it controls whether we verify
+            the server's TLS certificate, or a string, in which case it must be a path
+            to a CA bundle to use. Defaults to ``True``.
+    :param stream: (optional) if ``False``, the response content will be immediately downloaded.
+    :param cert: (optional) if String, path to ssl client cert file (.pem). If Tuple, ('cert', 'key') pair.
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+
+    Usage::
+
+      >>> import requests
+      >>> req = requests.request('GET', 'https://httpbin.org/get')
+      >>> req
+      <Response [200]>
+    """
+
+    # By using the 'with' statement we are sure the session is closed, thus we
+    # avoid leaving sockets open which can trigger a ResourceWarning in some
+    # cases, and look like a memory leak in others.
+    with sessions.Session() as session:
+        return session.request(method=method, url=url, **kwargs)
+
+
+def get(url, params=None, **kwargs):
+    r"""Sends a GET request.
+
+    :param url: URL for the new :class:`Request` object.
+    :param params: (optional) Dictionary, list of tuples or bytes to send
+        in the query string for the :class:`Request`.
+    :param \*\*kwargs: Optional arguments that ``request`` takes.
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+    """
+
+    return request("get", url, params=params, **kwargs)
+
+
+def options(url, **kwargs):
+    r"""Sends an OPTIONS request.
+
+    :param url: URL for the new :class:`Request` object.
+    :param \*\*kwargs: Optional arguments that ``request`` takes.
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+    """
+
+    return request("options", url, **kwargs)
+
+
+def head(url, **kwargs):
+    r"""Sends a HEAD request.
+
+    :param url: URL for the new :class:`Request` object.
+    :param \*\*kwargs: Optional arguments that ``request`` takes. If
+        `allow_redirects` is not provided, it will be set to `False` (as
+        opposed to the default :meth:`request` behavior).
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+    """
+
+    kwargs.setdefault("allow_redirects", False)
+    return request("head", url, **kwargs)
+
+
+def post(url, data=None, json=None, **kwargs):
+    r"""Sends a POST request.
+
+    :param url: URL for the new :class:`Request` object.
+    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+        object to send in the body of the :class:`Request`.
+    :param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
+    :param \*\*kwargs: Optional arguments that ``request`` takes.
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+    """
+
+    return request("post", url, data=data, json=json, **kwargs)
+
+
+def put(url, data=None, **kwargs):
+    r"""Sends a PUT request.
+
+    :param url: URL for the new :class:`Request` object.
+    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+        object to send in the body of the :class:`Request`.
+    :param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
+    :param \*\*kwargs: Optional arguments that ``request`` takes.
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+    """
+
+    return request("put", url, data=data, **kwargs)
+
+
+def patch(url, data=None, **kwargs):
+    r"""Sends a PATCH request.
+
+    :param url: URL for the new :class:`Request` object.
+    :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+        object to send in the body of the :class:`Request`.
+    :param json: (optional) A JSON serializable Python object to send in the body of the :class:`Request`.
+    :param \*\*kwargs: Optional arguments that ``request`` takes.
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+    """
+
+    return request("patch", url, data=data, **kwargs)
+
+
+def delete(url, **kwargs):
+    r"""Sends a DELETE request.
+
+    :param url: URL for the new :class:`Request` object.
+    :param \*\*kwargs: Optional arguments that ``request`` takes.
+    :return: :class:`Response <Response>` object
+    :rtype: requests.Response
+    """
+
+    return request("delete", url, **kwargs)

env-llmeval/lib/python3.10/site-packages/requests/auth.py

@@ -0,0 +1,315 @@
+"""
+requests.auth
+~~~~~~~~~~~~~
+
+This module contains the authentication handlers for Requests.
+"""
+
+import hashlib
+import os
+import re
+import threading
+import time
+import warnings
+from base64 import b64encode
+
+from ._internal_utils import to_native_string
+from .compat import basestring, str, urlparse
+from .cookies import extract_cookies_to_jar
+from .utils import parse_dict_header
+
+CONTENT_TYPE_FORM_URLENCODED = "application/x-www-form-urlencoded"
+CONTENT_TYPE_MULTI_PART = "multipart/form-data"
+
+
+def _basic_auth_str(username, password):
+    """Returns a Basic Auth string."""
+
+    # "I want us to put a big-ol' comment on top of it that
+    # says that this behaviour is dumb but we need to preserve
+    # it because people are relying on it."
+    #    - Lukasa
+    #
+    # These are here solely to maintain backwards compatibility
+    # for things like ints. This will be removed in 3.0.0.
+    if not isinstance(username, basestring):
+        warnings.warn(
+            "Non-string usernames will no longer be supported in Requests "
+            "3.0.0. Please convert the object you've passed in ({!r}) to "
+            "a string or bytes object in the near future to avoid "
+            "problems.".format(username),
+            category=DeprecationWarning,
+        )
+        username = str(username)
+
+    if not isinstance(password, basestring):
+        warnings.warn(
+            "Non-string passwords will no longer be supported in Requests "
+            "3.0.0. Please convert the object you've passed in ({!r}) to "
+            "a string or bytes object in the near future to avoid "
+            "problems.".format(type(password)),
+            category=DeprecationWarning,
+        )
+        password = str(password)
+    # -- End Removal --
+
+    if isinstance(username, str):
+        username = username.encode("latin1")
+
+    if isinstance(password, str):
+        password = password.encode("latin1")
+
+    authstr = "Basic " + to_native_string(
+        b64encode(b":".join((username, password))).strip()
+    )
+
+    return authstr
+
+
+class AuthBase:
+    """Base class that all auth implementations derive from"""
+
+    def __call__(self, r):
+        raise NotImplementedError("Auth hooks must be callable.")
+
+
+class HTTPBasicAuth(AuthBase):
+    """Attaches HTTP Basic Authentication to the given Request object."""
+
+    def __init__(self, username, password):
+        self.username = username
+        self.password = password
+
+    def __eq__(self, other):
+        return all(
+            [
+                self.username == getattr(other, "username", None),
+                self.password == getattr(other, "password", None),
+            ]
+        )
+
+    def __ne__(self, other):
+        return not self == other
+
+    def __call__(self, r):
+        r.headers["Authorization"] = _basic_auth_str(self.username, self.password)
+        return r
+
+
+class HTTPProxyAuth(HTTPBasicAuth):
+    """Attaches HTTP Proxy Authentication to a given Request object."""
+
+    def __call__(self, r):
+        r.headers["Proxy-Authorization"] = _basic_auth_str(self.username, self.password)
+        return r
+
+
+class HTTPDigestAuth(AuthBase):
+    """Attaches HTTP Digest Authentication to the given Request object."""
+
+    def __init__(self, username, password):
+        self.username = username
+        self.password = password
+        # Keep state in per-thread local storage
+        self._thread_local = threading.local()
+
+    def init_per_thread_state(self):
+        # Ensure state is initialized just once per-thread
+        if not hasattr(self._thread_local, "init"):
+            self._thread_local.init = True
+            self._thread_local.last_nonce = ""
+            self._thread_local.nonce_count = 0
+            self._thread_local.chal = {}
+            self._thread_local.pos = None
+            self._thread_local.num_401_calls = None
+
+    def build_digest_header(self, method, url):
+        """
+        :rtype: str
+        """
+
+        realm = self._thread_local.chal["realm"]
+        nonce = self._thread_local.chal["nonce"]
+        qop = self._thread_local.chal.get("qop")
+        algorithm = self._thread_local.chal.get("algorithm")
+        opaque = self._thread_local.chal.get("opaque")
+        hash_utf8 = None
+
+        if algorithm is None:
+            _algorithm = "MD5"
+        else:
+            _algorithm = algorithm.upper()
+        # lambdas assume digest modules are imported at the top level
+        if _algorithm == "MD5" or _algorithm == "MD5-SESS":
+
+            def md5_utf8(x):
+                if isinstance(x, str):
+                    x = x.encode("utf-8")
+                return hashlib.md5(x).hexdigest()
+
+            hash_utf8 = md5_utf8
+        elif _algorithm == "SHA":
+
+            def sha_utf8(x):
+                if isinstance(x, str):
+                    x = x.encode("utf-8")
+                return hashlib.sha1(x).hexdigest()
+
+            hash_utf8 = sha_utf8
+        elif _algorithm == "SHA-256":
+
+            def sha256_utf8(x):
+                if isinstance(x, str):
+                    x = x.encode("utf-8")
+                return hashlib.sha256(x).hexdigest()
+
+            hash_utf8 = sha256_utf8
+        elif _algorithm == "SHA-512":
+
+            def sha512_utf8(x):
+                if isinstance(x, str):
+                    x = x.encode("utf-8")
+                return hashlib.sha512(x).hexdigest()
+
+            hash_utf8 = sha512_utf8
+
+        KD = lambda s, d: hash_utf8(f"{s}:{d}")  # noqa:E731
+
+        if hash_utf8 is None:
+            return None
+
+        # XXX not implemented yet
+        entdig = None
+        p_parsed = urlparse(url)
+        #: path is request-uri defined in RFC 2616 which should not be empty
+        path = p_parsed.path or "/"
+        if p_parsed.query:
+            path += f"?{p_parsed.query}"
+
+        A1 = f"{self.username}:{realm}:{self.password}"
+        A2 = f"{method}:{path}"
+
+        HA1 = hash_utf8(A1)
+        HA2 = hash_utf8(A2)
+
+        if nonce == self._thread_local.last_nonce:
+            self._thread_local.nonce_count += 1
+        else:
+            self._thread_local.nonce_count = 1
+        ncvalue = f"{self._thread_local.nonce_count:08x}"
+        s = str(self._thread_local.nonce_count).encode("utf-8")
+        s += nonce.encode("utf-8")
+        s += time.ctime().encode("utf-8")
+        s += os.urandom(8)
+
+        cnonce = hashlib.sha1(s).hexdigest()[:16]
+        if _algorithm == "MD5-SESS":
+            HA1 = hash_utf8(f"{HA1}:{nonce}:{cnonce}")
+
+        if not qop:
+            respdig = KD(HA1, f"{nonce}:{HA2}")
+        elif qop == "auth" or "auth" in qop.split(","):
+            noncebit = f"{nonce}:{ncvalue}:{cnonce}:auth:{HA2}"
+            respdig = KD(HA1, noncebit)
+        else:
+            # XXX handle auth-int.
+            return None
+
+        self._thread_local.last_nonce = nonce
+
+        # XXX should the partial digests be encoded too?
+        base = (
+            f'username="{self.username}", realm="{realm}", nonce="{nonce}", '
+            f'uri="{path}", response="{respdig}"'
+        )
+        if opaque:
+            base += f', opaque="{opaque}"'
+        if algorithm:
+            base += f', algorithm="{algorithm}"'
+        if entdig:
+            base += f', digest="{entdig}"'
+        if qop:
+            base += f', qop="auth", nc={ncvalue}, cnonce="{cnonce}"'
+
+        return f"Digest {base}"
+
+    def handle_redirect(self, r, **kwargs):
+        """Reset num_401_calls counter on redirects."""
+        if r.is_redirect:
+            self._thread_local.num_401_calls = 1
+
+    def handle_401(self, r, **kwargs):
+        """
+        Takes the given response and tries digest-auth, if needed.
+
+        :rtype: requests.Response
+        """
+
+        # If response is not 4xx, do not auth
+        # See https://github.com/psf/requests/issues/3772
+        if not 400 <= r.status_code < 500:
+            self._thread_local.num_401_calls = 1
+            return r
+
+        if self._thread_local.pos is not None:
+            # Rewind the file position indicator of the body to where
+            # it was to resend the request.
+            r.request.body.seek(self._thread_local.pos)
+        s_auth = r.headers.get("www-authenticate", "")
+
+        if "digest" in s_auth.lower() and self._thread_local.num_401_calls < 2:
+
+            self._thread_local.num_401_calls += 1
+            pat = re.compile(r"digest ", flags=re.IGNORECASE)
+            self._thread_local.chal = parse_dict_header(pat.sub("", s_auth, count=1))
+
+            # Consume content and release the original connection
+            # to allow our new request to reuse the same one.
+            r.content
+            r.close()
+            prep = r.request.copy()
+            extract_cookies_to_jar(prep._cookies, r.request, r.raw)
+            prep.prepare_cookies(prep._cookies)
+
+            prep.headers["Authorization"] = self.build_digest_header(
+                prep.method, prep.url
+            )
+            _r = r.connection.send(prep, **kwargs)
+            _r.history.append(r)
+            _r.request = prep
+
+            return _r
+
+        self._thread_local.num_401_calls = 1
+        return r
+
+    def __call__(self, r):
+        # Initialize per-thread state, if needed
+        self.init_per_thread_state()
+        # If we have a saved nonce, skip the 401
+        if self._thread_local.last_nonce:
+            r.headers["Authorization"] = self.build_digest_header(r.method, r.url)
+        try:
+            self._thread_local.pos = r.body.tell()
+        except AttributeError:
+            # In the case of HTTPDigestAuth being reused and the body of
+            # the previous request was a file-like object, pos has the
+            # file position of the previous body. Ensure it's set to
+            # None.
+            self._thread_local.pos = None
+        r.register_hook("response", self.handle_401)
+        r.register_hook("response", self.handle_redirect)
+        self._thread_local.num_401_calls = 1
+
+        return r
+
+    def __eq__(self, other):
+        return all(
+            [
+                self.username == getattr(other, "username", None),
+                self.password == getattr(other, "password", None),
+            ]
+        )
+
+    def __ne__(self, other):
+        return not self == other

env-llmeval/lib/python3.10/site-packages/requests/certs.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+
+"""
+requests.certs
+~~~~~~~~~~~~~~
+
+This module returns the preferred default CA certificate bundle. There is
+only one — the one from the certifi package.
+
+If you are packaging Requests, e.g., for a Linux distribution or a managed
+environment, you can change the definition of where() to return a separately
+packaged CA bundle.
+"""
+from certifi import where
+
+if __name__ == "__main__":
+    print(where())

env-llmeval/lib/python3.10/site-packages/requests/compat.py

@@ -0,0 +1,79 @@
+"""
+requests.compat
+~~~~~~~~~~~~~~~
+
+This module previously handled import compatibility issues
+between Python 2 and Python 3. It remains for backwards
+compatibility until the next major version.
+"""
+
+try:
+    import chardet
+except ImportError:
+    import charset_normalizer as chardet
+
+import sys
+
+# -------
+# Pythons
+# -------
+
+# Syntax sugar.
+_ver = sys.version_info
+
+#: Python 2.x?
+is_py2 = _ver[0] == 2
+
+#: Python 3.x?
+is_py3 = _ver[0] == 3
+
+# json/simplejson module import resolution
+has_simplejson = False
+try:
+    import simplejson as json
+
+    has_simplejson = True
+except ImportError:
+    import json
+
+if has_simplejson:
+    from simplejson import JSONDecodeError
+else:
+    from json import JSONDecodeError
+
+# Keep OrderedDict for backwards compatibility.
+from collections import OrderedDict
+from collections.abc import Callable, Mapping, MutableMapping
+from http import cookiejar as cookielib
+from http.cookies import Morsel
+from io import StringIO
+
+# --------------
+# Legacy Imports
+# --------------
+from urllib.parse import (
+    quote,
+    quote_plus,
+    unquote,
+    unquote_plus,
+    urldefrag,
+    urlencode,
+    urljoin,
+    urlparse,
+    urlsplit,
+    urlunparse,
+)
+from urllib.request import (
+    getproxies,
+    getproxies_environment,
+    parse_http_list,
+    proxy_bypass,
+    proxy_bypass_environment,
+)
+
+builtin_str = str
+str = str
+bytes = bytes
+basestring = (str, bytes)
+numeric_types = (int, float)
+integer_types = (int,)

env-llmeval/lib/python3.10/site-packages/requests/exceptions.py

@@ -0,0 +1,141 @@
+"""
+requests.exceptions
+~~~~~~~~~~~~~~~~~~~
+
+This module contains the set of Requests' exceptions.
+"""
+from urllib3.exceptions import HTTPError as BaseHTTPError
+
+from .compat import JSONDecodeError as CompatJSONDecodeError
+
+
+class RequestException(IOError):
+    """There was an ambiguous exception that occurred while handling your
+    request.
+    """
+
+    def __init__(self, *args, **kwargs):
+        """Initialize RequestException with `request` and `response` objects."""
+        response = kwargs.pop("response", None)
+        self.response = response
+        self.request = kwargs.pop("request", None)
+        if response is not None and not self.request and hasattr(response, "request"):
+            self.request = self.response.request
+        super().__init__(*args, **kwargs)
+
+
+class InvalidJSONError(RequestException):
+    """A JSON error occurred."""
+
+
+class JSONDecodeError(InvalidJSONError, CompatJSONDecodeError):
+    """Couldn't decode the text into json"""
+
+    def __init__(self, *args, **kwargs):
+        """
+        Construct the JSONDecodeError instance first with all
+        args. Then use it's args to construct the IOError so that
+        the json specific args aren't used as IOError specific args
+        and the error message from JSONDecodeError is preserved.
+        """
+        CompatJSONDecodeError.__init__(self, *args)
+        InvalidJSONError.__init__(self, *self.args, **kwargs)
+
+
+class HTTPError(RequestException):
+    """An HTTP error occurred."""
+
+
+class ConnectionError(RequestException):
+    """A Connection error occurred."""
+
+
+class ProxyError(ConnectionError):
+    """A proxy error occurred."""
+
+
+class SSLError(ConnectionError):
+    """An SSL error occurred."""
+
+
+class Timeout(RequestException):
+    """The request timed out.
+
+    Catching this error will catch both
+    :exc:`~requests.exceptions.ConnectTimeout` and
+    :exc:`~requests.exceptions.ReadTimeout` errors.
+    """
+
+
+class ConnectTimeout(ConnectionError, Timeout):
+    """The request timed out while trying to connect to the remote server.
+
+    Requests that produced this error are safe to retry.
+    """
+
+
+class ReadTimeout(Timeout):
+    """The server did not send any data in the allotted amount of time."""
+
+
+class URLRequired(RequestException):
+    """A valid URL is required to make a request."""
+
+
+class TooManyRedirects(RequestException):
+    """Too many redirects."""
+
+
+class MissingSchema(RequestException, ValueError):
+    """The URL scheme (e.g. http or https) is missing."""
+
+
+class InvalidSchema(RequestException, ValueError):
+    """The URL scheme provided is either invalid or unsupported."""
+
+
+class InvalidURL(RequestException, ValueError):
+    """The URL provided was somehow invalid."""
+
+
+class InvalidHeader(RequestException, ValueError):
+    """The header value provided was somehow invalid."""
+
+
+class InvalidProxyURL(InvalidURL):
+    """The proxy URL provided is invalid."""
+
+
+class ChunkedEncodingError(RequestException):
+    """The server declared chunked encoding but sent an invalid chunk."""
+
+
+class ContentDecodingError(RequestException, BaseHTTPError):
+    """Failed to decode response content."""
+
+
+class StreamConsumedError(RequestException, TypeError):
+    """The content for this response was already consumed."""
+
+
+class RetryError(RequestException):
+    """Custom retries logic failed"""
+
+
+class UnrewindableBodyError(RequestException):
+    """Requests encountered an error when trying to rewind a body."""
+
+
+# Warnings
+
+
+class RequestsWarning(Warning):
+    """Base warning for Requests."""
+
+
+class FileModeWarning(RequestsWarning, DeprecationWarning):
+    """A file was opened in text mode, but Requests determined its binary length."""
+
+
+class RequestsDependencyWarning(RequestsWarning):
+    """An imported dependency doesn't match the expected version range."""

