Add files using upload-large-folder tool

.gitattributes

@@ -328,3 +328,4 @@ llava_next/lib/python3.10/site-packages/pandas/io/__pycache__/pytables.cpython-3
 llava_next/lib/python3.10/site-packages/torchvision/_C.so filter=lfs diff=lfs merge=lfs -text
 parrot/lib/python3.10/site-packages/transformers/models/perceiver/__pycache__/modeling_perceiver.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
 parrot/lib/python3.10/site-packages/decord/libdecord.so filter=lfs diff=lfs merge=lfs -text
+llava_next/lib/python3.10/site-packages/pandas/tests/indexing/__pycache__/test_loc.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text

llava_next/lib/python3.10/site-packages/anyio-4.6.2.post1.dist-info/INSTALLER

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

llava_next/lib/python3.10/site-packages/anyio-4.6.2.post1.dist-info/LICENSE

@@ -0,0 +1,20 @@
+The MIT License (MIT)
+
+Copyright (c) 2018 Alex Grönholm
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

llava_next/lib/python3.10/site-packages/anyio-4.6.2.post1.dist-info/METADATA

@@ -0,0 +1,105 @@
+Metadata-Version: 2.1
+Name: anyio
+Version: 4.6.2.post1
+Summary: High level compatibility layer for multiple asynchronous event loop implementations
+Author-email: Alex Grönholm <alex.gronholm@nextday.fi>
+License: MIT
+Project-URL: Documentation, https://anyio.readthedocs.io/en/latest/
+Project-URL: Changelog, https://anyio.readthedocs.io/en/stable/versionhistory.html
+Project-URL: Source code, https://github.com/agronholm/anyio
+Project-URL: Issue tracker, https://github.com/agronholm/anyio/issues
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Framework :: AnyIO
+Classifier: Typing :: Typed
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Requires-Python: >=3.9
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: idna >=2.8
+Requires-Dist: sniffio >=1.1
+Requires-Dist: exceptiongroup >=1.0.2 ; python_version < "3.11"
+Requires-Dist: typing-extensions >=4.1 ; python_version < "3.11"
+Provides-Extra: doc
+Requires-Dist: packaging ; extra == 'doc'
+Requires-Dist: Sphinx ~=7.4 ; extra == 'doc'
+Requires-Dist: sphinx-rtd-theme ; extra == 'doc'
+Requires-Dist: sphinx-autodoc-typehints >=1.2.0 ; extra == 'doc'
+Provides-Extra: test
+Requires-Dist: anyio[trio] ; extra == 'test'
+Requires-Dist: coverage[toml] >=7 ; extra == 'test'
+Requires-Dist: exceptiongroup >=1.2.0 ; extra == 'test'
+Requires-Dist: hypothesis >=4.0 ; extra == 'test'
+Requires-Dist: psutil >=5.9 ; extra == 'test'
+Requires-Dist: pytest >=7.0 ; extra == 'test'
+Requires-Dist: pytest-mock >=3.6.1 ; extra == 'test'
+Requires-Dist: trustme ; extra == 'test'
+Requires-Dist: uvloop >=0.21.0b1 ; (platform_python_implementation == "CPython" and platform_system != "Windows") and extra == 'test'
+Requires-Dist: truststore >=0.9.1 ; (python_version >= "3.10") and extra == 'test'
+Provides-Extra: trio
+Requires-Dist: trio >=0.26.1 ; extra == 'trio'
+
+.. image:: https://github.com/agronholm/anyio/actions/workflows/test.yml/badge.svg
+  :target: https://github.com/agronholm/anyio/actions/workflows/test.yml
+  :alt: Build Status
+.. image:: https://coveralls.io/repos/github/agronholm/anyio/badge.svg?branch=master
+  :target: https://coveralls.io/github/agronholm/anyio?branch=master
+  :alt: Code Coverage
+.. image:: https://readthedocs.org/projects/anyio/badge/?version=latest
+  :target: https://anyio.readthedocs.io/en/latest/?badge=latest
+  :alt: Documentation
+.. image:: https://badges.gitter.im/gitterHQ/gitter.svg
+  :target: https://gitter.im/python-trio/AnyIO
+  :alt: Gitter chat
+
+AnyIO is an asynchronous networking and concurrency library that works on top of either asyncio_ or
+trio_. It implements trio-like `structured concurrency`_ (SC) on top of asyncio and works in harmony
+with the native SC of trio itself.
+
+Applications and libraries written against AnyIO's API will run unmodified on either asyncio_ or
+trio_. AnyIO can also be adopted into a library or application incrementally – bit by bit, no full
+refactoring necessary. It will blend in with the native libraries of your chosen backend.
+
+Documentation
+-------------
+
+View full documentation at: https://anyio.readthedocs.io/
+
+Features
+--------
+
+AnyIO offers the following functionality:
+
+* Task groups (nurseries_ in trio terminology)
+* High-level networking (TCP, UDP and UNIX sockets)
+
+  * `Happy eyeballs`_ algorithm for TCP connections (more robust than that of asyncio on Python
+    3.8)
+  * async/await style UDP sockets (unlike asyncio where you still have to use Transports and
+    Protocols)
+
+* A versatile API for byte streams and object streams
+* Inter-task synchronization and communication (locks, conditions, events, semaphores, object
+  streams)
+* Worker threads
+* Subprocesses
+* Asynchronous file I/O (using worker threads)
+* Signal handling
+
+AnyIO also comes with its own pytest_ plugin which also supports asynchronous fixtures.
+It even works with the popular Hypothesis_ library.
+
+.. _asyncio: https://docs.python.org/3/library/asyncio.html
+.. _trio: https://github.com/python-trio/trio
+.. _structured concurrency: https://en.wikipedia.org/wiki/Structured_concurrency
+.. _nurseries: https://trio.readthedocs.io/en/stable/reference-core.html#nurseries-and-spawning
+.. _Happy eyeballs: https://en.wikipedia.org/wiki/Happy_Eyeballs
+.. _pytest: https://docs.pytest.org/en/latest/
+.. _Hypothesis: https://hypothesis.works/