env-llmeval/lib/python3.10/site-packages/requests/help.py

@@ -0,0 +1,134 @@
+"""Module containing bug report helper(s)."""
+
+import json
+import platform
+import ssl
+import sys
+
+import idna
+import urllib3
+
+from . import __version__ as requests_version
+
+try:
+    import charset_normalizer
+except ImportError:
+    charset_normalizer = None
+
+try:
+    import chardet
+except ImportError:
+    chardet = None
+
+try:
+    from urllib3.contrib import pyopenssl
+except ImportError:
+    pyopenssl = None
+    OpenSSL = None
+    cryptography = None
+else:
+    import cryptography
+    import OpenSSL
+
+
+def _implementation():
+    """Return a dict with the Python implementation and version.
+
+    Provide both the name and the version of the Python implementation
+    currently running. For example, on CPython 3.10.3 it will return
+    {'name': 'CPython', 'version': '3.10.3'}.
+
+    This function works best on CPython and PyPy: in particular, it probably
+    doesn't work for Jython or IronPython. Future investigation should be done
+    to work out the correct shape of the code for those platforms.
+    """
+    implementation = platform.python_implementation()
+
+    if implementation == "CPython":
+        implementation_version = platform.python_version()
+    elif implementation == "PyPy":
+        implementation_version = "{}.{}.{}".format(
+            sys.pypy_version_info.major,
+            sys.pypy_version_info.minor,
+            sys.pypy_version_info.micro,
+        )
+        if sys.pypy_version_info.releaselevel != "final":
+            implementation_version = "".join(
+                [implementation_version, sys.pypy_version_info.releaselevel]
+            )
+    elif implementation == "Jython":
+        implementation_version = platform.python_version()  # Complete Guess
+    elif implementation == "IronPython":
+        implementation_version = platform.python_version()  # Complete Guess
+    else:
+        implementation_version = "Unknown"
+
+    return {"name": implementation, "version": implementation_version}
+
+
+def info():
+    """Generate information for a bug report."""
+    try:
+        platform_info = {
+            "system": platform.system(),
+            "release": platform.release(),
+        }
+    except OSError:
+        platform_info = {
+            "system": "Unknown",
+            "release": "Unknown",
+        }
+
+    implementation_info = _implementation()
+    urllib3_info = {"version": urllib3.__version__}
+    charset_normalizer_info = {"version": None}
+    chardet_info = {"version": None}
+    if charset_normalizer:
+        charset_normalizer_info = {"version": charset_normalizer.__version__}
+    if chardet:
+        chardet_info = {"version": chardet.__version__}
+
+    pyopenssl_info = {
+        "version": None,
+        "openssl_version": "",
+    }
+    if OpenSSL:
+        pyopenssl_info = {
+            "version": OpenSSL.__version__,
+            "openssl_version": f"{OpenSSL.SSL.OPENSSL_VERSION_NUMBER:x}",
+        }
+    cryptography_info = {
+        "version": getattr(cryptography, "__version__", ""),
+    }
+    idna_info = {
+        "version": getattr(idna, "__version__", ""),
+    }
+
+    system_ssl = ssl.OPENSSL_VERSION_NUMBER
+    system_ssl_info = {"version": f"{system_ssl:x}" if system_ssl is not None else ""}
+
+    return {
+        "platform": platform_info,
+        "implementation": implementation_info,
+        "system_ssl": system_ssl_info,
+        "using_pyopenssl": pyopenssl is not None,
+        "using_charset_normalizer": chardet is None,
+        "pyOpenSSL": pyopenssl_info,
+        "urllib3": urllib3_info,
+        "chardet": chardet_info,
+        "charset_normalizer": charset_normalizer_info,
+        "cryptography": cryptography_info,
+        "idna": idna_info,
+        "requests": {
+            "version": requests_version,
+        },
+    }
+
+
+def main():
+    """Pretty-print the bug information as JSON."""
+    print(json.dumps(info(), sort_keys=True, indent=2))
+
+
+if __name__ == "__main__":
+    main()

env-llmeval/lib/python3.10/site-packages/requests/hooks.py

@@ -0,0 +1,33 @@
+"""
+requests.hooks
+~~~~~~~~~~~~~~
+
+This module provides the capabilities for the Requests hooks system.
+
+Available hooks:
+
+``response``:
+    The response generated from a Request.
+"""
+HOOKS = ["response"]
+
+
+def default_hooks():
+    return {event: [] for event in HOOKS}
+
+
+# TODO: response is the only one
+
+
+def dispatch_hook(key, hooks, hook_data, **kwargs):
+    """Dispatches a hook dictionary on a given piece of data."""
+    hooks = hooks or {}
+    hooks = hooks.get(key)
+    if hooks:
+        if hasattr(hooks, "__call__"):
+            hooks = [hooks]
+        for hook in hooks:
+            _hook_data = hook(hook_data, **kwargs)
+            if _hook_data is not None:
+                hook_data = _hook_data
+    return hook_data

env-llmeval/lib/python3.10/site-packages/requests/packages.py

@@ -0,0 +1,28 @@
+import sys
+
+try:
+    import chardet
+except ImportError:
+    import warnings
+
+    import charset_normalizer as chardet
+
+    warnings.filterwarnings("ignore", "Trying to detect", module="charset_normalizer")
+
+# This code exists for backwards compatibility reasons.
+# I don't like it either. Just look the other way. :)
+
+for package in ("urllib3", "idna"):
+    locals()[package] = __import__(package)
+    # This traversal is apparently necessary such that the identities are
+    # preserved (requests.packages.urllib3.* is urllib3.*)
+    for mod in list(sys.modules):
+        if mod == package or mod.startswith(f"{package}."):
+            sys.modules[f"requests.packages.{mod}"] = sys.modules[mod]
+
+target = chardet.__name__
+for mod in list(sys.modules):
+    if mod == target or mod.startswith(f"{target}."):
+        target = target.replace(target, "chardet")
+        sys.modules[f"requests.packages.{target}"] = sys.modules[mod]
+# Kinda cool, though, right?

env-llmeval/lib/python3.10/site-packages/requests/sessions.py

@@ -0,0 +1,833 @@
+"""
+requests.sessions
+~~~~~~~~~~~~~~~~~
+
+This module provides a Session object to manage and persist settings across
+requests (cookies, auth, proxies).
+"""
+import os
+import sys
+import time
+from collections import OrderedDict
+from datetime import timedelta
+
+from ._internal_utils import to_native_string
+from .adapters import HTTPAdapter
+from .auth import _basic_auth_str
+from .compat import Mapping, cookielib, urljoin, urlparse
+from .cookies import (
+    RequestsCookieJar,
+    cookiejar_from_dict,
+    extract_cookies_to_jar,
+    merge_cookies,
+)
+from .exceptions import (
+    ChunkedEncodingError,
+    ContentDecodingError,
+    InvalidSchema,
+    TooManyRedirects,
+)
+from .hooks import default_hooks, dispatch_hook
+
+# formerly defined here, reexposed here for backward compatibility
+from .models import (  # noqa: F401
+    DEFAULT_REDIRECT_LIMIT,
+    REDIRECT_STATI,
+    PreparedRequest,
+    Request,
+)
+from .status_codes import codes
+from .structures import CaseInsensitiveDict
+from .utils import (  # noqa: F401
+    DEFAULT_PORTS,
+    default_headers,
+    get_auth_from_url,
+    get_environ_proxies,
+    get_netrc_auth,
+    requote_uri,
+    resolve_proxies,
+    rewind_body,
+    should_bypass_proxies,
+    to_key_val_list,
+)
+
+# Preferred clock, based on which one is more accurate on a given system.
+if sys.platform == "win32":
+    preferred_clock = time.perf_counter
+else:
+    preferred_clock = time.time
+
+
+def merge_setting(request_setting, session_setting, dict_class=OrderedDict):
+    """Determines appropriate setting for a given request, taking into account
+    the explicit setting on that request, and the setting in the session. If a
+    setting is a dictionary, they will be merged together using `dict_class`
+    """
+
+    if session_setting is None:
+        return request_setting
+
+    if request_setting is None:
+        return session_setting
+
+    # Bypass if not a dictionary (e.g. verify)
+    if not (
+        isinstance(session_setting, Mapping) and isinstance(request_setting, Mapping)
+    ):
+        return request_setting
+
+    merged_setting = dict_class(to_key_val_list(session_setting))
+    merged_setting.update(to_key_val_list(request_setting))
+
+    # Remove keys that are set to None. Extract keys first to avoid altering
+    # the dictionary during iteration.
+    none_keys = [k for (k, v) in merged_setting.items() if v is None]
+    for key in none_keys:
+        del merged_setting[key]
+
+    return merged_setting
+
+
+def merge_hooks(request_hooks, session_hooks, dict_class=OrderedDict):
+    """Properly merges both requests and session hooks.
+
+    This is necessary because when request_hooks == {'response': []}, the
+    merge breaks Session hooks entirely.
+    """
+    if session_hooks is None or session_hooks.get("response") == []:
+        return request_hooks
+
+    if request_hooks is None or request_hooks.get("response") == []:
+        return session_hooks
+
+    return merge_setting(request_hooks, session_hooks, dict_class)
+
+
+class SessionRedirectMixin:
+    def get_redirect_target(self, resp):
+        """Receives a Response. Returns a redirect URI or ``None``"""
+        # Due to the nature of how requests processes redirects this method will
+        # be called at least once upon the original response and at least twice
+        # on each subsequent redirect response (if any).
+        # If a custom mixin is used to handle this logic, it may be advantageous
+        # to cache the redirect location onto the response object as a private
+        # attribute.
+        if resp.is_redirect:
+            location = resp.headers["location"]
+            # Currently the underlying http module on py3 decode headers
+            # in latin1, but empirical evidence suggests that latin1 is very
+            # rarely used with non-ASCII characters in HTTP headers.
+            # It is more likely to get UTF8 header rather than latin1.
+            # This causes incorrect handling of UTF8 encoded location headers.
+            # To solve this, we re-encode the location in latin1.
+            location = location.encode("latin1")
+            return to_native_string(location, "utf8")
+        return None
+
+    def should_strip_auth(self, old_url, new_url):
+        """Decide whether Authorization header should be removed when redirecting"""
+        old_parsed = urlparse(old_url)
+        new_parsed = urlparse(new_url)
+        if old_parsed.hostname != new_parsed.hostname:
+            return True
+        # Special case: allow http -> https redirect when using the standard
+        # ports. This isn't specified by RFC 7235, but is kept to avoid
+        # breaking backwards compatibility with older versions of requests
+        # that allowed any redirects on the same host.
+        if (
+            old_parsed.scheme == "http"
+            and old_parsed.port in (80, None)
+            and new_parsed.scheme == "https"
+            and new_parsed.port in (443, None)
+        ):
+            return False
+
+        # Handle default port usage corresponding to scheme.
+        changed_port = old_parsed.port != new_parsed.port
+        changed_scheme = old_parsed.scheme != new_parsed.scheme
+        default_port = (DEFAULT_PORTS.get(old_parsed.scheme, None), None)
+        if (
+            not changed_scheme
+            and old_parsed.port in default_port
+            and new_parsed.port in default_port
+        ):
+            return False
+
+        # Standard case: root URI must match
+        return changed_port or changed_scheme
+
+    def resolve_redirects(
+        self,
+        resp,
+        req,
+        stream=False,
+        timeout=None,
+        verify=True,
+        cert=None,
+        proxies=None,
+        yield_requests=False,
+        **adapter_kwargs,
+    ):
+        """Receives a Response. Returns a generator of Responses or Requests."""
+
+        hist = []  # keep track of history
+
+        url = self.get_redirect_target(resp)
+        previous_fragment = urlparse(req.url).fragment
+        while url:
+            prepared_request = req.copy()
+
+            # Update history and keep track of redirects.
+            # resp.history must ignore the original request in this loop
+            hist.append(resp)
+            resp.history = hist[1:]
+
+            try:
+                resp.content  # Consume socket so it can be released
+            except (ChunkedEncodingError, ContentDecodingError, RuntimeError):
+                resp.raw.read(decode_content=False)
+
+            if len(resp.history) >= self.max_redirects:
+                raise TooManyRedirects(
+                    f"Exceeded {self.max_redirects} redirects.", response=resp
+                )
+
+            # Release the connection back into the pool.
+            resp.close()
+
+            # Handle redirection without scheme (see: RFC 1808 Section 4)
+            if url.startswith("//"):
+                parsed_rurl = urlparse(resp.url)
+                url = ":".join([to_native_string(parsed_rurl.scheme), url])
+
+            # Normalize url case and attach previous fragment if needed (RFC 7231 7.1.2)
+            parsed = urlparse(url)
+            if parsed.fragment == "" and previous_fragment:
+                parsed = parsed._replace(fragment=previous_fragment)
+            elif parsed.fragment:
+                previous_fragment = parsed.fragment
+            url = parsed.geturl()
+
+            # Facilitate relative 'location' headers, as allowed by RFC 7231.
+            # (e.g. '/path/to/resource' instead of 'http://domain.tld/path/to/resource')
+            # Compliant with RFC3986, we percent encode the url.
+            if not parsed.netloc:
+                url = urljoin(resp.url, requote_uri(url))
+            else:
+                url = requote_uri(url)
+
+            prepared_request.url = to_native_string(url)
+
+            self.rebuild_method(prepared_request, resp)
+
+            # https://github.com/psf/requests/issues/1084
+            if resp.status_code not in (
+                codes.temporary_redirect,
+                codes.permanent_redirect,
+            ):
+                # https://github.com/psf/requests/issues/3490
+                purged_headers = ("Content-Length", "Content-Type", "Transfer-Encoding")
+                for header in purged_headers:
+                    prepared_request.headers.pop(header, None)
+                prepared_request.body = None
+
+            headers = prepared_request.headers
+            headers.pop("Cookie", None)
+
+            # Extract any cookies sent on the response to the cookiejar
+            # in the new request. Because we've mutated our copied prepared
+            # request, use the old one that we haven't yet touched.
+            extract_cookies_to_jar(prepared_request._cookies, req, resp.raw)
+            merge_cookies(prepared_request._cookies, self.cookies)
+            prepared_request.prepare_cookies(prepared_request._cookies)
+
+            # Rebuild auth and proxy information.
+            proxies = self.rebuild_proxies(prepared_request, proxies)
+            self.rebuild_auth(prepared_request, resp)
+
+            # A failed tell() sets `_body_position` to `object()`. This non-None
+            # value ensures `rewindable` will be True, allowing us to raise an
+            # UnrewindableBodyError, instead of hanging the connection.
+            rewindable = prepared_request._body_position is not None and (
+                "Content-Length" in headers or "Transfer-Encoding" in headers
+            )
+
+            # Attempt to rewind consumed file-like object.
+            if rewindable:
+                rewind_body(prepared_request)
+
+            # Override the original request.
+            req = prepared_request
+
+            if yield_requests:
+                yield req
+            else:
+
+                resp = self.send(
+                    req,
+                    stream=stream,
+                    timeout=timeout,
+                    verify=verify,
+                    cert=cert,
+                    proxies=proxies,
+                    allow_redirects=False,
+                    **adapter_kwargs,
+                )
+
+                extract_cookies_to_jar(self.cookies, prepared_request, resp.raw)
+
+                # extract redirect url, if any, for the next loop
+                url = self.get_redirect_target(resp)
+                yield resp
+
+    def rebuild_auth(self, prepared_request, response):
+        """When being redirected we may want to strip authentication from the
+        request to avoid leaking credentials. This method intelligently removes
+        and reapplies authentication where possible to avoid credential loss.
+        """
+        headers = prepared_request.headers
+        url = prepared_request.url
+
+        if "Authorization" in headers and self.should_strip_auth(
+            response.request.url, url
+        ):
+            # If we get redirected to a new host, we should strip out any
+            # authentication headers.
+            del headers["Authorization"]
+
+        # .netrc might have more auth for us on our new host.
+        new_auth = get_netrc_auth(url) if self.trust_env else None
+        if new_auth is not None:
+            prepared_request.prepare_auth(new_auth)
+
+    def rebuild_proxies(self, prepared_request, proxies):
+        """This method re-evaluates the proxy configuration by considering the
+        environment variables. If we are redirected to a URL covered by
+        NO_PROXY, we strip the proxy configuration. Otherwise, we set missing
+        proxy keys for this URL (in case they were stripped by a previous
+        redirect).
+
+        This method also replaces the Proxy-Authorization header where
+        necessary.
+
+        :rtype: dict
+        """
+        headers = prepared_request.headers
+        scheme = urlparse(prepared_request.url).scheme
+        new_proxies = resolve_proxies(prepared_request, proxies, self.trust_env)
+
+        if "Proxy-Authorization" in headers:
+            del headers["Proxy-Authorization"]
+
+        try:
+            username, password = get_auth_from_url(new_proxies[scheme])
+        except KeyError:
+            username, password = None, None
+
+        # urllib3 handles proxy authorization for us in the standard adapter.
+        # Avoid appending this to TLS tunneled requests where it may be leaked.
+        if not scheme.startswith('https') and username and password:
+            headers["Proxy-Authorization"] = _basic_auth_str(username, password)
+
+        return new_proxies
+
+    def rebuild_method(self, prepared_request, response):
+        """When being redirected we may want to change the method of the request
+        based on certain specs or browser behavior.
+        """
+        method = prepared_request.method
+
+        # https://tools.ietf.org/html/rfc7231#section-6.4.4
+        if response.status_code == codes.see_other and method != "HEAD":
+            method = "GET"
+
+        # Do what the browsers do, despite standards...
+        # First, turn 302s into GETs.
+        if response.status_code == codes.found and method != "HEAD":
+            method = "GET"
+
+        # Second, if a POST is responded to with a 301, turn it into a GET.
+        # This bizarre behaviour is explained in Issue 1704.
+        if response.status_code == codes.moved and method == "POST":
+            method = "GET"
+
+        prepared_request.method = method
+
+
+class Session(SessionRedirectMixin):
+    """A Requests session.
+
+    Provides cookie persistence, connection-pooling, and configuration.
+
+    Basic Usage::
+
+      >>> import requests
+      >>> s = requests.Session()
+      >>> s.get('https://httpbin.org/get')
+      <Response [200]>
+
+    Or as a context manager::
+
+      >>> with requests.Session() as s:
+      ...     s.get('https://httpbin.org/get')
+      <Response [200]>
+    """
+
+    __attrs__ = [
+        "headers",
+        "cookies",
+        "auth",
+        "proxies",
+        "hooks",
+        "params",
+        "verify",
+        "cert",
+        "adapters",
+        "stream",
+        "trust_env",
+        "max_redirects",
+    ]
+
+    def __init__(self):
+
+        #: A case-insensitive dictionary of headers to be sent on each
+        #: :class:`Request <Request>` sent from this
+        #: :class:`Session <Session>`.
+        self.headers = default_headers()
+
+        #: Default Authentication tuple or object to attach to
+        #: :class:`Request <Request>`.
+        self.auth = None
+
+        #: Dictionary mapping protocol or protocol and host to the URL of the proxy
+        #: (e.g. {'http': 'foo.bar:3128', 'http://host.name': 'foo.bar:4012'}) to
+        #: be used on each :class:`Request <Request>`.
+        self.proxies = {}
+
+        #: Event-handling hooks.
+        self.hooks = default_hooks()
+
+        #: Dictionary of querystring data to attach to each
+        #: :class:`Request <Request>`. The dictionary values may be lists for
+        #: representing multivalued query parameters.
+        self.params = {}
+
+        #: Stream response content default.
+        self.stream = False
+
+        #: SSL Verification default.
+        #: Defaults to `True`, requiring requests to verify the TLS certificate at the
+        #: remote end.
+        #: If verify is set to `False`, requests will accept any TLS certificate
+        #: presented by the server, and will ignore hostname mismatches and/or
+        #: expired certificates, which will make your application vulnerable to
+        #: man-in-the-middle (MitM) attacks.
+        #: Only set this to `False` for testing.
+        self.verify = True
+
+        #: SSL client certificate default, if String, path to ssl client
+        #: cert file (.pem). If Tuple, ('cert', 'key') pair.
+        self.cert = None
+
+        #: Maximum number of redirects allowed. If the request exceeds this
+        #: limit, a :class:`TooManyRedirects` exception is raised.
+        #: This defaults to requests.models.DEFAULT_REDIRECT_LIMIT, which is
+        #: 30.
+        self.max_redirects = DEFAULT_REDIRECT_LIMIT
+
+        #: Trust environment settings for proxy configuration, default
+        #: authentication and similar.
+        self.trust_env = True
+
+        #: A CookieJar containing all currently outstanding cookies set on this
+        #: session. By default it is a
+        #: :class:`RequestsCookieJar <requests.cookies.RequestsCookieJar>`, but
+        #: may be any other ``cookielib.CookieJar`` compatible object.
+        self.cookies = cookiejar_from_dict({})
+
+        # Default connection adapters.
+        self.adapters = OrderedDict()
+        self.mount("https://", HTTPAdapter())
+        self.mount("http://", HTTPAdapter())
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        self.close()
+
+    def prepare_request(self, request):
+        """Constructs a :class:`PreparedRequest <PreparedRequest>` for
+        transmission and returns it. The :class:`PreparedRequest` has settings
+        merged from the :class:`Request <Request>` instance and those of the
+        :class:`Session`.
+
+        :param request: :class:`Request` instance to prepare with this
+            session's settings.
+        :rtype: requests.PreparedRequest
+        """
+        cookies = request.cookies or {}
+
+        # Bootstrap CookieJar.
+        if not isinstance(cookies, cookielib.CookieJar):
+            cookies = cookiejar_from_dict(cookies)
+
+        # Merge with session cookies
+        merged_cookies = merge_cookies(
+            merge_cookies(RequestsCookieJar(), self.cookies), cookies
+        )
+
+        # Set environment's basic authentication if not explicitly set.
+        auth = request.auth
+        if self.trust_env and not auth and not self.auth:
+            auth = get_netrc_auth(request.url)
+
+        p = PreparedRequest()
+        p.prepare(
+            method=request.method.upper(),
+            url=request.url,
+            files=request.files,
+            data=request.data,
+            json=request.json,
+            headers=merge_setting(
+                request.headers, self.headers, dict_class=CaseInsensitiveDict
+            ),
+            params=merge_setting(request.params, self.params),
+            auth=merge_setting(auth, self.auth),
+            cookies=merged_cookies,
+            hooks=merge_hooks(request.hooks, self.hooks),
+        )
+        return p
+
+    def request(
+        self,
+        method,
+        url,
+        params=None,
+        data=None,
+        headers=None,
+        cookies=None,
+        files=None,
+        auth=None,
+        timeout=None,
+        allow_redirects=True,
+        proxies=None,
+        hooks=None,
+        stream=None,
+        verify=None,
+        cert=None,
+        json=None,
+    ):
+        """Constructs a :class:`Request <Request>`, prepares it and sends it.
+        Returns :class:`Response <Response>` object.
+
+        :param method: method for the new :class:`Request` object.
+        :param url: URL for the new :class:`Request` object.
+        :param params: (optional) Dictionary or bytes to be sent in the query
+            string for the :class:`Request`.
+        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+            object to send in the body of the :class:`Request`.
+        :param json: (optional) json to send in the body of the
+            :class:`Request`.
+        :param headers: (optional) Dictionary of HTTP Headers to send with the
+            :class:`Request`.
+        :param cookies: (optional) Dict or CookieJar object to send with the
+            :class:`Request`.
+        :param files: (optional) Dictionary of ``'filename': file-like-objects``
+            for multipart encoding upload.
+        :param auth: (optional) Auth tuple or callable to enable
+            Basic/Digest/Custom HTTP Auth.
+        :param timeout: (optional) How long to wait for the server to send
+            data before giving up, as a float, or a :ref:`(connect timeout,
+            read timeout) <timeouts>` tuple.
+        :type timeout: float or tuple
+        :param allow_redirects: (optional) Set to True by default.
+        :type allow_redirects: bool
+        :param proxies: (optional) Dictionary mapping protocol or protocol and
+            hostname to the URL of the proxy.
+        :param stream: (optional) whether to immediately download the response
+            content. Defaults to ``False``.
+        :param verify: (optional) Either a boolean, in which case it controls whether we verify
+            the server's TLS certificate, or a string, in which case it must be a path
+            to a CA bundle to use. Defaults to ``True``. When set to
+            ``False``, requests will accept any TLS certificate presented by
+            the server, and will ignore hostname mismatches and/or expired
+            certificates, which will make your application vulnerable to
+            man-in-the-middle (MitM) attacks. Setting verify to ``False``
+            may be useful during local development or testing.
+        :param cert: (optional) if String, path to ssl client cert file (.pem).
+            If Tuple, ('cert', 'key') pair.
+        :rtype: requests.Response
+        """
+        # Create the Request.
+        req = Request(
+            method=method.upper(),
+            url=url,
+            headers=headers,
+            files=files,
+            data=data or {},
+            json=json,
+            params=params or {},
+            auth=auth,
+            cookies=cookies,
+            hooks=hooks,
+        )
+        prep = self.prepare_request(req)
+
+        proxies = proxies or {}
+
+        settings = self.merge_environment_settings(
+            prep.url, proxies, stream, verify, cert
+        )
+
+        # Send the request.
+        send_kwargs = {
+            "timeout": timeout,
+            "allow_redirects": allow_redirects,
+        }
+        send_kwargs.update(settings)
+        resp = self.send(prep, **send_kwargs)
+
+        return resp
+
+    def get(self, url, **kwargs):
+        r"""Sends a GET request. Returns :class:`Response` object.
+
+        :param url: URL for the new :class:`Request` object.
+        :param \*\*kwargs: Optional arguments that ``request`` takes.
+        :rtype: requests.Response
+        """
+
+        kwargs.setdefault("allow_redirects", True)
+        return self.request("GET", url, **kwargs)
+
+    def options(self, url, **kwargs):
+        r"""Sends a OPTIONS request. Returns :class:`Response` object.
+
+        :param url: URL for the new :class:`Request` object.
+        :param \*\*kwargs: Optional arguments that ``request`` takes.
+        :rtype: requests.Response
+        """
+
+        kwargs.setdefault("allow_redirects", True)
+        return self.request("OPTIONS", url, **kwargs)
+
+    def head(self, url, **kwargs):
+        r"""Sends a HEAD request. Returns :class:`Response` object.
+
+        :param url: URL for the new :class:`Request` object.
+        :param \*\*kwargs: Optional arguments that ``request`` takes.
+        :rtype: requests.Response
+        """
+
+        kwargs.setdefault("allow_redirects", False)
+        return self.request("HEAD", url, **kwargs)
+
+    def post(self, url, data=None, json=None, **kwargs):
+        r"""Sends a POST request. Returns :class:`Response` object.
+
+        :param url: URL for the new :class:`Request` object.
+        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+            object to send in the body of the :class:`Request`.
+        :param json: (optional) json to send in the body of the :class:`Request`.
+        :param \*\*kwargs: Optional arguments that ``request`` takes.
+        :rtype: requests.Response
+        """
+
+        return self.request("POST", url, data=data, json=json, **kwargs)
+
+    def put(self, url, data=None, **kwargs):
+        r"""Sends a PUT request. Returns :class:`Response` object.
+
+        :param url: URL for the new :class:`Request` object.
+        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+            object to send in the body of the :class:`Request`.
+        :param \*\*kwargs: Optional arguments that ``request`` takes.
+        :rtype: requests.Response
+        """
+
+        return self.request("PUT", url, data=data, **kwargs)
+
+    def patch(self, url, data=None, **kwargs):
+        r"""Sends a PATCH request. Returns :class:`Response` object.
+
+        :param url: URL for the new :class:`Request` object.
+        :param data: (optional) Dictionary, list of tuples, bytes, or file-like
+            object to send in the body of the :class:`Request`.
+        :param \*\*kwargs: Optional arguments that ``request`` takes.
+        :rtype: requests.Response
+        """
+
+        return self.request("PATCH", url, data=data, **kwargs)
+
+    def delete(self, url, **kwargs):
+        r"""Sends a DELETE request. Returns :class:`Response` object.
+
+        :param url: URL for the new :class:`Request` object.
+        :param \*\*kwargs: Optional arguments that ``request`` takes.
+        :rtype: requests.Response
+        """
+
+        return self.request("DELETE", url, **kwargs)
+
+    def send(self, request, **kwargs):
+        """Send a given PreparedRequest.
+
+        :rtype: requests.Response
+        """
+        # Set defaults that the hooks can utilize to ensure they always have
+        # the correct parameters to reproduce the previous request.
+        kwargs.setdefault("stream", self.stream)
+        kwargs.setdefault("verify", self.verify)
+        kwargs.setdefault("cert", self.cert)
+        if "proxies" not in kwargs:
+            kwargs["proxies"] = resolve_proxies(request, self.proxies, self.trust_env)
+
+        # It's possible that users might accidentally send a Request object.
+        # Guard against that specific failure case.
+        if isinstance(request, Request):
+            raise ValueError("You can only send PreparedRequests.")
+
+        # Set up variables needed for resolve_redirects and dispatching of hooks
+        allow_redirects = kwargs.pop("allow_redirects", True)
+        stream = kwargs.get("stream")
+        hooks = request.hooks
+
+        # Get the appropriate adapter to use
+        adapter = self.get_adapter(url=request.url)
+
+        # Start time (approximately) of the request
+        start = preferred_clock()
+
+        # Send the request
+        r = adapter.send(request, **kwargs)
+
+        # Total elapsed time of the request (approximately)
+        elapsed = preferred_clock() - start
+        r.elapsed = timedelta(seconds=elapsed)
+
+        # Response manipulation hooks
+        r = dispatch_hook("response", hooks, r, **kwargs)
+
+        # Persist cookies
+        if r.history:
+
+            # If the hooks create history then we want those cookies too
+            for resp in r.history:
+                extract_cookies_to_jar(self.cookies, resp.request, resp.raw)
+
+        extract_cookies_to_jar(self.cookies, request, r.raw)
+
+        # Resolve redirects if allowed.
+        if allow_redirects:
+            # Redirect resolving generator.
+            gen = self.resolve_redirects(r, request, **kwargs)
+            history = [resp for resp in gen]
+        else:
+            history = []
+
+        # Shuffle things around if there's history.
+        if history:
+            # Insert the first (original) request at the start
+            history.insert(0, r)
+            # Get the last request made
+            r = history.pop()
+            r.history = history
+
+        # If redirects aren't being followed, store the response on the Request for Response.next().
+        if not allow_redirects:
+            try:
+                r._next = next(
+                    self.resolve_redirects(r, request, yield_requests=True, **kwargs)
+                )
+            except StopIteration:
+                pass
+
+        if not stream:
+            r.content
+
+        return r
+
+    def merge_environment_settings(self, url, proxies, stream, verify, cert):
+        """
+        Check the environment and merge it with some settings.
+
+        :rtype: dict
+        """
+        # Gather clues from the surrounding environment.
+        if self.trust_env:
+            # Set environment's proxies.
+            no_proxy = proxies.get("no_proxy") if proxies is not None else None
+            env_proxies = get_environ_proxies(url, no_proxy=no_proxy)
+            for (k, v) in env_proxies.items():
+                proxies.setdefault(k, v)
+
+            # Look for requests environment configuration
+            # and be compatible with cURL.
+            if verify is True or verify is None:
+                verify = (
+                    os.environ.get("REQUESTS_CA_BUNDLE")
+                    or os.environ.get("CURL_CA_BUNDLE")
+                    or verify
+                )
+
+        # Merge all the kwargs.
+        proxies = merge_setting(proxies, self.proxies)
+        stream = merge_setting(stream, self.stream)
+        verify = merge_setting(verify, self.verify)
+        cert = merge_setting(cert, self.cert)
+
+        return {"proxies": proxies, "stream": stream, "verify": verify, "cert": cert}
+
+    def get_adapter(self, url):
+        """
+        Returns the appropriate connection adapter for the given URL.
+
+        :rtype: requests.adapters.BaseAdapter
+        """
+        for (prefix, adapter) in self.adapters.items():
+
+            if url.lower().startswith(prefix.lower()):
+                return adapter
+
+        # Nothing matches :-/
+        raise InvalidSchema(f"No connection adapters were found for {url!r}")
+
+    def close(self):
+        """Closes all adapters and as such the session"""
+        for v in self.adapters.values():
+            v.close()
+
+    def mount(self, prefix, adapter):
+        """Registers a connection adapter to a prefix.
+
+        Adapters are sorted in descending order by prefix length.
+        """
+        self.adapters[prefix] = adapter
+        keys_to_move = [k for k in self.adapters if len(k) < len(prefix)]
+
+        for key in keys_to_move:
+            self.adapters[key] = self.adapters.pop(key)
+
+    def __getstate__(self):
+        state = {attr: getattr(self, attr, None) for attr in self.__attrs__}
+        return state
+
+    def __setstate__(self, state):
+        for attr, value in state.items():
+            setattr(self, attr, value)
+
+
+def session():
+    """
+    Returns a :class:`Session` for context-management.
+
+    .. deprecated:: 1.0.0
+
+        This method has been deprecated since version 1.0.0 and is only kept for
+        backwards compatibility. New code should use :class:`~requests.sessions.Session`
+        to create a session. This may be removed at a future date.
+
+    :rtype: Session
+    """
+    return Session()

env-llmeval/lib/python3.10/site-packages/requests/status_codes.py

@@ -0,0 +1,128 @@
+r"""
+The ``codes`` object defines a mapping from common names for HTTP statuses
+to their numerical codes, accessible either as attributes or as dictionary
+items.
+
+Example::
+
+    >>> import requests
+    >>> requests.codes['temporary_redirect']
+    307
+    >>> requests.codes.teapot
+    418
+    >>> requests.codes['\o/']
+    200
+
+Some codes have multiple names, and both upper- and lower-case versions of
+the names are allowed. For example, ``codes.ok``, ``codes.OK``, and
+``codes.okay`` all correspond to the HTTP status code 200.
+"""
+
+from .structures import LookupDict
+
+_codes = {
+    # Informational.
+    100: ("continue",),
+    101: ("switching_protocols",),
+    102: ("processing",),
+    103: ("checkpoint",),
+    122: ("uri_too_long", "request_uri_too_long"),
+    200: ("ok", "okay", "all_ok", "all_okay", "all_good", "\\o/", "✓"),
+    201: ("created",),
+    202: ("accepted",),
+    203: ("non_authoritative_info", "non_authoritative_information"),
+    204: ("no_content",),
+    205: ("reset_content", "reset"),
+    206: ("partial_content", "partial"),
+    207: ("multi_status", "multiple_status", "multi_stati", "multiple_stati"),
+    208: ("already_reported",),
+    226: ("im_used",),
+    # Redirection.
+    300: ("multiple_choices",),
+    301: ("moved_permanently", "moved", "\\o-"),
+    302: ("found",),
+    303: ("see_other", "other"),
+    304: ("not_modified",),
+    305: ("use_proxy",),
+    306: ("switch_proxy",),
+    307: ("temporary_redirect", "temporary_moved", "temporary"),
+    308: (
+        "permanent_redirect",
+        "resume_incomplete",
+        "resume",
+    ),  # "resume" and "resume_incomplete" to be removed in 3.0
+    # Client Error.
+    400: ("bad_request", "bad"),
+    401: ("unauthorized",),
+    402: ("payment_required", "payment"),
+    403: ("forbidden",),
+    404: ("not_found", "-o-"),
+    405: ("method_not_allowed", "not_allowed"),
+    406: ("not_acceptable",),
+    407: ("proxy_authentication_required", "proxy_auth", "proxy_authentication"),
+    408: ("request_timeout", "timeout"),
+    409: ("conflict",),
+    410: ("gone",),
+    411: ("length_required",),
+    412: ("precondition_failed", "precondition"),
+    413: ("request_entity_too_large",),
+    414: ("request_uri_too_large",),
+    415: ("unsupported_media_type", "unsupported_media", "media_type"),
+    416: (
+        "requested_range_not_satisfiable",
+        "requested_range",
+        "range_not_satisfiable",
+    ),
+    417: ("expectation_failed",),
+    418: ("im_a_teapot", "teapot", "i_am_a_teapot"),
+    421: ("misdirected_request",),
+    422: ("unprocessable_entity", "unprocessable"),
+    423: ("locked",),
+    424: ("failed_dependency", "dependency"),
+    425: ("unordered_collection", "unordered"),
+    426: ("upgrade_required", "upgrade"),
+    428: ("precondition_required", "precondition"),
+    429: ("too_many_requests", "too_many"),
+    431: ("header_fields_too_large", "fields_too_large"),
+    444: ("no_response", "none"),
+    449: ("retry_with", "retry"),
+    450: ("blocked_by_windows_parental_controls", "parental_controls"),
+    451: ("unavailable_for_legal_reasons", "legal_reasons"),
+    499: ("client_closed_request",),
+    # Server Error.
+    500: ("internal_server_error", "server_error", "/o\\", "✗"),
+    501: ("not_implemented",),
+    502: ("bad_gateway",),
+    503: ("service_unavailable", "unavailable"),
+    504: ("gateway_timeout",),
+    505: ("http_version_not_supported", "http_version"),
+    506: ("variant_also_negotiates",),
+    507: ("insufficient_storage",),
+    509: ("bandwidth_limit_exceeded", "bandwidth"),
+    510: ("not_extended",),
+    511: ("network_authentication_required", "network_auth", "network_authentication"),
+}
+
+codes = LookupDict(name="status_codes")
+
+
+def _init():
+    for code, titles in _codes.items():
+        for title in titles:
+            setattr(codes, title, code)
+            if not title.startswith(("\\", "/")):
+                setattr(codes, title.upper(), code)
+
+    def doc(code):
+        names = ", ".join(f"``{n}``" for n in _codes[code])
+        return "* %d: %s" % (code, names)
+
+    global __doc__
+    __doc__ = (
+        __doc__ + "\n" + "\n".join(doc(code) for code in sorted(_codes))
+        if __doc__ is not None
+        else None
+    )
+
+
+_init()