llava_next/lib/python3.10/site-packages/anyio-4.6.2.post1.dist-info/RECORD

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llava_next/lib/python3.10/site-packages/anyio-4.6.2.post1.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (75.1.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

llava_next/lib/python3.10/site-packages/anyio-4.6.2.post1.dist-info/entry_points.txt

@@ -0,0 +1,2 @@
+[pytest11]
+anyio = anyio.pytest_plugin

llava_next/lib/python3.10/site-packages/anyio-4.6.2.post1.dist-info/top_level.txt

@@ -0,0 +1 @@
+anyio

llava_next/lib/python3.10/site-packages/bitsandbytes/__init__.py

@@ -0,0 +1,24 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from . import research, utils
+from .autograd._functions import (
+    MatmulLtState,
+    bmm_cublas,
+    matmul,
+    matmul_4bit,
+    matmul_cublas,
+    mm_cublas,
+)
+from .nn import modules
+from .optim import adam
+
+__pdoc__ = {
+    "libbitsandbytes": False,
+    "optim.optimizer.Optimizer8bit": False,
+    "optim.optimizer.MockArgs": False,
+}
+
+__version__ = "0.44.1"

llava_next/lib/python3.10/site-packages/bitsandbytes/__main__.py

@@ -0,0 +1,4 @@
+if __name__ == "__main__":
+    from bitsandbytes.diagnostics.main import main
+
+    main()

llava_next/lib/python3.10/site-packages/bitsandbytes/consts.py

@@ -0,0 +1,12 @@
+from pathlib import Path
+import platform
+
+DYNAMIC_LIBRARY_SUFFIX = {
+    "Darwin": ".dylib",
+    "Linux": ".so",
+    "Windows": ".dll",
+}.get(platform.system(), ".so")
+
+PACKAGE_DIR = Path(__file__).parent
+PACKAGE_GITHUB_URL = "https://github.com/TimDettmers/bitsandbytes"
+NONPYTORCH_DOC_URL = "https://github.com/TimDettmers/bitsandbytes/blob/main/docs/source/nonpytorchcuda.mdx"

llava_next/lib/python3.10/site-packages/bitsandbytes/cuda_specs.py

@@ -0,0 +1,41 @@
+import dataclasses
+from typing import List, Optional, Tuple
+
+import torch
+
+
+@dataclasses.dataclass(frozen=True)
+class CUDASpecs:
+    highest_compute_capability: Tuple[int, int]
+    cuda_version_string: str
+    cuda_version_tuple: Tuple[int, int]
+
+    @property
+    def has_cublaslt(self) -> bool:
+        return self.highest_compute_capability >= (7, 5)
+
+
+def get_compute_capabilities() -> List[Tuple[int, int]]:
+    return sorted(torch.cuda.get_device_capability(torch.cuda.device(i)) for i in range(torch.cuda.device_count()))
+
+
+def get_cuda_version_tuple() -> Tuple[int, int]:
+    # https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
+    major, minor = map(int, torch.version.cuda.split("."))
+    return major, minor
+
+
+def get_cuda_version_string() -> str:
+    major, minor = get_cuda_version_tuple()
+    return f"{major}{minor}"
+
+
+def get_cuda_specs() -> Optional[CUDASpecs]:
+    if not torch.cuda.is_available():
+        return None
+
+    return CUDASpecs(
+        highest_compute_capability=(get_compute_capabilities()[-1]),
+        cuda_version_string=(get_cuda_version_string()),
+        cuda_version_tuple=get_cuda_version_tuple(),
+    )

llava_next/lib/python3.10/site-packages/gitdb-4.0.11.dist-info/AUTHORS

@@ -0,0 +1,4 @@
+Creator: Sebastian Thiel
+
+Contributors:
+  - Ram Rachum (@cool-RR)

llava_next/lib/python3.10/site-packages/gitdb-4.0.11.dist-info/INSTALLER

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

llava_next/lib/python3.10/site-packages/gitdb-4.0.11.dist-info/LICENSE

@@ -0,0 +1,42 @@
+Copyright (C) 2010, 2011 Sebastian Thiel and contributors
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without 
+modification, are permitted provided that the following conditions 
+are met:
+
+* Redistributions of source code must retain the above copyright 
+notice, this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright 
+notice, this list of conditions and the following disclaimer in the 
+documentation and/or other materials provided with the distribution.
+
+* Neither the name of the GitDB project nor the names of 
+its contributors may be used to endorse or promote products derived 
+from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+Additional Licenses
+-------------------
+The files at 
+gitdb/test/fixtures/packs/pack-11fdfa9e156ab73caae3b6da867192221f2089c2.idx
+and 
+gitdb/test/fixtures/packs/pack-11fdfa9e156ab73caae3b6da867192221f2089c2.pack 
+are licensed under GNU GPL as part of the git source repository, 
+see http://en.wikipedia.org/wiki/Git_%28software%29 for more information.
+
+They are not required for the actual operation, which is why they are not found 
+in the distribution package.

llava_next/lib/python3.10/site-packages/gitdb-4.0.11.dist-info/METADATA

@@ -0,0 +1,31 @@
+Metadata-Version: 2.1
+Name: gitdb
+Version: 4.0.11
+Summary: Git Object Database
+Home-page: https://github.com/gitpython-developers/gitdb
+Author: Sebastian Thiel
+Author-email: byronimo@gmail.com
+License: BSD License
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Console
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.7
+License-File: LICENSE
+License-File: AUTHORS
+Requires-Dist: smmap <6,>=3.0.1
+
+GitDB is a pure-Python git object database