env-llmeval/lib/python3.10/site-packages/requests/utils.py

@@ -0,0 +1,1094 @@
+"""
+requests.utils
+~~~~~~~~~~~~~~
+
+This module provides utility functions that are used within Requests
+that are also useful for external consumption.
+"""
+
+import codecs
+import contextlib
+import io
+import os
+import re
+import socket
+import struct
+import sys
+import tempfile
+import warnings
+import zipfile
+from collections import OrderedDict
+
+from urllib3.util import make_headers, parse_url
+
+from . import certs
+from .__version__ import __version__
+
+# to_native_string is unused here, but imported here for backwards compatibility
+from ._internal_utils import (  # noqa: F401
+    _HEADER_VALIDATORS_BYTE,
+    _HEADER_VALIDATORS_STR,
+    HEADER_VALIDATORS,
+    to_native_string,
+)
+from .compat import (
+    Mapping,
+    basestring,
+    bytes,
+    getproxies,
+    getproxies_environment,
+    integer_types,
+)
+from .compat import parse_http_list as _parse_list_header
+from .compat import (
+    proxy_bypass,
+    proxy_bypass_environment,
+    quote,
+    str,
+    unquote,
+    urlparse,
+    urlunparse,
+)
+from .cookies import cookiejar_from_dict
+from .exceptions import (
+    FileModeWarning,
+    InvalidHeader,
+    InvalidURL,
+    UnrewindableBodyError,
+)
+from .structures import CaseInsensitiveDict
+
+NETRC_FILES = (".netrc", "_netrc")
+
+DEFAULT_CA_BUNDLE_PATH = certs.where()
+
+DEFAULT_PORTS = {"http": 80, "https": 443}
+
+# Ensure that ', ' is used to preserve previous delimiter behavior.
+DEFAULT_ACCEPT_ENCODING = ", ".join(
+    re.split(r",\s*", make_headers(accept_encoding=True)["accept-encoding"])
+)
+
+
+if sys.platform == "win32":
+    # provide a proxy_bypass version on Windows without DNS lookups
+
+    def proxy_bypass_registry(host):
+        try:
+            import winreg
+        except ImportError:
+            return False
+
+        try:
+            internetSettings = winreg.OpenKey(
+                winreg.HKEY_CURRENT_USER,
+                r"Software\Microsoft\Windows\CurrentVersion\Internet Settings",
+            )
+            # ProxyEnable could be REG_SZ or REG_DWORD, normalizing it
+            proxyEnable = int(winreg.QueryValueEx(internetSettings, "ProxyEnable")[0])
+            # ProxyOverride is almost always a string
+            proxyOverride = winreg.QueryValueEx(internetSettings, "ProxyOverride")[0]
+        except (OSError, ValueError):
+            return False
+        if not proxyEnable or not proxyOverride:
+            return False
+
+        # make a check value list from the registry entry: replace the
+        # '<local>' string by the localhost entry and the corresponding
+        # canonical entry.
+        proxyOverride = proxyOverride.split(";")
+        # now check if we match one of the registry values.
+        for test in proxyOverride:
+            if test == "<local>":
+                if "." not in host:
+                    return True
+            test = test.replace(".", r"\.")  # mask dots
+            test = test.replace("*", r".*")  # change glob sequence
+            test = test.replace("?", r".")  # change glob char
+            if re.match(test, host, re.I):
+                return True
+        return False
+
+    def proxy_bypass(host):  # noqa
+        """Return True, if the host should be bypassed.
+
+        Checks proxy settings gathered from the environment, if specified,
+        or the registry.
+        """
+        if getproxies_environment():
+            return proxy_bypass_environment(host)
+        else:
+            return proxy_bypass_registry(host)
+
+
+def dict_to_sequence(d):
+    """Returns an internal sequence dictionary update."""
+
+    if hasattr(d, "items"):
+        d = d.items()
+
+    return d
+
+
+def super_len(o):
+    total_length = None
+    current_position = 0
+
+    if hasattr(o, "__len__"):
+        total_length = len(o)
+
+    elif hasattr(o, "len"):
+        total_length = o.len
+
+    elif hasattr(o, "fileno"):
+        try:
+            fileno = o.fileno()
+        except (io.UnsupportedOperation, AttributeError):
+            # AttributeError is a surprising exception, seeing as how we've just checked
+            # that `hasattr(o, 'fileno')`.  It happens for objects obtained via
+            # `Tarfile.extractfile()`, per issue 5229.
+            pass
+        else:
+            total_length = os.fstat(fileno).st_size
+
+            # Having used fstat to determine the file length, we need to
+            # confirm that this file was opened up in binary mode.
+            if "b" not in o.mode:
+                warnings.warn(
+                    (
+                        "Requests has determined the content-length for this "
+                        "request using the binary size of the file: however, the "
+                        "file has been opened in text mode (i.e. without the 'b' "
+                        "flag in the mode). This may lead to an incorrect "
+                        "content-length. In Requests 3.0, support will be removed "
+                        "for files in text mode."
+                    ),
+                    FileModeWarning,
+                )
+
+    if hasattr(o, "tell"):
+        try:
+            current_position = o.tell()
+        except OSError:
+            # This can happen in some weird situations, such as when the file
+            # is actually a special file descriptor like stdin. In this
+            # instance, we don't know what the length is, so set it to zero and
+            # let requests chunk it instead.
+            if total_length is not None:
+                current_position = total_length
+        else:
+            if hasattr(o, "seek") and total_length is None:
+                # StringIO and BytesIO have seek but no usable fileno
+                try:
+                    # seek to end of file
+                    o.seek(0, 2)
+                    total_length = o.tell()
+
+                    # seek back to current position to support
+                    # partially read file-like objects
+                    o.seek(current_position or 0)
+                except OSError:
+                    total_length = 0
+
+    if total_length is None:
+        total_length = 0
+
+    return max(0, total_length - current_position)
+
+
+def get_netrc_auth(url, raise_errors=False):
+    """Returns the Requests tuple auth for a given url from netrc."""
+
+    netrc_file = os.environ.get("NETRC")
+    if netrc_file is not None:
+        netrc_locations = (netrc_file,)
+    else:
+        netrc_locations = (f"~/{f}" for f in NETRC_FILES)
+
+    try:
+        from netrc import NetrcParseError, netrc
+
+        netrc_path = None
+
+        for f in netrc_locations:
+            try:
+                loc = os.path.expanduser(f)
+            except KeyError:
+                # os.path.expanduser can fail when $HOME is undefined and
+                # getpwuid fails. See https://bugs.python.org/issue20164 &
+                # https://github.com/psf/requests/issues/1846
+                return
+
+            if os.path.exists(loc):
+                netrc_path = loc
+                break
+
+        # Abort early if there isn't one.
+        if netrc_path is None:
+            return
+
+        ri = urlparse(url)
+
+        # Strip port numbers from netloc. This weird `if...encode`` dance is
+        # used for Python 3.2, which doesn't support unicode literals.
+        splitstr = b":"
+        if isinstance(url, str):
+            splitstr = splitstr.decode("ascii")
+        host = ri.netloc.split(splitstr)[0]
+
+        try:
+            _netrc = netrc(netrc_path).authenticators(host)
+            if _netrc:
+                # Return with login / password
+                login_i = 0 if _netrc[0] else 1
+                return (_netrc[login_i], _netrc[2])
+        except (NetrcParseError, OSError):
+            # If there was a parsing error or a permissions issue reading the file,
+            # we'll just skip netrc auth unless explicitly asked to raise errors.
+            if raise_errors:
+                raise
+
+    # App Engine hackiness.
+    except (ImportError, AttributeError):
+        pass
+
+
+def guess_filename(obj):
+    """Tries to guess the filename of the given object."""
+    name = getattr(obj, "name", None)
+    if name and isinstance(name, basestring) and name[0] != "<" and name[-1] != ">":
+        return os.path.basename(name)
+
+
+def extract_zipped_paths(path):
+    """Replace nonexistent paths that look like they refer to a member of a zip
+    archive with the location of an extracted copy of the target, or else
+    just return the provided path unchanged.
+    """
+    if os.path.exists(path):
+        # this is already a valid path, no need to do anything further
+        return path
+
+    # find the first valid part of the provided path and treat that as a zip archive
+    # assume the rest of the path is the name of a member in the archive
+    archive, member = os.path.split(path)
+    while archive and not os.path.exists(archive):
+        archive, prefix = os.path.split(archive)
+        if not prefix:
+            # If we don't check for an empty prefix after the split (in other words, archive remains unchanged after the split),
+            # we _can_ end up in an infinite loop on a rare corner case affecting a small number of users
+            break
+        member = "/".join([prefix, member])
+
+    if not zipfile.is_zipfile(archive):
+        return path
+
+    zip_file = zipfile.ZipFile(archive)
+    if member not in zip_file.namelist():
+        return path
+
+    # we have a valid zip archive and a valid member of that archive
+    tmp = tempfile.gettempdir()
+    extracted_path = os.path.join(tmp, member.split("/")[-1])
+    if not os.path.exists(extracted_path):
+        # use read + write to avoid the creating nested folders, we only want the file, avoids mkdir racing condition
+        with atomic_open(extracted_path) as file_handler:
+            file_handler.write(zip_file.read(member))
+    return extracted_path
+
+
+@contextlib.contextmanager
+def atomic_open(filename):
+    """Write a file to the disk in an atomic fashion"""
+    tmp_descriptor, tmp_name = tempfile.mkstemp(dir=os.path.dirname(filename))
+    try:
+        with os.fdopen(tmp_descriptor, "wb") as tmp_handler:
+            yield tmp_handler
+        os.replace(tmp_name, filename)
+    except BaseException:
+        os.remove(tmp_name)
+        raise
+
+
+def from_key_val_list(value):
+    """Take an object and test to see if it can be represented as a
+    dictionary. Unless it can not be represented as such, return an
+    OrderedDict, e.g.,
+
+    ::
+
+        >>> from_key_val_list([('key', 'val')])
+        OrderedDict([('key', 'val')])
+        >>> from_key_val_list('string')
+        Traceback (most recent call last):
+        ...
+        ValueError: cannot encode objects that are not 2-tuples
+        >>> from_key_val_list({'key': 'val'})
+        OrderedDict([('key', 'val')])
+
+    :rtype: OrderedDict
+    """
+    if value is None:
+        return None
+
+    if isinstance(value, (str, bytes, bool, int)):
+        raise ValueError("cannot encode objects that are not 2-tuples")
+
+    return OrderedDict(value)
+
+
+def to_key_val_list(value):
+    """Take an object and test to see if it can be represented as a
+    dictionary. If it can be, return a list of tuples, e.g.,
+
+    ::
+
+        >>> to_key_val_list([('key', 'val')])
+        [('key', 'val')]
+        >>> to_key_val_list({'key': 'val'})
+        [('key', 'val')]
+        >>> to_key_val_list('string')
+        Traceback (most recent call last):
+        ...
+        ValueError: cannot encode objects that are not 2-tuples
+
+    :rtype: list
+    """
+    if value is None:
+        return None
+
+    if isinstance(value, (str, bytes, bool, int)):
+        raise ValueError("cannot encode objects that are not 2-tuples")
+
+    if isinstance(value, Mapping):
+        value = value.items()
+
+    return list(value)
+
+
+# From mitsuhiko/werkzeug (used with permission).
+def parse_list_header(value):
+    """Parse lists as described by RFC 2068 Section 2.
+
+    In particular, parse comma-separated lists where the elements of
+    the list may include quoted-strings.  A quoted-string could
+    contain a comma.  A non-quoted string could have quotes in the
+    middle.  Quotes are removed automatically after parsing.
+
+    It basically works like :func:`parse_set_header` just that items
+    may appear multiple times and case sensitivity is preserved.
+
+    The return value is a standard :class:`list`:
+
+    >>> parse_list_header('token, "quoted value"')
+    ['token', 'quoted value']
+
+    To create a header from the :class:`list` again, use the
+    :func:`dump_header` function.
+
+    :param value: a string with a list header.
+    :return: :class:`list`
+    :rtype: list
+    """
+    result = []
+    for item in _parse_list_header(value):
+        if item[:1] == item[-1:] == '"':
+            item = unquote_header_value(item[1:-1])
+        result.append(item)
+    return result
+
+
+# From mitsuhiko/werkzeug (used with permission).
+def parse_dict_header(value):
+    """Parse lists of key, value pairs as described by RFC 2068 Section 2 and
+    convert them into a python dict:
+
+    >>> d = parse_dict_header('foo="is a fish", bar="as well"')
+    >>> type(d) is dict
+    True
+    >>> sorted(d.items())
+    [('bar', 'as well'), ('foo', 'is a fish')]
+
+    If there is no value for a key it will be `None`:
+
+    >>> parse_dict_header('key_without_value')
+    {'key_without_value': None}
+
+    To create a header from the :class:`dict` again, use the
+    :func:`dump_header` function.
+
+    :param value: a string with a dict header.
+    :return: :class:`dict`
+    :rtype: dict
+    """
+    result = {}
+    for item in _parse_list_header(value):
+        if "=" not in item:
+            result[item] = None
+            continue
+        name, value = item.split("=", 1)
+        if value[:1] == value[-1:] == '"':
+            value = unquote_header_value(value[1:-1])
+        result[name] = value
+    return result
+
+
+# From mitsuhiko/werkzeug (used with permission).
+def unquote_header_value(value, is_filename=False):
+    r"""Unquotes a header value.  (Reversal of :func:`quote_header_value`).
+    This does not use the real unquoting but what browsers are actually
+    using for quoting.
+
+    :param value: the header value to unquote.
+    :rtype: str
+    """
+    if value and value[0] == value[-1] == '"':
+        # this is not the real unquoting, but fixing this so that the
+        # RFC is met will result in bugs with internet explorer and
+        # probably some other browsers as well.  IE for example is
+        # uploading files with "C:\foo\bar.txt" as filename
+        value = value[1:-1]
+
+        # if this is a filename and the starting characters look like
+        # a UNC path, then just return the value without quotes.  Using the
+        # replace sequence below on a UNC path has the effect of turning
+        # the leading double slash into a single slash and then
+        # _fix_ie_filename() doesn't work correctly.  See #458.
+        if not is_filename or value[:2] != "\\\\":
+            return value.replace("\\\\", "\\").replace('\\"', '"')
+    return value
+
+
+def dict_from_cookiejar(cj):
+    """Returns a key/value dictionary from a CookieJar.
+
+    :param cj: CookieJar object to extract cookies from.
+    :rtype: dict
+    """
+
+    cookie_dict = {}
+
+    for cookie in cj:
+        cookie_dict[cookie.name] = cookie.value
+
+    return cookie_dict
+
+
+def add_dict_to_cookiejar(cj, cookie_dict):
+    """Returns a CookieJar from a key/value dictionary.
+
+    :param cj: CookieJar to insert cookies into.
+    :param cookie_dict: Dict of key/values to insert into CookieJar.
+    :rtype: CookieJar
+    """
+
+    return cookiejar_from_dict(cookie_dict, cj)
+
+
+def get_encodings_from_content(content):
+    """Returns encodings from given content string.
+
+    :param content: bytestring to extract encodings from.
+    """
+    warnings.warn(
+        (
+            "In requests 3.0, get_encodings_from_content will be removed. For "
+            "more information, please see the discussion on issue #2266. (This"
+            " warning should only appear once.)"
+        ),
+        DeprecationWarning,
+    )
+
+    charset_re = re.compile(r'<meta.*?charset=["\']*(.+?)["\'>]', flags=re.I)
+    pragma_re = re.compile(r'<meta.*?content=["\']*;?charset=(.+?)["\'>]', flags=re.I)
+    xml_re = re.compile(r'^<\?xml.*?encoding=["\']*(.+?)["\'>]')
+
+    return (
+        charset_re.findall(content)
+        + pragma_re.findall(content)
+        + xml_re.findall(content)
+    )
+
+
+def _parse_content_type_header(header):
+    """Returns content type and parameters from given header
+
+    :param header: string
+    :return: tuple containing content type and dictionary of
+         parameters
+    """
+
+    tokens = header.split(";")
+    content_type, params = tokens[0].strip(), tokens[1:]
+    params_dict = {}
+    items_to_strip = "\"' "
+
+    for param in params:
+        param = param.strip()
+        if param:
+            key, value = param, True
+            index_of_equals = param.find("=")
+            if index_of_equals != -1:
+                key = param[:index_of_equals].strip(items_to_strip)
+                value = param[index_of_equals + 1 :].strip(items_to_strip)
+            params_dict[key.lower()] = value
+    return content_type, params_dict
+
+
+def get_encoding_from_headers(headers):
+    """Returns encodings from given HTTP Header Dict.
+
+    :param headers: dictionary to extract encoding from.
+    :rtype: str
+    """
+
+    content_type = headers.get("content-type")
+
+    if not content_type:
+        return None
+
+    content_type, params = _parse_content_type_header(content_type)
+
+    if "charset" in params:
+        return params["charset"].strip("'\"")
+
+    if "text" in content_type:
+        return "ISO-8859-1"
+
+    if "application/json" in content_type:
+        # Assume UTF-8 based on RFC 4627: https://www.ietf.org/rfc/rfc4627.txt since the charset was unset
+        return "utf-8"
+
+
+def stream_decode_response_unicode(iterator, r):
+    """Stream decodes an iterator."""
+
+    if r.encoding is None:
+        yield from iterator
+        return
+
+    decoder = codecs.getincrementaldecoder(r.encoding)(errors="replace")
+    for chunk in iterator:
+        rv = decoder.decode(chunk)
+        if rv:
+            yield rv
+    rv = decoder.decode(b"", final=True)
+    if rv:
+        yield rv
+
+
+def iter_slices(string, slice_length):
+    """Iterate over slices of a string."""
+    pos = 0
+    if slice_length is None or slice_length <= 0:
+        slice_length = len(string)
+    while pos < len(string):
+        yield string[pos : pos + slice_length]
+        pos += slice_length
+
+
+def get_unicode_from_response(r):
+    """Returns the requested content back in unicode.
+
+    :param r: Response object to get unicode content from.
+
+    Tried:
+
+    1. charset from content-type
+    2. fall back and replace all unicode characters
+
+    :rtype: str
+    """
+    warnings.warn(
+        (
+            "In requests 3.0, get_unicode_from_response will be removed. For "
+            "more information, please see the discussion on issue #2266. (This"
+            " warning should only appear once.)"
+        ),
+        DeprecationWarning,
+    )
+
+    tried_encodings = []
+
+    # Try charset from content-type
+    encoding = get_encoding_from_headers(r.headers)
+
+    if encoding:
+        try:
+            return str(r.content, encoding)
+        except UnicodeError:
+            tried_encodings.append(encoding)
+
+    # Fall back:
+    try:
+        return str(r.content, encoding, errors="replace")
+    except TypeError:
+        return r.content
+
+
+# The unreserved URI characters (RFC 3986)
+UNRESERVED_SET = frozenset(
+    "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" + "0123456789-._~"
+)
+
+
+def unquote_unreserved(uri):
+    """Un-escape any percent-escape sequences in a URI that are unreserved
+    characters. This leaves all reserved, illegal and non-ASCII bytes encoded.
+
+    :rtype: str
+    """
+    parts = uri.split("%")
+    for i in range(1, len(parts)):
+        h = parts[i][0:2]
+        if len(h) == 2 and h.isalnum():
+            try:
+                c = chr(int(h, 16))
+            except ValueError:
+                raise InvalidURL(f"Invalid percent-escape sequence: '{h}'")
+
+            if c in UNRESERVED_SET:
+                parts[i] = c + parts[i][2:]
+            else:
+                parts[i] = f"%{parts[i]}"
+        else:
+            parts[i] = f"%{parts[i]}"
+    return "".join(parts)
+
+
+def requote_uri(uri):
+    """Re-quote the given URI.
+
+    This function passes the given URI through an unquote/quote cycle to
+    ensure that it is fully and consistently quoted.
+
+    :rtype: str
+    """
+    safe_with_percent = "!#$%&'()*+,/:;=?@[]~"
+    safe_without_percent = "!#$&'()*+,/:;=?@[]~"
+    try:
+        # Unquote only the unreserved characters
+        # Then quote only illegal characters (do not quote reserved,
+        # unreserved, or '%')
+        return quote(unquote_unreserved(uri), safe=safe_with_percent)
+    except InvalidURL:
+        # We couldn't unquote the given URI, so let's try quoting it, but
+        # there may be unquoted '%'s in the URI. We need to make sure they're
+        # properly quoted so they do not cause issues elsewhere.
+        return quote(uri, safe=safe_without_percent)
+
+
+def address_in_network(ip, net):
+    """This function allows you to check if an IP belongs to a network subnet
+
+    Example: returns True if ip = 192.168.1.1 and net = 192.168.1.0/24
+             returns False if ip = 192.168.1.1 and net = 192.168.100.0/24
+
+    :rtype: bool
+    """
+    ipaddr = struct.unpack("=L", socket.inet_aton(ip))[0]
+    netaddr, bits = net.split("/")
+    netmask = struct.unpack("=L", socket.inet_aton(dotted_netmask(int(bits))))[0]
+    network = struct.unpack("=L", socket.inet_aton(netaddr))[0] & netmask
+    return (ipaddr & netmask) == (network & netmask)
+
+
+def dotted_netmask(mask):
+    """Converts mask from /xx format to xxx.xxx.xxx.xxx
+
+    Example: if mask is 24 function returns 255.255.255.0
+
+    :rtype: str
+    """
+    bits = 0xFFFFFFFF ^ (1 << 32 - mask) - 1
+    return socket.inet_ntoa(struct.pack(">I", bits))
+
+
+def is_ipv4_address(string_ip):
+    """
+    :rtype: bool
+    """
+    try:
+        socket.inet_aton(string_ip)
+    except OSError:
+        return False
+    return True
+
+
+def is_valid_cidr(string_network):
+    """
+    Very simple check of the cidr format in no_proxy variable.
+
+    :rtype: bool
+    """
+    if string_network.count("/") == 1:
+        try:
+            mask = int(string_network.split("/")[1])
+        except ValueError:
+            return False
+
+        if mask < 1 or mask > 32:
+            return False
+
+        try:
+            socket.inet_aton(string_network.split("/")[0])
+        except OSError:
+            return False
+    else:
+        return False
+    return True
+
+
+@contextlib.contextmanager
+def set_environ(env_name, value):
+    """Set the environment variable 'env_name' to 'value'
+
+    Save previous value, yield, and then restore the previous value stored in
+    the environment variable 'env_name'.
+
+    If 'value' is None, do nothing"""
+    value_changed = value is not None
+    if value_changed:
+        old_value = os.environ.get(env_name)
+        os.environ[env_name] = value
+    try:
+        yield
+    finally:
+        if value_changed:
+            if old_value is None:
+                del os.environ[env_name]
+            else:
+                os.environ[env_name] = old_value
+
+
+def should_bypass_proxies(url, no_proxy):
+    """
+    Returns whether we should bypass proxies or not.
+
+    :rtype: bool
+    """
+    # Prioritize lowercase environment variables over uppercase
+    # to keep a consistent behaviour with other http projects (curl, wget).
+    def get_proxy(key):
+        return os.environ.get(key) or os.environ.get(key.upper())
+
+    # First check whether no_proxy is defined. If it is, check that the URL
+    # we're getting isn't in the no_proxy list.
+    no_proxy_arg = no_proxy
+    if no_proxy is None:
+        no_proxy = get_proxy("no_proxy")
+    parsed = urlparse(url)
+
+    if parsed.hostname is None:
+        # URLs don't always have hostnames, e.g. file:/// urls.
+        return True
+
+    if no_proxy:
+        # We need to check whether we match here. We need to see if we match
+        # the end of the hostname, both with and without the port.
+        no_proxy = (host for host in no_proxy.replace(" ", "").split(",") if host)
+
+        if is_ipv4_address(parsed.hostname):
+            for proxy_ip in no_proxy:
+                if is_valid_cidr(proxy_ip):
+                    if address_in_network(parsed.hostname, proxy_ip):
+                        return True
+                elif parsed.hostname == proxy_ip:
+                    # If no_proxy ip was defined in plain IP notation instead of cidr notation &
+                    # matches the IP of the index
+                    return True
+        else:
+            host_with_port = parsed.hostname
+            if parsed.port:
+                host_with_port += f":{parsed.port}"
+
+            for host in no_proxy:
+                if parsed.hostname.endswith(host) or host_with_port.endswith(host):
+                    # The URL does match something in no_proxy, so we don't want
+                    # to apply the proxies on this URL.
+                    return True
+
+    with set_environ("no_proxy", no_proxy_arg):
+        # parsed.hostname can be `None` in cases such as a file URI.
+        try:
+            bypass = proxy_bypass(parsed.hostname)
+        except (TypeError, socket.gaierror):
+            bypass = False
+
+    if bypass:
+        return True
+
+    return False
+
+
+def get_environ_proxies(url, no_proxy=None):
+    """
+    Return a dict of environment proxies.
+
+    :rtype: dict
+    """
+    if should_bypass_proxies(url, no_proxy=no_proxy):
+        return {}
+    else:
+        return getproxies()
+
+
+def select_proxy(url, proxies):
+    """Select a proxy for the url, if applicable.
+
+    :param url: The url being for the request
+    :param proxies: A dictionary of schemes or schemes and hosts to proxy URLs
+    """
+    proxies = proxies or {}
+    urlparts = urlparse(url)
+    if urlparts.hostname is None:
+        return proxies.get(urlparts.scheme, proxies.get("all"))
+
+    proxy_keys = [
+        urlparts.scheme + "://" + urlparts.hostname,
+        urlparts.scheme,
+        "all://" + urlparts.hostname,
+        "all",
+    ]
+    proxy = None
+    for proxy_key in proxy_keys:
+        if proxy_key in proxies:
+            proxy = proxies[proxy_key]
+            break
+
+    return proxy
+
+
+def resolve_proxies(request, proxies, trust_env=True):
+    """This method takes proxy information from a request and configuration
+    input to resolve a mapping of target proxies. This will consider settings
+    such a NO_PROXY to strip proxy configurations.
+
+    :param request: Request or PreparedRequest
+    :param proxies: A dictionary of schemes or schemes and hosts to proxy URLs
+    :param trust_env: Boolean declaring whether to trust environment configs
+
+    :rtype: dict
+    """
+    proxies = proxies if proxies is not None else {}
+    url = request.url
+    scheme = urlparse(url).scheme
+    no_proxy = proxies.get("no_proxy")
+    new_proxies = proxies.copy()
+
+    if trust_env and not should_bypass_proxies(url, no_proxy=no_proxy):
+        environ_proxies = get_environ_proxies(url, no_proxy=no_proxy)
+
+        proxy = environ_proxies.get(scheme, environ_proxies.get("all"))
+
+        if proxy:
+            new_proxies.setdefault(scheme, proxy)
+    return new_proxies
+
+
+def default_user_agent(name="python-requests"):
+    """
+    Return a string representing the default user agent.
+
+    :rtype: str
+    """
+    return f"{name}/{__version__}"
+
+
+def default_headers():
+    """
+    :rtype: requests.structures.CaseInsensitiveDict
+    """
+    return CaseInsensitiveDict(
+        {
+            "User-Agent": default_user_agent(),
+            "Accept-Encoding": DEFAULT_ACCEPT_ENCODING,
+            "Accept": "*/*",
+            "Connection": "keep-alive",
+        }
+    )
+
+
+def parse_header_links(value):
+    """Return a list of parsed link headers proxies.
+
+    i.e. Link: <http:/.../front.jpeg>; rel=front; type="image/jpeg",<http://.../back.jpeg>; rel=back;type="image/jpeg"
+
+    :rtype: list
+    """
+
+    links = []
+
+    replace_chars = " '\""
+
+    value = value.strip(replace_chars)
+    if not value:
+        return links
+
+    for val in re.split(", *<", value):
+        try:
+            url, params = val.split(";", 1)
+        except ValueError:
+            url, params = val, ""
+
+        link = {"url": url.strip("<> '\"")}
+
+        for param in params.split(";"):
+            try:
+                key, value = param.split("=")
+            except ValueError:
+                break
+
+            link[key.strip(replace_chars)] = value.strip(replace_chars)
+
+        links.append(link)
+
+    return links
+
+
+# Null bytes; no need to recreate these on each call to guess_json_utf
+_null = "\x00".encode("ascii")  # encoding to ASCII for Python 3
+_null2 = _null * 2
+_null3 = _null * 3
+
+
+def guess_json_utf(data):
+    """
+    :rtype: str
+    """
+    # JSON always starts with two ASCII characters, so detection is as
+    # easy as counting the nulls and from their location and count
+    # determine the encoding. Also detect a BOM, if present.
+    sample = data[:4]
+    if sample in (codecs.BOM_UTF32_LE, codecs.BOM_UTF32_BE):
+        return "utf-32"  # BOM included
+    if sample[:3] == codecs.BOM_UTF8:
+        return "utf-8-sig"  # BOM included, MS style (discouraged)
+    if sample[:2] in (codecs.BOM_UTF16_LE, codecs.BOM_UTF16_BE):
+        return "utf-16"  # BOM included
+    nullcount = sample.count(_null)
+    if nullcount == 0:
+        return "utf-8"
+    if nullcount == 2:
+        if sample[::2] == _null2:  # 1st and 3rd are null
+            return "utf-16-be"
+        if sample[1::2] == _null2:  # 2nd and 4th are null
+            return "utf-16-le"
+        # Did not detect 2 valid UTF-16 ascii-range characters
+    if nullcount == 3:
+        if sample[:3] == _null3:
+            return "utf-32-be"
+        if sample[1:] == _null3:
+            return "utf-32-le"
+        # Did not detect a valid UTF-32 ascii-range character
+    return None
+
+
+def prepend_scheme_if_needed(url, new_scheme):
+    """Given a URL that may or may not have a scheme, prepend the given scheme.
+    Does not replace a present scheme with the one provided as an argument.
+
+    :rtype: str
+    """
+    parsed = parse_url(url)
+    scheme, auth, host, port, path, query, fragment = parsed
+
+    # A defect in urlparse determines that there isn't a netloc present in some
+    # urls. We previously assumed parsing was overly cautious, and swapped the
+    # netloc and path. Due to a lack of tests on the original defect, this is
+    # maintained with parse_url for backwards compatibility.
+    netloc = parsed.netloc
+    if not netloc:
+        netloc, path = path, netloc
+
+    if auth:
+        # parse_url doesn't provide the netloc with auth
+        # so we'll add it ourselves.
+        netloc = "@".join([auth, netloc])
+    if scheme is None:
+        scheme = new_scheme
+    if path is None:
+        path = ""
+
+    return urlunparse((scheme, netloc, path, "", query, fragment))
+
+
+def get_auth_from_url(url):
+    """Given a url with authentication components, extract them into a tuple of
+    username,password.
+
+    :rtype: (str,str)
+    """
+    parsed = urlparse(url)
+
+    try:
+        auth = (unquote(parsed.username), unquote(parsed.password))
+    except (AttributeError, TypeError):
+        auth = ("", "")
+
+    return auth
+
+
+def check_header_validity(header):
+    """Verifies that header parts don't contain leading whitespace
+    reserved characters, or return characters.
+
+    :param header: tuple, in the format (name, value).
+    """
+    name, value = header
+    _validate_header_part(header, name, 0)
+    _validate_header_part(header, value, 1)
+
+
+def _validate_header_part(header, header_part, header_validator_index):
+    if isinstance(header_part, str):
+        validator = _HEADER_VALIDATORS_STR[header_validator_index]
+    elif isinstance(header_part, bytes):
+        validator = _HEADER_VALIDATORS_BYTE[header_validator_index]
+    else:
+        raise InvalidHeader(
+            f"Header part ({header_part!r}) from {header} "
+            f"must be of type str or bytes, not {type(header_part)}"
+        )
+
+    if not validator.match(header_part):
+        header_kind = "name" if header_validator_index == 0 else "value"
+        raise InvalidHeader(
+            f"Invalid leading whitespace, reserved character(s), or return"
+            f"character(s) in header {header_kind}: {header_part!r}"
+        )
+
+
+def urldefragauth(url):
+    """
+    Given a url remove the fragment and the authentication part.
+
+    :rtype: str
+    """
+    scheme, netloc, path, params, query, fragment = urlparse(url)
+
+    # see func:`prepend_scheme_if_needed`
+    if not netloc:
+        netloc, path = path, netloc
+
+    netloc = netloc.rsplit("@", 1)[-1]
+
+    return urlunparse((scheme, netloc, path, params, query, ""))
+
+
+def rewind_body(prepared_request):
+    """Move file pointer back to its recorded starting position
+    so it can be read again on redirect.
+    """
+    body_seek = getattr(prepared_request.body, "seek", None)
+    if body_seek is not None and isinstance(
+        prepared_request._body_position, integer_types
+    ):
+        try:
+            body_seek(prepared_request._body_position)
+        except OSError:
+            raise UnrewindableBodyError(
+                "An error occurred when rewinding request body for redirect."
+            )
+    else:
+        raise UnrewindableBodyError("Unable to rewind request body for redirect.")

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/__init__.py

@@ -0,0 +1,76 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
+from ...utils import OptionalDependencyNotAvailable
+
+
+_import_structure = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
+
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["feature_extraction_dpt"] = ["DPTFeatureExtractor"]
+    _import_structure["image_processing_dpt"] = ["DPTImageProcessor"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_dpt"] = [
+        "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "DPTForDepthEstimation",
+        "DPTForSemanticSegmentation",
+        "DPTModel",
+        "DPTPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
+
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .feature_extraction_dpt import DPTFeatureExtractor
+        from .image_processing_dpt import DPTImageProcessor
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_dpt import (
+            DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
+            DPTForDepthEstimation,
+            DPTForSemanticSegmentation,
+            DPTModel,
+            DPTPreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/configuration_dpt.py

@@ -0,0 +1,284 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" DPT model configuration"""
+
+import copy
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+from ..auto.configuration_auto import CONFIG_MAPPING
+from ..bit import BitConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+from ..deprecated._archive_maps import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP  # noqa: F401, E402
+
+
+class DPTConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`DPTModel`]. It is used to instantiate an DPT
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the DPT
+    [Intel/dpt-large](https://huggingface.co/Intel/dpt-large) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        image_size (`int`, *optional*, defaults to 384):
+            The size (resolution) of each image.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        num_channels (`int`, *optional*, defaults to 3):
+            The number of input channels.
+        is_hybrid (`bool`, *optional*, defaults to `False`):
+            Whether to use a hybrid backbone. Useful in the context of loading DPT-Hybrid models.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+        backbone_out_indices (`List[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
+            Indices of the intermediate hidden states to use from backbone.
+        readout_type (`str`, *optional*, defaults to `"project"`):
+            The readout type to use when processing the readout token (CLS token) of the intermediate hidden states of
+            the ViT backbone. Can be one of [`"ignore"`, `"add"`, `"project"`].
+
+            - "ignore" simply ignores the CLS token.
+            - "add" passes the information from the CLS token to all other tokens by adding the representations.
+            - "project" passes information to the other tokens by concatenating the readout to all other tokens before
+              projecting the
+            representation to the original feature dimension D using a linear layer followed by a GELU non-linearity.
+        reassemble_factors (`List[int]`, *optional*, defaults to `[4, 2, 1, 0.5]`):
+            The up/downsampling factors of the reassemble layers.
+        neck_hidden_sizes (`List[str]`, *optional*, defaults to `[96, 192, 384, 768]`):
+            The hidden sizes to project to for the feature maps of the backbone.
+        fusion_hidden_size (`int`, *optional*, defaults to 256):
+            The number of channels before fusion.
+        head_in_index (`int`, *optional*, defaults to -1):
+            The index of the features to use in the heads.
+        use_batch_norm_in_fusion_residual (`bool`, *optional*, defaults to `False`):
+            Whether to use batch normalization in the pre-activate residual units of the fusion blocks.
+        use_bias_in_fusion_residual (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the pre-activate residual units of the fusion blocks.
+        add_projection (`bool`, *optional*, defaults to `False`):
+            Whether to add a projection layer before the depth estimation head.
+        use_auxiliary_head (`bool`, *optional*, defaults to `True`):
+            Whether to use an auxiliary head during training.
+        auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
+            Weight of the cross-entropy loss of the auxiliary head.
+        semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
+            The index that is ignored by the loss function of the semantic segmentation model.
+        semantic_classifier_dropout (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the semantic classification head.
+        backbone_featmap_shape (`List[int]`, *optional*, defaults to `[1, 1024, 24, 24]`):
+            Used only for the `hybrid` embedding type. The shape of the feature maps of the backbone.
+        neck_ignore_stages (`List[int]`, *optional*, defaults to `[0, 1]`):
+            Used only for the `hybrid` embedding type. The stages of the readout layers to ignore.
+        backbone_config (`Union[Dict[str, Any], PretrainedConfig]`, *optional*):
+            The configuration of the backbone model. Only used in case `is_hybrid` is `True` or in case you want to
+            leverage the [`AutoBackbone`] API.
+        backbone (`str`, *optional*):
+            Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
+            will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
+            is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
+        use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
+            Whether to use pretrained weights for the backbone.
+        use_timm_backbone (`bool`, *optional*, defaults to `False`):
+            Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
+            library.
+        backbone_kwargs (`dict`, *optional*):
+            Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
+            e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
+
+    Example:
+
+    ```python
+    >>> from transformers import DPTModel, DPTConfig
+
+    >>> # Initializing a DPT dpt-large style configuration
+    >>> configuration = DPTConfig()
+
+    >>> # Initializing a model from the dpt-large style configuration
+    >>> model = DPTModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "dpt"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        image_size=384,
+        patch_size=16,
+        num_channels=3,
+        is_hybrid=False,
+        qkv_bias=True,
+        backbone_out_indices=[2, 5, 8, 11],
+        readout_type="project",
+        reassemble_factors=[4, 2, 1, 0.5],
+        neck_hidden_sizes=[96, 192, 384, 768],
+        fusion_hidden_size=256,
+        head_in_index=-1,
+        use_batch_norm_in_fusion_residual=False,
+        use_bias_in_fusion_residual=None,
+        add_projection=False,
+        use_auxiliary_head=True,
+        auxiliary_loss_weight=0.4,
+        semantic_loss_ignore_index=255,
+        semantic_classifier_dropout=0.1,
+        backbone_featmap_shape=[1, 1024, 24, 24],
+        neck_ignore_stages=[0, 1],
+        backbone_config=None,
+        backbone=None,
+        use_pretrained_backbone=False,
+        use_timm_backbone=False,
+        backbone_kwargs=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.is_hybrid = is_hybrid
+
+        if use_pretrained_backbone:
+            raise ValueError("Pretrained backbones are not supported yet.")
+
+        use_autobackbone = False
+        if self.is_hybrid:
+            if backbone_config is None and backbone is None:
+                logger.info("Initializing the config with a `BiT` backbone.")
+                backbone_config = {
+                    "global_padding": "same",
+                    "layer_type": "bottleneck",
+                    "depths": [3, 4, 9],
+                    "out_features": ["stage1", "stage2", "stage3"],
+                    "embedding_dynamic_padding": True,
+                }
+                backbone_config = BitConfig(**backbone_config)
+            elif isinstance(backbone_config, dict):
+                logger.info("Initializing the config with a `BiT` backbone.")
+                backbone_config = BitConfig(**backbone_config)
+            elif isinstance(backbone_config, PretrainedConfig):
+                backbone_config = backbone_config
+            else:
+                raise ValueError(
+                    f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."
+                )
+            self.backbone_config = backbone_config
+            self.backbone_featmap_shape = backbone_featmap_shape
+            self.neck_ignore_stages = neck_ignore_stages
+
+            if readout_type != "project":
+                raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode.")
+
+        elif backbone_config is not None:
+            use_autobackbone = True
+
+            if isinstance(backbone_config, dict):
+                backbone_model_type = backbone_config.get("model_type")
+                config_class = CONFIG_MAPPING[backbone_model_type]
+                backbone_config = config_class.from_dict(backbone_config)
+
+            self.backbone_config = backbone_config
+            self.backbone_featmap_shape = None
+            self.neck_ignore_stages = []
+        else:
+            self.backbone_config = backbone_config
+            self.backbone_featmap_shape = None
+            self.neck_ignore_stages = []
+
+        if use_autobackbone and backbone_config is not None and backbone is not None:
+            raise ValueError("You can't specify both `backbone` and `backbone_config`.")
+
+        if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
+            raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+
+        self.backbone = backbone
+        self.use_pretrained_backbone = use_pretrained_backbone
+        self.use_timm_backbone = use_timm_backbone
+        self.backbone_kwargs = backbone_kwargs
+        self.num_hidden_layers = None if use_autobackbone else num_hidden_layers
+        self.num_attention_heads = None if use_autobackbone else num_attention_heads
+        self.intermediate_size = None if use_autobackbone else intermediate_size
+        self.hidden_dropout_prob = None if use_autobackbone else hidden_dropout_prob
+        self.attention_probs_dropout_prob = None if use_autobackbone else attention_probs_dropout_prob
+        self.layer_norm_eps = None if use_autobackbone else layer_norm_eps
+        self.image_size = None if use_autobackbone else image_size
+        self.patch_size = None if use_autobackbone else patch_size
+        self.num_channels = None if use_autobackbone else num_channels
+        self.qkv_bias = None if use_autobackbone else qkv_bias
+        self.backbone_out_indices = None if use_autobackbone else backbone_out_indices
+
+        if readout_type not in ["ignore", "add", "project"]:
+            raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']")
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.readout_type = readout_type
+        self.reassemble_factors = reassemble_factors
+        self.neck_hidden_sizes = neck_hidden_sizes
+        self.fusion_hidden_size = fusion_hidden_size
+        self.head_in_index = head_in_index
+        self.use_batch_norm_in_fusion_residual = use_batch_norm_in_fusion_residual
+        self.use_bias_in_fusion_residual = use_bias_in_fusion_residual
+        self.add_projection = add_projection
+
+        # auxiliary head attributes (semantic segmentation)
+        self.use_auxiliary_head = use_auxiliary_head
+        self.auxiliary_loss_weight = auxiliary_loss_weight
+        self.semantic_loss_ignore_index = semantic_loss_ignore_index
+        self.semantic_classifier_dropout = semantic_classifier_dropout
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+
+        if output["backbone_config"] is not None:
+            output["backbone_config"] = self.backbone_config.to_dict()
+
+        output["model_type"] = self.__class__.model_type
+        return output

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/convert_dpt_beit_to_hf.py

@@ -0,0 +1,306 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert DPT 3.1 checkpoints from the MiDaS repository. URL: https://github.com/isl-org/MiDaS"""
+
+
+import argparse
+from pathlib import Path
+
+import requests
+import torch
+from PIL import Image
+
+from transformers import BeitConfig, DPTConfig, DPTForDepthEstimation, DPTImageProcessor
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_dpt_config(model_name):
+    hidden_size = 768
+    num_hidden_layers = 12
+    num_attention_heads = 12
+    intermediate_size = 3072
+    out_features = ["stage3", "stage6", "stage9", "stage12"]  # beit-base-384 uses [2, 5, 8, 11]
+
+    if "large" in model_name:
+        hidden_size = 1024
+        num_hidden_layers = 24
+        num_attention_heads = 16
+        intermediate_size = 4096
+        out_features = ["stage6", "stage12", "stage18", "stage24"]  # beit-large-512 uses [5, 11, 17, 23]
+
+    if "512" in model_name:
+        image_size = 512
+    elif "384" in model_name:
+        image_size = 384
+    else:
+        raise ValueError("Model not supported")
+
+    backbone_config = BeitConfig(
+        image_size=image_size,
+        num_hidden_layers=num_hidden_layers,
+        hidden_size=hidden_size,
+        intermediate_size=intermediate_size,
+        num_attention_heads=num_attention_heads,
+        use_relative_position_bias=True,
+        reshape_hidden_states=False,
+        out_features=out_features,
+    )
+
+    neck_hidden_sizes = [256, 512, 1024, 1024] if "large" in model_name else [96, 192, 384, 768]
+    config = DPTConfig(backbone_config=backbone_config, neck_hidden_sizes=neck_hidden_sizes)
+
+    return config, image_size
+
+
+# here we list all keys to be renamed (original name on the left, our name on the right)
+def create_rename_keys(config):
+    rename_keys = []
+
+    # fmt: off
+    # stem
+    rename_keys.append(("pretrained.model.cls_token", "backbone.embeddings.cls_token"))
+    rename_keys.append(("pretrained.model.patch_embed.proj.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
+    rename_keys.append(("pretrained.model.patch_embed.proj.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
+
+    # Transfomer encoder
+    for i in range(config.backbone_config.num_hidden_layers):
+        rename_keys.append((f"pretrained.model.blocks.{i}.gamma_1", f"backbone.encoder.layer.{i}.lambda_1"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.gamma_2", f"backbone.encoder.layer.{i}.lambda_2"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.norm1.weight", f"backbone.encoder.layer.{i}.layernorm_before.weight"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.norm1.bias", f"backbone.encoder.layer.{i}.layernorm_before.bias"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.norm2.weight", f"backbone.encoder.layer.{i}.layernorm_after.weight"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.norm2.bias", f"backbone.encoder.layer.{i}.layernorm_after.bias"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc1.weight", f"backbone.encoder.layer.{i}.intermediate.dense.weight"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc1.bias", f"backbone.encoder.layer.{i}.intermediate.dense.bias"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc2.weight", f"backbone.encoder.layer.{i}.output.dense.weight"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.mlp.fc2.bias", f"backbone.encoder.layer.{i}.output.dense.bias"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.attn.proj.weight", f"backbone.encoder.layer.{i}.attention.output.dense.weight"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.attn.proj.bias", f"backbone.encoder.layer.{i}.attention.output.dense.bias"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.attn.relative_position_bias_table", f"backbone.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_bias_table"))
+        rename_keys.append((f"pretrained.model.blocks.{i}.attn.relative_position_index", f"backbone.encoder.layer.{i}.attention.attention.relative_position_bias.relative_position_index"))
+
+    # activation postprocessing (readout projections + resize blocks)
+    for i in range(4):
+        rename_keys.append((f"pretrained.act_postprocess{i+1}.0.project.0.weight", f"neck.reassemble_stage.readout_projects.{i}.0.weight"))
+        rename_keys.append((f"pretrained.act_postprocess{i+1}.0.project.0.bias", f"neck.reassemble_stage.readout_projects.{i}.0.bias"))
+
+        rename_keys.append((f"pretrained.act_postprocess{i+1}.3.weight", f"neck.reassemble_stage.layers.{i}.projection.weight"))
+        rename_keys.append((f"pretrained.act_postprocess{i+1}.3.bias", f"neck.reassemble_stage.layers.{i}.projection.bias"))
+
+        if i != 2:
+            rename_keys.append((f"pretrained.act_postprocess{i+1}.4.weight", f"neck.reassemble_stage.layers.{i}.resize.weight"))
+            rename_keys.append((f"pretrained.act_postprocess{i+1}.4.bias", f"neck.reassemble_stage.layers.{i}.resize.bias"))
+
+    # refinenet (tricky here)
+    mapping = {1:3, 2:2, 3:1, 4:0}
+
+    for i in range(1, 5):
+        j = mapping[i]
+        rename_keys.append((f"scratch.refinenet{i}.out_conv.weight", f"neck.fusion_stage.layers.{j}.projection.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.out_conv.bias", f"neck.fusion_stage.layers.{j}.projection.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.bias"))
+
+    # scratch convolutions
+    for i in range(4):
+        rename_keys.append((f"scratch.layer{i+1}_rn.weight", f"neck.convs.{i}.weight"))
+
+    # head
+    for i in range(0, 5, 2):
+        rename_keys.append((f"scratch.output_conv.{i}.weight", f"head.head.{i}.weight"))
+        rename_keys.append((f"scratch.output_conv.{i}.bias", f"head.head.{i}.bias"))
+
+    return rename_keys
+
+
+def remove_ignore_keys_(state_dict):
+    ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
+    for k in ignore_keys:
+        state_dict.pop(k, None)
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_q_k_v(state_dict, config):
+    hidden_size = config.backbone_config.hidden_size
+    for i in range(config.backbone_config.num_hidden_layers):
+        # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
+        in_proj_weight = state_dict.pop(f"pretrained.model.blocks.{i}.attn.qkv.weight")
+        q_bias = state_dict.pop(f"pretrained.model.blocks.{i}.attn.q_bias")
+        v_bias = state_dict.pop(f"pretrained.model.blocks.{i}.attn.v_bias")
+        # next, add query, keys and values (in that order) to the state dict
+        state_dict[f"backbone.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[:hidden_size, :]
+        state_dict[f"backbone.encoder.layer.{i}.attention.attention.query.bias"] = q_bias
+        state_dict[f"backbone.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
+            hidden_size : hidden_size * 2, :
+        ]
+        state_dict[f"backbone.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[-hidden_size:, :]
+        state_dict[f"backbone.encoder.layer.{i}.attention.attention.value.bias"] = v_bias
+
+
+def rename_key(dct, old, new):
+    val = dct.pop(old)
+    dct[new] = val
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@torch.no_grad()
+def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub):
+    """
+    Copy/paste/tweak model's weights to our DPT structure.
+    """
+
+    name_to_url = {
+        "dpt-beit-large-512": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt",
+        "dpt-beit-large-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_384.pt",
+        "dpt-beit-base-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_base_384.pt",
+    }
+
+    # define DPT configuration based on URL
+    checkpoint_url = name_to_url[model_name]
+    config, image_size = get_dpt_config(model_name)
+    # load original state_dict from URL
+    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
+    # remove certain keys
+    remove_ignore_keys_(state_dict)
+    # rename keys
+    rename_keys = create_rename_keys(config)
+    for src, dest in rename_keys:
+        rename_key(state_dict, src, dest)
+    # read in qkv matrices
+    read_in_q_k_v(state_dict, config)
+
+    # load HuggingFace model
+    model = DPTForDepthEstimation(config)
+    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+    print("Missing keys:", missing_keys)
+    print("Unexpected keys:", unexpected_keys)
+    assert missing_keys == []
+    # assert unexpected_keys == ["pretrained.model.fc_norm.weight", "pretrained.model.fc_norm.bias"]
+    model.eval()
+
+    # Check outputs on an image
+    # We set `keep_aspect_ratio=False` as our current BEiT does not support arbitrary window sizes
+    processor = DPTImageProcessor(
+        size={"height": image_size, "width": image_size}, keep_aspect_ratio=False, ensure_multiple_of=32
+    )
+
+    image = prepare_img()
+    pixel_values = processor(image, return_tensors="pt").pixel_values
+
+    print("First values of pixel values:", pixel_values[0, 0, :3, :3])
+    print("Mean of pixel values:", pixel_values.mean().item())
+    print("Shape of pixel values:", pixel_values.shape)
+
+    import requests
+    from PIL import Image
+    from torchvision import transforms
+
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    image = Image.open(requests.get(url, stream=True).raw)
+
+    transforms = transforms.Compose(
+        [
+            transforms.Resize((image_size, image_size)),
+            transforms.ToTensor(),
+        ]
+    )
+    pixel_values = transforms(image).unsqueeze(0)
+
+    # forward pass
+    with torch.no_grad():
+        outputs = model(pixel_values)
+
+    predicted_depth = outputs.predicted_depth
+
+    print("Shape of predicted depth:", predicted_depth.shape)
+    print("First values of predicted depth:", predicted_depth[0, :3, :3])
+
+    # assert logits
+    # TODO there's still a small difference with the original logits
+    if model_name == "dpt-beit-large-512":
+        # OK, checked
+        expected_shape = torch.Size([1, 512, 512])
+        expected_slice = torch.tensor(
+            [[2804.6260, 2792.5708, 2812.9263], [2772.0288, 2780.1118, 2796.2529], [2748.1094, 2766.6558, 2766.9834]]
+        )
+    elif model_name == "dpt-beit-large-384":
+        # OK, checked
+        expected_shape = torch.Size([1, 384, 384])
+        expected_slice = torch.tensor(
+            [[1783.2273, 1780.5729, 1792.6453], [1759.9817, 1765.5359, 1778.5002], [1739.1633, 1754.7903, 1757.1990]],
+        )
+    elif model_name == "dpt-beit-base-384":
+        # OK, checked
+        expected_shape = torch.Size([1, 384, 384])
+        expected_slice = torch.tensor(
+            [[2898.4482, 2891.3750, 2904.8079], [2858.6685, 2877.2615, 2894.4507], [2842.1235, 2854.1023, 2861.6328]],
+        )
+
+    assert predicted_depth.shape == torch.Size(expected_shape)
+    assert torch.allclose(predicted_depth[0, :3, :3], expected_slice)
+    print("Looks ok!")
+
+    if pytorch_dump_folder_path is not None:
+        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+        print(f"Saving model and processor to {pytorch_dump_folder_path}")
+        model.save_pretrained(pytorch_dump_folder_path)
+        processor.save_pretrained(pytorch_dump_folder_path)
+
+    if push_to_hub:
+        print("Pushing model and processor to hub...")
+        model.push_to_hub(repo_id=f"nielsr/{model_name}")
+        processor.push_to_hub(repo_id=f"nielsr/{model_name}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--model_name",
+        default="dpt-beit-large-512",
+        type=str,
+        choices=["dpt-beit-large-512", "dpt-beit-large-384", "dpt-beit-base-384"],
+        help="Name of the model you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default=None,
+        type=str,
+        help="Path to the output PyTorch model directory.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Whether to push the model to the hub after conversion.",
+    )
+
+    args = parser.parse_args()
+    convert_dpt_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/convert_dpt_hybrid_to_pytorch.py

@@ -0,0 +1,316 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert DPT checkpoints from the original repository. URL: https://github.com/isl-org/DPT"""
+
+
+import argparse
+import json
+from pathlib import Path
+
+import requests
+import torch
+from huggingface_hub import cached_download, hf_hub_url
+from PIL import Image
+
+from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_dpt_config(checkpoint_url):
+    config = DPTConfig(embedding_type="hybrid")
+
+    if "large" in checkpoint_url:
+        config.hidden_size = 1024
+        config.intermediate_size = 4096
+        config.num_hidden_layers = 24
+        config.num_attention_heads = 16
+        config.backbone_out_indices = [5, 11, 17, 23]
+        config.neck_hidden_sizes = [256, 512, 1024, 1024]
+        expected_shape = (1, 384, 384)
+
+    if "nyu" or "midas" in checkpoint_url:
+        config.hidden_size = 768
+        config.reassemble_factors = [1, 1, 1, 0.5]
+        config.neck_hidden_sizes = [256, 512, 768, 768]
+        config.num_labels = 150
+        config.patch_size = 16
+        expected_shape = (1, 384, 384)
+        config.use_batch_norm_in_fusion_residual = False
+        config.readout_type = "project"
+
+    if "ade" in checkpoint_url:
+        config.use_batch_norm_in_fusion_residual = True
+        config.hidden_size = 768
+        config.reassemble_stage = [1, 1, 1, 0.5]
+        config.num_labels = 150
+        config.patch_size = 16
+        repo_id = "huggingface/label-files"
+        filename = "ade20k-id2label.json"
+        id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
+        id2label = {int(k): v for k, v in id2label.items()}
+        config.id2label = id2label
+        config.label2id = {v: k for k, v in id2label.items()}
+        expected_shape = [1, 150, 480, 480]
+
+    return config, expected_shape
+
+
+def remove_ignore_keys_(state_dict):
+    ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
+    for k in ignore_keys:
+        state_dict.pop(k, None)
+
+
+def rename_key(name):
+    if (
+        "pretrained.model" in name
+        and "cls_token" not in name
+        and "pos_embed" not in name
+        and "patch_embed" not in name
+    ):
+        name = name.replace("pretrained.model", "dpt.encoder")
+    if "pretrained.model" in name:
+        name = name.replace("pretrained.model", "dpt.embeddings")
+    if "patch_embed" in name:
+        name = name.replace("patch_embed", "")
+    if "pos_embed" in name:
+        name = name.replace("pos_embed", "position_embeddings")
+    if "attn.proj" in name:
+        name = name.replace("attn.proj", "attention.output.dense")
+    if "proj" in name and "project" not in name:
+        name = name.replace("proj", "projection")
+    if "blocks" in name:
+        name = name.replace("blocks", "layer")
+    if "mlp.fc1" in name:
+        name = name.replace("mlp.fc1", "intermediate.dense")
+    if "mlp.fc2" in name:
+        name = name.replace("mlp.fc2", "output.dense")
+    if "norm1" in name and "backbone" not in name:
+        name = name.replace("norm1", "layernorm_before")
+    if "norm2" in name and "backbone" not in name:
+        name = name.replace("norm2", "layernorm_after")
+    if "scratch.output_conv" in name:
+        name = name.replace("scratch.output_conv", "head")
+    if "scratch" in name:
+        name = name.replace("scratch", "neck")
+    if "layer1_rn" in name:
+        name = name.replace("layer1_rn", "convs.0")
+    if "layer2_rn" in name:
+        name = name.replace("layer2_rn", "convs.1")
+    if "layer3_rn" in name:
+        name = name.replace("layer3_rn", "convs.2")
+    if "layer4_rn" in name:
+        name = name.replace("layer4_rn", "convs.3")
+    if "refinenet" in name:
+        layer_idx = int(name[len("neck.refinenet") : len("neck.refinenet") + 1])
+        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
+        name = name.replace(f"refinenet{layer_idx}", f"fusion_stage.layers.{abs(layer_idx-4)}")
+    if "out_conv" in name:
+        name = name.replace("out_conv", "projection")
+    if "resConfUnit1" in name:
+        name = name.replace("resConfUnit1", "residual_layer1")
+    if "resConfUnit2" in name:
+        name = name.replace("resConfUnit2", "residual_layer2")
+    if "conv1" in name:
+        name = name.replace("conv1", "convolution1")
+    if "conv2" in name:
+        name = name.replace("conv2", "convolution2")
+    # readout blocks
+    if "pretrained.act_postprocess1.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess1.0.project.0", "neck.reassemble_stage.readout_projects.0.0")
+    if "pretrained.act_postprocess2.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess2.0.project.0", "neck.reassemble_stage.readout_projects.1.0")
+    if "pretrained.act_postprocess3.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess3.0.project.0", "neck.reassemble_stage.readout_projects.2.0")
+    if "pretrained.act_postprocess4.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess4.0.project.0", "neck.reassemble_stage.readout_projects.3.0")
+
+    # resize blocks
+    if "pretrained.act_postprocess1.3" in name:
+        name = name.replace("pretrained.act_postprocess1.3", "neck.reassemble_stage.layers.0.projection")
+    if "pretrained.act_postprocess1.4" in name:
+        name = name.replace("pretrained.act_postprocess1.4", "neck.reassemble_stage.layers.0.resize")
+    if "pretrained.act_postprocess2.3" in name:
+        name = name.replace("pretrained.act_postprocess2.3", "neck.reassemble_stage.layers.1.projection")
+    if "pretrained.act_postprocess2.4" in name:
+        name = name.replace("pretrained.act_postprocess2.4", "neck.reassemble_stage.layers.1.resize")
+    if "pretrained.act_postprocess3.3" in name:
+        name = name.replace("pretrained.act_postprocess3.3", "neck.reassemble_stage.layers.2.projection")
+    if "pretrained.act_postprocess4.3" in name:
+        name = name.replace("pretrained.act_postprocess4.3", "neck.reassemble_stage.layers.3.projection")
+    if "pretrained.act_postprocess4.4" in name:
+        name = name.replace("pretrained.act_postprocess4.4", "neck.reassemble_stage.layers.3.resize")
+    if "pretrained" in name:
+        name = name.replace("pretrained", "dpt")
+    if "bn" in name:
+        name = name.replace("bn", "batch_norm")
+    if "head" in name:
+        name = name.replace("head", "head.head")
+    if "encoder.norm" in name:
+        name = name.replace("encoder.norm", "layernorm")
+    if "auxlayer" in name:
+        name = name.replace("auxlayer", "auxiliary_head.head")
+    if "backbone" in name:
+        name = name.replace("backbone", "backbone.bit.encoder")
+
+    if ".." in name:
+        name = name.replace("..", ".")
+
+    if "stem.conv" in name:
+        name = name.replace("stem.conv", "bit.embedder.convolution")
+    if "blocks" in name:
+        name = name.replace("blocks", "layers")
+    if "convolution" in name and "backbone" in name:
+        name = name.replace("convolution", "conv")
+    if "layer" in name and "backbone" in name:
+        name = name.replace("layer", "layers")
+    if "backbone.bit.encoder.bit" in name:
+        name = name.replace("backbone.bit.encoder.bit", "backbone.bit")
+    if "embedder.conv" in name:
+        name = name.replace("embedder.conv", "embedder.convolution")
+    if "backbone.bit.encoder.stem.norm" in name:
+        name = name.replace("backbone.bit.encoder.stem.norm", "backbone.bit.embedder.norm")
+    return name
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_q_k_v(state_dict, config):
+    for i in range(config.num_hidden_layers):
+        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
+        in_proj_weight = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight")
+        in_proj_bias = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias")
+        # next, add query, keys and values (in that order) to the state dict
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[: config.hidden_size, :]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
+            config.hidden_size : config.hidden_size * 2, :
+        ]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[
+            config.hidden_size : config.hidden_size * 2
+        ]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
+            -config.hidden_size :, :
+        ]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@torch.no_grad()
+def convert_dpt_checkpoint(checkpoint_url, pytorch_dump_folder_path, push_to_hub, model_name, show_prediction):
+    """
+    Copy/paste/tweak model's weights to our DPT structure.
+    """
+
+    # define DPT configuration based on URL
+    config, expected_shape = get_dpt_config(checkpoint_url)
+    # load original state_dict from URL
+    # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
+    state_dict = torch.load(checkpoint_url, map_location="cpu")
+    # remove certain keys
+    remove_ignore_keys_(state_dict)
+    # rename keys
+    for key in state_dict.copy().keys():
+        val = state_dict.pop(key)
+        state_dict[rename_key(key)] = val
+    # read in qkv matrices
+    read_in_q_k_v(state_dict, config)
+
+    # load HuggingFace model
+    model = DPTForSemanticSegmentation(config) if "ade" in checkpoint_url else DPTForDepthEstimation(config)
+    model.load_state_dict(state_dict)
+    model.eval()
+
+    # Check outputs on an image
+    size = 480 if "ade" in checkpoint_url else 384
+    image_processor = DPTImageProcessor(size=size)
+
+    image = prepare_img()
+    encoding = image_processor(image, return_tensors="pt")
+
+    # forward pass
+    outputs = model(**encoding).logits if "ade" in checkpoint_url else model(**encoding).predicted_depth
+
+    if show_prediction:
+        prediction = (
+            torch.nn.functional.interpolate(
+                outputs.unsqueeze(1),
+                size=(image.size[1], image.size[0]),
+                mode="bicubic",
+                align_corners=False,
+            )
+            .squeeze()
+            .cpu()
+            .numpy()
+        )
+
+        Image.fromarray((prediction / prediction.max()) * 255).show()
+
+    if pytorch_dump_folder_path is not None:
+        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+        print(f"Saving model to {pytorch_dump_folder_path}")
+        model.save_pretrained(pytorch_dump_folder_path)
+        print(f"Saving image processor to {pytorch_dump_folder_path}")
+        image_processor.save_pretrained(pytorch_dump_folder_path)
+
+    if push_to_hub:
+        model.push_to_hub("ybelkada/dpt-hybrid-midas")
+        image_processor.push_to_hub("ybelkada/dpt-hybrid-midas")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--checkpoint_url",
+        default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
+        type=str,
+        help="URL of the original DPT checkpoint you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default=None,
+        type=str,
+        required=False,
+        help="Path to the output PyTorch model directory.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--model_name",
+        default="dpt-large",
+        type=str,
+        help="Name of the model, in case you're pushing to the hub.",
+    )
+    parser.add_argument(
+        "--show_prediction",
+        action="store_true",
+    )
+
+    args = parser.parse_args()
+    convert_dpt_checkpoint(
+        args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
+    )

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/convert_dpt_swinv2_to_hf.py

@@ -0,0 +1,322 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert DPT 3.1 checkpoints from the MiDaS repository. URL: https://github.com/isl-org/MiDaS"""
+
+
+import argparse
+from pathlib import Path
+
+import requests
+import torch
+from PIL import Image
+
+from transformers import DPTConfig, DPTForDepthEstimation, DPTImageProcessor, Swinv2Config
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_dpt_config(model_name):
+    if "tiny" in model_name:
+        embed_dim = 96
+        depths = (2, 2, 6, 2)
+        num_heads = (3, 6, 12, 24)
+        window_size = 16
+        # note: for Swinv2-tiny authors used the window_size = 16 variant
+        # as seen here: https://github.com/isl-org/MiDaS/blob/bdc4ed64c095e026dc0a2f17cabb14d58263decb/midas/backbones/swin2.py#L26
+        pretrained_window_sizes = (0, 0, 0, 0)
+    elif "base" in model_name:
+        embed_dim = 128
+        depths = (2, 2, 18, 2)
+        num_heads = (4, 8, 16, 32)
+        window_size = 24
+        pretrained_window_sizes = (12, 12, 12, 6)
+    elif "large" in model_name:
+        embed_dim = 192
+        depths = (2, 2, 18, 2)
+        num_heads = (6, 12, 24, 48)
+        window_size = 24
+        pretrained_window_sizes = (12, 12, 12, 6)
+
+    if "384" in model_name:
+        image_size = 384
+    elif "256" in model_name:
+        image_size = 256
+    else:
+        raise ValueError("Model not supported, to do")
+
+    backbone_config = Swinv2Config(
+        image_size=image_size,
+        embed_dim=embed_dim,
+        depths=depths,
+        window_size=window_size,
+        pretrained_window_sizes=pretrained_window_sizes,
+        num_heads=num_heads,
+        out_features=["stage1", "stage2", "stage3", "stage4"],
+    )
+
+    if model_name == "dpt-swinv2-tiny-256":
+        neck_hidden_sizes = [96, 192, 384, 768]
+    elif model_name == "dpt-swinv2-base-384":
+        neck_hidden_sizes = [128, 256, 512, 1024]
+    elif model_name == "dpt-swinv2-large-384":
+        neck_hidden_sizes = [192, 384, 768, 1536]
+
+    config = DPTConfig(backbone_config=backbone_config, neck_hidden_sizes=neck_hidden_sizes)
+
+    return config, image_size
+
+
+# here we list all keys to be renamed (original name on the left, our name on the right)
+def create_rename_keys(config):
+    rename_keys = []
+
+    # fmt: off
+    # stem
+    rename_keys.append(("pretrained.model.patch_embed.proj.weight", "backbone.embeddings.patch_embeddings.projection.weight"))
+    rename_keys.append(("pretrained.model.patch_embed.proj.bias", "backbone.embeddings.patch_embeddings.projection.bias"))
+    rename_keys.append(("pretrained.model.patch_embed.norm.weight", "backbone.embeddings.norm.weight"))
+    rename_keys.append(("pretrained.model.patch_embed.norm.bias", "backbone.embeddings.norm.bias"))
+
+    # transformer encoder
+    for i in range(len(config.backbone_config.depths)):
+        for j in range(config.backbone_config.depths[i]):
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.logit_scale", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.logit_scale"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.0.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.0.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.0.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.0.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.cpb_mlp.2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.continuous_position_bias_mlp.2.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.q_bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.v_bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.proj.weight", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.attn.proj.bias", f"backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc1.weight", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc1.bias", f"backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.mlp.fc2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm2.weight", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.blocks.{j}.norm2.bias", f"backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias"))
+
+        # downsample parameters
+        if i in [0,1,2]:
+            rename_keys.append((f"pretrained.model.layers.{i}.downsample.reduction.weight", f"backbone.encoder.layers.{i}.downsample.reduction.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.downsample.norm.weight", f"backbone.encoder.layers.{i}.downsample.norm.weight"))
+            rename_keys.append((f"pretrained.model.layers.{i}.downsample.norm.bias", f"backbone.encoder.layers.{i}.downsample.norm.bias"))
+
+    # note: non-Transformer backbones like Swinv2, LeViT et al don't require activation postprocessing (readout projections + resize blocks)
+
+    # refinenet (tricky here)
+    mapping = {1:3, 2:2, 3:1, 4:0}
+
+    for i in range(1, 5):
+        j = mapping[i]
+        rename_keys.append((f"scratch.refinenet{i}.out_conv.weight", f"neck.fusion_stage.layers.{j}.projection.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.out_conv.bias", f"neck.fusion_stage.layers.{j}.projection.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution1.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit1.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer1.convolution2.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv1.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution1.bias"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.weight", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.weight"))
+        rename_keys.append((f"scratch.refinenet{i}.resConfUnit2.conv2.bias", f"neck.fusion_stage.layers.{j}.residual_layer2.convolution2.bias"))
+
+    # scratch convolutions
+    for i in range(4):
+        rename_keys.append((f"scratch.layer{i+1}_rn.weight", f"neck.convs.{i}.weight"))
+
+    # head
+    for i in range(0, 5, 2):
+        rename_keys.append((f"scratch.output_conv.{i}.weight", f"head.head.{i}.weight"))
+        rename_keys.append((f"scratch.output_conv.{i}.bias", f"head.head.{i}.bias"))
+
+    return rename_keys
+
+
+def remove_ignore_keys_(state_dict):
+    ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
+    for k in ignore_keys:
+        state_dict.pop(k, None)
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_q_k_v(state_dict, config, model):
+    for i in range(len(config.backbone_config.depths)):
+        for j in range(config.backbone_config.depths[i]):
+            dim = model.backbone.encoder.layers[i].blocks[j].attention.self.all_head_size
+            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
+            in_proj_weight = state_dict.pop(f"pretrained.model.layers.{i}.blocks.{j}.attn.qkv.weight")
+            # next, add query, keys and values (in that order) to the state dict
+            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.query.weight"] = in_proj_weight[:dim, :]
+            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.key.weight"] = in_proj_weight[
+                dim : dim * 2, :
+            ]
+            state_dict[f"backbone.encoder.layers.{i}.blocks.{j}.attention.self.value.weight"] = in_proj_weight[
+                -dim:, :
+            ]
+
+
+def rename_key(dct, old, new):
+    val = dct.pop(old)
+    dct[new] = val
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@torch.no_grad()
+def convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, verify_logits, push_to_hub):
+    """
+    Copy/paste/tweak model's weights to our DPT structure.
+    """
+
+    name_to_url = {
+        "dpt-swinv2-tiny-256": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_tiny_256.pt",
+        "dpt-swinv2-base-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_base_384.pt",
+        "dpt-swinv2-large-384": "https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_swin2_large_384.pt",
+    }
+
+    # define DPT configuration based on URL
+    checkpoint_url = name_to_url[model_name]
+    config, image_size = get_dpt_config(model_name)
+    # load original state_dict from URL
+    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
+
+    # load HuggingFace model
+    model = DPTForDepthEstimation(config)
+
+    # remove certain keys
+    remove_ignore_keys_(state_dict)
+    # rename keys
+    rename_keys = create_rename_keys(config)
+    for src, dest in rename_keys:
+        rename_key(state_dict, src, dest)
+    # read in qkv matrices
+    read_in_q_k_v(state_dict, config, model)
+
+    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
+    print("Missing keys:", missing_keys)
+    print("Unexpected keys:", unexpected_keys)
+    model.eval()
+
+    # Check outputs on an image
+    processor = DPTImageProcessor(size={"height": image_size, "width": image_size})
+
+    image = prepare_img()
+    processor(image, return_tensors="pt")
+
+    if verify_logits:
+        from torchvision import transforms
+
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        image = Image.open(requests.get(url, stream=True).raw)
+
+        transforms = transforms.Compose(
+            [
+                transforms.Resize((image_size, image_size)),
+                transforms.ToTensor(),
+            ]
+        )
+        pixel_values = transforms(image).unsqueeze(0)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(pixel_values)
+
+        predicted_depth = outputs.predicted_depth
+
+        print("Shape of predicted depth:", predicted_depth.shape)
+        print("First values of predicted depth:", predicted_depth[0, :3, :3])
+
+        # assert logits
+        if model_name == "dpt-swinv2-base-384":
+            # OK, checked
+            expected_shape = torch.Size([1, 384, 384])
+            expected_slice = torch.tensor(
+                [
+                    [1998.5575, 1997.3887, 2009.2981],
+                    [1952.8607, 1979.6488, 2001.0854],
+                    [1953.7697, 1961.7711, 1968.8904],
+                ],
+            )
+        elif model_name == "dpt-swinv2-tiny-256":
+            # OK, checked
+            expected_shape = torch.Size([1, 256, 256])
+            expected_slice = torch.tensor(
+                [[978.9163, 976.5215, 978.5349], [974.1859, 971.7249, 975.8046], [971.3419, 970.3118, 971.6830]],
+            )
+        elif model_name == "dpt-swinv2-large-384":
+            # OK, checked
+            expected_shape = torch.Size([1, 384, 384])
+            expected_slice = torch.tensor(
+                [
+                    [1203.7206, 1200.1495, 1197.8234],
+                    [1196.2484, 1183.5033, 1186.4640],
+                    [1178.8131, 1182.3260, 1174.3975],
+                ],
+            )
+
+        assert predicted_depth.shape == torch.Size(expected_shape)
+        assert torch.allclose(predicted_depth[0, :3, :3], expected_slice)
+        print("Looks ok!")
+
+    if pytorch_dump_folder_path is not None:
+        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+        print(f"Saving model and processor to {pytorch_dump_folder_path}")
+        model.save_pretrained(pytorch_dump_folder_path)
+        processor.save_pretrained(pytorch_dump_folder_path)
+
+    if push_to_hub:
+        print("Pushing model and processor to hub...")
+        model.push_to_hub(repo_id=f"Intel/{model_name}")
+        processor.push_to_hub(repo_id=f"Intel/{model_name}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--model_name",
+        default="dpt-swinv2-base-384",
+        type=str,
+        choices=["dpt-swinv2-tiny-256", "dpt-swinv2-base-384", "dpt-swinv2-large-384"],
+        help="Name of the model you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default=None,
+        type=str,
+        help="Path to the output PyTorch model directory.",
+    )
+    parser.add_argument(
+        "--verify_logits",
+        action="store_true",
+        help="Whether to verify logits after conversion.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Whether to push the model to the hub after conversion.",
+    )
+
+    args = parser.parse_args()
+    convert_dpt_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.verify_logits, args.push_to_hub)

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/convert_dpt_to_pytorch.py

@@ -0,0 +1,286 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert DPT checkpoints from the original repository. URL: https://github.com/isl-org/DPT"""
+
+
+import argparse
+import json
+from pathlib import Path
+
+import requests
+import torch
+from huggingface_hub import cached_download, hf_hub_url
+from PIL import Image
+
+from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+def get_dpt_config(checkpoint_url):
+    config = DPTConfig()
+
+    if "large" in checkpoint_url:
+        config.hidden_size = 1024
+        config.intermediate_size = 4096
+        config.num_hidden_layers = 24
+        config.num_attention_heads = 16
+        config.backbone_out_indices = [5, 11, 17, 23]
+        config.neck_hidden_sizes = [256, 512, 1024, 1024]
+        expected_shape = (1, 384, 384)
+
+    if "ade" in checkpoint_url:
+        config.use_batch_norm_in_fusion_residual = True
+
+        config.num_labels = 150
+        repo_id = "huggingface/label-files"
+        filename = "ade20k-id2label.json"
+        id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
+        id2label = {int(k): v for k, v in id2label.items()}
+        config.id2label = id2label
+        config.label2id = {v: k for k, v in id2label.items()}
+        expected_shape = [1, 150, 480, 480]
+
+    return config, expected_shape
+
+
+def remove_ignore_keys_(state_dict):
+    ignore_keys = ["pretrained.model.head.weight", "pretrained.model.head.bias"]
+    for k in ignore_keys:
+        state_dict.pop(k, None)
+
+
+def rename_key(name):
+    if (
+        "pretrained.model" in name
+        and "cls_token" not in name
+        and "pos_embed" not in name
+        and "patch_embed" not in name
+    ):
+        name = name.replace("pretrained.model", "dpt.encoder")
+    if "pretrained.model" in name:
+        name = name.replace("pretrained.model", "dpt.embeddings")
+    if "patch_embed" in name:
+        name = name.replace("patch_embed", "patch_embeddings")
+    if "pos_embed" in name:
+        name = name.replace("pos_embed", "position_embeddings")
+    if "attn.proj" in name:
+        name = name.replace("attn.proj", "attention.output.dense")
+    if "proj" in name and "project" not in name:
+        name = name.replace("proj", "projection")
+    if "blocks" in name:
+        name = name.replace("blocks", "layer")
+    if "mlp.fc1" in name:
+        name = name.replace("mlp.fc1", "intermediate.dense")
+    if "mlp.fc2" in name:
+        name = name.replace("mlp.fc2", "output.dense")
+    if "norm1" in name:
+        name = name.replace("norm1", "layernorm_before")
+    if "norm2" in name:
+        name = name.replace("norm2", "layernorm_after")
+    if "scratch.output_conv" in name:
+        name = name.replace("scratch.output_conv", "head")
+    if "scratch" in name:
+        name = name.replace("scratch", "neck")
+    if "layer1_rn" in name:
+        name = name.replace("layer1_rn", "convs.0")
+    if "layer2_rn" in name:
+        name = name.replace("layer2_rn", "convs.1")
+    if "layer3_rn" in name:
+        name = name.replace("layer3_rn", "convs.2")
+    if "layer4_rn" in name:
+        name = name.replace("layer4_rn", "convs.3")
+    if "refinenet" in name:
+        layer_idx = int(name[len("neck.refinenet") : len("neck.refinenet") + 1])
+        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
+        name = name.replace(f"refinenet{layer_idx}", f"fusion_stage.layers.{abs(layer_idx-4)}")
+    if "out_conv" in name:
+        name = name.replace("out_conv", "projection")
+    if "resConfUnit1" in name:
+        name = name.replace("resConfUnit1", "residual_layer1")
+    if "resConfUnit2" in name:
+        name = name.replace("resConfUnit2", "residual_layer2")
+    if "conv1" in name:
+        name = name.replace("conv1", "convolution1")
+    if "conv2" in name:
+        name = name.replace("conv2", "convolution2")
+    # readout blocks
+    if "pretrained.act_postprocess1.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess1.0.project.0", "neck.reassemble_stage.readout_projects.0.0")
+    if "pretrained.act_postprocess2.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess2.0.project.0", "neck.reassemble_stage.readout_projects.1.0")
+    if "pretrained.act_postprocess3.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess3.0.project.0", "neck.reassemble_stage.readout_projects.2.0")
+    if "pretrained.act_postprocess4.0.project.0" in name:
+        name = name.replace("pretrained.act_postprocess4.0.project.0", "neck.reassemble_stage.readout_projects.3.0")
+    # resize blocks
+    if "pretrained.act_postprocess1.3" in name:
+        name = name.replace("pretrained.act_postprocess1.3", "neck.reassemble_stage.layers.0.projection")
+    if "pretrained.act_postprocess1.4" in name:
+        name = name.replace("pretrained.act_postprocess1.4", "neck.reassemble_stage.layers.0.resize")
+    if "pretrained.act_postprocess2.3" in name:
+        name = name.replace("pretrained.act_postprocess2.3", "neck.reassemble_stage.layers.1.projection")
+    if "pretrained.act_postprocess2.4" in name:
+        name = name.replace("pretrained.act_postprocess2.4", "neck.reassemble_stage.layers.1.resize")
+    if "pretrained.act_postprocess3.3" in name:
+        name = name.replace("pretrained.act_postprocess3.3", "neck.reassemble_stage.layers.2.projection")
+    if "pretrained.act_postprocess4.3" in name:
+        name = name.replace("pretrained.act_postprocess4.3", "neck.reassemble_stage.layers.3.projection")
+    if "pretrained.act_postprocess4.4" in name:
+        name = name.replace("pretrained.act_postprocess4.4", "neck.reassemble_stage.layers.3.resize")
+    if "pretrained" in name:
+        name = name.replace("pretrained", "dpt")
+    if "bn" in name:
+        name = name.replace("bn", "batch_norm")
+    if "head" in name:
+        name = name.replace("head", "head.head")
+    if "encoder.norm" in name:
+        name = name.replace("encoder.norm", "layernorm")
+    if "auxlayer" in name:
+        name = name.replace("auxlayer", "auxiliary_head.head")
+
+    return name
+
+
+# we split up the matrix of each encoder layer into queries, keys and values
+def read_in_q_k_v(state_dict, config):
+    for i in range(config.num_hidden_layers):
+        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
+        in_proj_weight = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight")
+        in_proj_bias = state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias")
+        # next, add query, keys and values (in that order) to the state dict
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[: config.hidden_size, :]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
+            config.hidden_size : config.hidden_size * 2, :
+        ]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[
+            config.hidden_size : config.hidden_size * 2
+        ]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
+            -config.hidden_size :, :
+        ]
+        state_dict[f"dpt.encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    im = Image.open(requests.get(url, stream=True).raw)
+    return im
+
+
+@torch.no_grad()
+def convert_dpt_checkpoint(checkpoint_url, pytorch_dump_folder_path, push_to_hub, model_name):
+    """
+    Copy/paste/tweak model's weights to our DPT structure.
+    """
+
+    # define DPT configuration based on URL
+    config, expected_shape = get_dpt_config(checkpoint_url)
+    # load original state_dict from URL
+    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
+    # remove certain keys
+    remove_ignore_keys_(state_dict)
+    # rename keys
+    for key in state_dict.copy().keys():
+        val = state_dict.pop(key)
+        state_dict[rename_key(key)] = val
+    # read in qkv matrices
+    read_in_q_k_v(state_dict, config)
+
+    # load HuggingFace model
+    model = DPTForSemanticSegmentation(config) if "ade" in checkpoint_url else DPTForDepthEstimation(config)
+    model.load_state_dict(state_dict)
+    model.eval()
+
+    # Check outputs on an image
+    size = 480 if "ade" in checkpoint_url else 384
+    image_processor = DPTImageProcessor(size=size)
+
+    image = prepare_img()
+    encoding = image_processor(image, return_tensors="pt")
+
+    # forward pass
+    outputs = model(**encoding).logits if "ade" in checkpoint_url else model(**encoding).predicted_depth
+
+    # Assert logits
+    expected_slice = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]])
+    if "ade" in checkpoint_url:
+        expected_slice = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]])
+    assert outputs.shape == torch.Size(expected_shape)
+    assert (
+        torch.allclose(outputs[0, 0, :3, :3], expected_slice, atol=1e-4)
+        if "ade" in checkpoint_url
+        else torch.allclose(outputs[0, :3, :3], expected_slice)
+    )
+    print("Looks ok!")
+
+    if pytorch_dump_folder_path is not None:
+        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+        print(f"Saving model to {pytorch_dump_folder_path}")
+        model.save_pretrained(pytorch_dump_folder_path)
+        print(f"Saving image processor to {pytorch_dump_folder_path}")
+        image_processor.save_pretrained(pytorch_dump_folder_path)
+
+    if push_to_hub:
+        print("Pushing model to hub...")
+        model.push_to_hub(
+            repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
+            organization="nielsr",
+            commit_message="Add model",
+            use_temp_dir=True,
+        )
+        image_processor.push_to_hub(
+            repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
+            organization="nielsr",
+            commit_message="Add image processor",
+            use_temp_dir=True,
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--checkpoint_url",
+        default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
+        type=str,
+        help="URL of the original DPT checkpoint you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default=None,
+        type=str,
+        required=False,
+        help="Path to the output PyTorch model directory.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+    )
+    parser.add_argument(
+        "--model_name",
+        default="dpt-large",
+        type=str,
+        required=False,
+        help="Name of the model, in case you're pushing to the hub.",
+    )
+
+    args = parser.parse_args()
+    convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/feature_extraction_dpt.py

@@ -0,0 +1,33 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for DPT."""
+
+import warnings
+
+from ...utils import logging
+from .image_processing_dpt import DPTImageProcessor
+
+
+logger = logging.get_logger(__name__)
+
+
+class DPTFeatureExtractor(DPTImageProcessor):
+    def __init__(self, *args, **kwargs) -> None:
+        warnings.warn(
+            "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
+            " use DPTImageProcessor instead.",
+            FutureWarning,
+        )
+        super().__init__(*args, **kwargs)

llmeval-env/lib/python3.10/site-packages/transformers/models/dpt/image_processing_dpt.py

@@ -0,0 +1,484 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for DPT."""
+
+import math
+from typing import Dict, Iterable, List, Optional, Tuple, Union
+
+import numpy as np
+
+from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
+from ...image_transforms import pad, resize, to_channel_dimension_format
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_scaled_image,
+    is_torch_available,
+    is_torch_tensor,
+    make_list_of_images,
+    to_numpy_array,
+    valid_images,
+    validate_kwargs,
+    validate_preprocess_arguments,
+)
+from ...utils import TensorType, is_vision_available, logging
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    import PIL
+
+
+logger = logging.get_logger(__name__)
+
+
+def get_resize_output_image_size(
+    input_image: np.ndarray,
+    output_size: Union[int, Iterable[int]],
+    keep_aspect_ratio: bool,
+    multiple: int,
+    input_data_format: Optional[Union[str, ChannelDimension]] = None,
+) -> Tuple[int, int]:
+    def constraint_to_multiple_of(val, multiple, min_val=0, max_val=None):
+        x = round(val / multiple) * multiple
+
+        if max_val is not None and x > max_val:
+            x = math.floor(val / multiple) * multiple
+
+        if x < min_val:
+            x = math.ceil(val / multiple) * multiple
+
+        return x
+
+    output_size = (output_size, output_size) if isinstance(output_size, int) else output_size
+
+    input_height, input_width = get_image_size(input_image, input_data_format)
+    output_height, output_width = output_size
+
+    # determine new height and width
+    scale_height = output_height / input_height
+    scale_width = output_width / input_width
+
+    if keep_aspect_ratio:
+        # scale as little as possible
+        if abs(1 - scale_width) < abs(1 - scale_height):
+            # fit width
+            scale_height = scale_width
+        else:
+            # fit height
+            scale_width = scale_height
+
+    new_height = constraint_to_multiple_of(scale_height * input_height, multiple=multiple)
+    new_width = constraint_to_multiple_of(scale_width * input_width, multiple=multiple)
+
+    return (new_height, new_width)
+
+
+class DPTImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a DPT image processor.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image's (height, width) dimensions. Can be overidden by `do_resize` in `preprocess`.
+        size (`Dict[str, int]` *optional*, defaults to `{"height": 384, "width": 384}`):
+            Size of the image after resizing. Can be overidden by `size` in `preprocess`.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+            Defines the resampling filter to use if resizing the image. Can be overidden by `resample` in `preprocess`.
+        keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
+            If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. Can
+            be overidden by `keep_aspect_ratio` in `preprocess`.
+        ensure_multiple_of (`int`, *optional*, defaults to 1):
+            If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. Can be overidden
+            by `ensure_multiple_of` in `preprocess`.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by the specified scale `rescale_factor`. Can be overidden by `do_rescale` in
+            `preprocess`.
+        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
+            Scale factor to use if rescaling the image. Can be overidden by `rescale_factor` in `preprocess`.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
+            method.
+        image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
+            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
+            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
+        image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
+            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
+            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
+        do_pad (`bool`, *optional*, defaults to `False`):
+            Whether to apply center padding. This was introduced in the DINOv2 paper, which uses the model in
+            combination with DPT.
+        size_divisor (`int`, *optional*):
+            If `do_pad` is `True`, pads the image dimensions to be divisible by this value. This was introduced in the
+            DINOv2 paper, which uses the model in combination with DPT.
+    """
+
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: Dict[str, int] = None,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        keep_aspect_ratio: bool = False,
+        ensure_multiple_of: int = 1,
+        do_rescale: bool = True,
+        rescale_factor: Union[int, float] = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_pad: bool = False,
+        size_divisor: int = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        size = size if size is not None else {"height": 384, "width": 384}
+        size = get_size_dict(size)
+        self.do_resize = do_resize
+        self.size = size
+        self.keep_aspect_ratio = keep_aspect_ratio
+        self.ensure_multiple_of = ensure_multiple_of
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+        self.do_pad = do_pad
+        self.size_divisor = size_divisor
+        self._valid_processor_keys = [
+            "images",
+            "do_resize",
+            "size",
+            "keep_aspect_ratio",
+            "ensure_multiple_of",
+            "resample",
+            "do_rescale",
+            "rescale_factor",
+            "do_normalize",
+            "image_mean",
+            "image_std",
+            "do_pad",
+            "size_divisor",
+            "return_tensors",
+            "data_format",
+            "input_data_format",
+        ]
+
+    def resize(
+        self,
+        image: np.ndarray,
+        size: Dict[str, int],
+        keep_aspect_ratio: bool = False,
+        ensure_multiple_of: int = 1,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Resize an image to target size `(size["height"], size["width"])`. If `keep_aspect_ratio` is `True`, the image
+        is resized to the largest possible size such that the aspect ratio is preserved. If `ensure_multiple_of` is
+        set, the image is resized to a size that is a multiple of this value.
+
+        Args:
+            image (`np.ndarray`):
+                Image to resize.
+            size (`Dict[str, int]`):
+                Target size of the output image.
+            keep_aspect_ratio (`bool`, *optional*, defaults to `False`):
+                If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved.
+            ensure_multiple_of (`int`, *optional*, defaults to 1):
+                The image is resized to a size that is a multiple of this value.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+                Defines the resampling filter to use if resizing the image. Otherwise, the image is resized to size
+                specified in `size`.
+            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
+                Resampling filter to use when resiizing the image.
+            data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format of the image. If not provided, it will be the same as the input image.
+            input_data_format (`str` or `ChannelDimension`, *optional*):
+                The channel dimension format of the input image. If not provided, it will be inferred.
+        """
+        size = get_size_dict(size)
+        if "height" not in size or "width" not in size:
+            raise ValueError(f"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}")
+
+        output_size = get_resize_output_image_size(
+            image,
+            output_size=(size["height"], size["width"]),
+            keep_aspect_ratio=keep_aspect_ratio,
+            multiple=ensure_multiple_of,
+            input_data_format=input_data_format,
+        )
+        return resize(
+            image,
+            size=output_size,
+            resample=resample,
+            data_format=data_format,
+            input_data_format=input_data_format,
+            **kwargs,
+        )
+
+    def pad_image(
+        self,
+        image: np.array,
+        size_divisor: int,
+        data_format: Optional[Union[str, ChannelDimension]] = None,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+    ):
+        """
+        Center pad an image to be a multiple of `multiple`.
+
+        Args:
+            image (`np.ndarray`):
+                Image to pad.
+            size_divisor (`int`):
+                The width and height of the image will be padded to a multiple of this number.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+
+        def _get_pad(size, size_divisor):
+            new_size = math.ceil(size / size_divisor) * size_divisor
+            pad_size = new_size - size
+            pad_size_left = pad_size // 2
+            pad_size_right = pad_size - pad_size_left
+            return pad_size_left, pad_size_right
+
+        if input_data_format is None:
+            input_data_format = infer_channel_dimension_format(image)
+
+        height, width = get_image_size(image, input_data_format)
+
+        pad_size_left, pad_size_right = _get_pad(height, size_divisor)
+        pad_size_top, pad_size_bottom = _get_pad(width, size_divisor)
+
+        return pad(image, ((pad_size_left, pad_size_right), (pad_size_top, pad_size_bottom)), data_format=data_format)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: bool = None,
+        size: int = None,
+        keep_aspect_ratio: bool = None,
+        ensure_multiple_of: int = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool = None,
+        rescale_factor: float = None,
+        do_normalize: bool = None,
+        image_mean: Optional[Union[float, List[float]]] = None,
+        image_std: Optional[Union[float, List[float]]] = None,
+        do_pad: bool = None,
+        size_divisor: int = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        data_format: ChannelDimension = ChannelDimension.FIRST,
+        input_data_format: Optional[Union[str, ChannelDimension]] = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after reszing. If `keep_aspect_ratio` is `True`, the image is resized to the largest
+                possible size such that the aspect ratio is preserved. If `ensure_multiple_of` is set, the image is
+                resized to a size that is a multiple of this value.
+            keep_aspect_ratio (`bool`, *optional*, defaults to `self.keep_aspect_ratio`):
+                Whether to keep the aspect ratio of the image. If False, the image will be resized to (size, size). If
+                True, the image will be resized to keep the aspect ratio and the size will be the maximum possible.
+            ensure_multiple_of (`int`, *optional*, defaults to `self.ensure_multiple_of`):
+                Ensure that the image size is a multiple of this value.
+            resample (`int`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
+                has an effect if `do_resize` is set to `True`.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image values between [0 - 1].
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean.
+            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                    - Unset: Return a list of `np.ndarray`.
+                    - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                    - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. If unset, the channel dimension format is inferred
+                from the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        size = size if size is not None else self.size
+        size = get_size_dict(size)
+        keep_aspect_ratio = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
+        ensure_multiple_of = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        do_pad = do_pad if do_pad is not None else self.do_pad
+        size_divisor = size_divisor if size_divisor is not None else self.size_divisor
+
+        images = make_list_of_images(images)
+
+        validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys)
+
+        if not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+        validate_preprocess_arguments(
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_pad=do_pad,
+            size_divisibility=size_divisor,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+        # All transformations expect numpy arrays.
+        images = [to_numpy_array(image) for image in images]
+
+        if is_scaled_image(images[0]) and do_rescale:
+            logger.warning_once(
+                "It looks like you are trying to rescale already rescaled images. If the input"
+                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+            )
+
+        if input_data_format is None:
+            # We assume that all images have the same channel dimension format.
+            input_data_format = infer_channel_dimension_format(images[0])
+
+        if do_resize:
+            images = [
+                self.resize(
+                    image=image,
+                    size=size,
+                    resample=resample,
+                    keep_aspect_ratio=keep_aspect_ratio,
+                    ensure_multiple_of=ensure_multiple_of,
+                    input_data_format=input_data_format,
+                )
+                for image in images
+            ]
+
+        if do_rescale:
+            images = [
+                self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
+                for image in images
+            ]
+
+        if do_normalize:
+            images = [
+                self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
+                for image in images
+            ]
+
+        if do_pad:
+            images = [
+                self.pad_image(image=image, size_divisor=size_divisor, input_data_format=input_data_format)
+                for image in images
+            ]
+
+        images = [
+            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
+        ]
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->DPT
+    def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
+        """
+        Converts the output of [`DPTForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
+
+        Args:
+            outputs ([`DPTForSemanticSegmentation`]):
+                Raw outputs of the model.
+            target_sizes (`List[Tuple]` of length `batch_size`, *optional*):
+                List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
+                predictions will not be resized.
+
+        Returns:
+            semantic_segmentation: `List[torch.Tensor]` of length `batch_size`, where each item is a semantic
+            segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
+            specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
+        """
+        # TODO: add support for other frameworks
+        logits = outputs.logits
+
+        # Resize logits and compute semantic segmentation maps
+        if target_sizes is not None:
+            if len(logits) != len(target_sizes):
+                raise ValueError(
+                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
+                )
+
+            if is_torch_tensor(target_sizes):
+                target_sizes = target_sizes.numpy()
+
+            semantic_segmentation = []
+
+            for idx in range(len(logits)):
+                resized_logits = torch.nn.functional.interpolate(
+                    logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
+                )
+                semantic_map = resized_logits[0].argmax(dim=0)
+                semantic_segmentation.append(semantic_map)
+        else:
+            semantic_segmentation = logits.argmax(dim=1)
+            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
+
+        return semantic_segmentation