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llava_next/lib/python3.10/site-packages/httpcore-0.17.3.dist-info/INSTALLER

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+Metadata-Version: 2.1
+Name: httpcore
+Version: 0.17.3
+Summary: A minimal low-level HTTP client.
+Home-page: https://github.com/encode/httpcore
+Author: Tom Christie
+Author-email: tom@tomchristie.com
+License: BSD
+Project-URL: Documentation, https://www.encode.io/httpcore
+Project-URL: Source, https://github.com/encode/httpcore
+Classifier: Development Status :: 3 - Alpha
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: Internet :: WWW/HTTP
+Classifier: Framework :: AsyncIO
+Classifier: Framework :: Trio
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+License-File: LICENSE.md
+Requires-Dist: h11 (<0.15,>=0.13)
+Requires-Dist: sniffio (==1.*)
+Requires-Dist: anyio (<5.0,>=3.0)
+Requires-Dist: certifi
+Provides-Extra: http2
+Requires-Dist: h2 (<5,>=3) ; extra == 'http2'
+Provides-Extra: socks
+Requires-Dist: socksio (==1.*) ; extra == 'socks'
+
+# HTTP Core
+
+[![Test Suite](https://github.com/encode/httpcore/workflows/Test%20Suite/badge.svg)](https://github.com/encode/httpcore/actions)
+[![Package version](https://badge.fury.io/py/httpcore.svg)](https://pypi.org/project/httpcore/)
+
+> *Do one thing, and do it well.*
+
+The HTTP Core package provides a minimal low-level HTTP client, which does
+one thing only. Sending HTTP requests.
+
+It does not provide any high level model abstractions over the API,
+does not handle redirects, multipart uploads, building authentication headers,
+transparent HTTP caching, URL parsing, session cookie handling,
+content or charset decoding, handling JSON, environment based configuration
+defaults, or any of that Jazz.
+
+Some things HTTP Core does do:
+
+* Sending HTTP requests.
+* Thread-safe / task-safe connection pooling.
+* HTTP(S) proxy & SOCKS proxy support.
+* Supports HTTP/1.1 and HTTP/2.
+* Provides both sync and async interfaces.
+* Async backend support for `asyncio` and `trio`.
+
+## Requirements
+
+Python 3.7+
+
+## Installation
+
+For HTTP/1.1 only support, install with:
+
+```shell
+$ pip install httpcore
+```
+
+For HTTP/1.1 and HTTP/2 support, install with:
+
+```shell
+$ pip install httpcore[http2]
+```
+
+For SOCKS proxy support, install with:
+
+```shell
+$ pip install httpcore[socks]
+```
+
+# Sending requests
+
+Send an HTTP request:
+
+```python
+import httpcore
+
+response = httpcore.request("GET", "https://www.example.com/")
+
+print(response)
+# <Response [200]>
+print(response.status)
+# 200
+print(response.headers)
+# [(b'Accept-Ranges', b'bytes'), (b'Age', b'557328'), (b'Cache-Control', b'max-age=604800'), ...]
+print(response.content)
+# b'<!doctype html>\n<html>\n<head>\n<title>Example Domain</title>\n\n<meta charset="utf-8"/>\n ...'
+```
+
+The top-level `httpcore.request()` function is provided for convenience. In practice whenever you're working with `httpcore` you'll want to use the connection pooling functionality that it provides.
+
+```python
+import httpcore
+
+http = httpcore.ConnectionPool()
+response = http.request("GET", "https://www.example.com/")
+```
+
+Once you're ready to get going, [head over to the documentation](https://www.encode.io/httpcore/).
+
+## Motivation
+
+You *probably* don't want to be using HTTP Core directly. It might make sense if
+you're writing something like a proxy service in Python, and you just want
+something at the lowest possible level, but more typically you'll want to use
+a higher level client library, such as `httpx`.
+
+The motivation for `httpcore` is:
+
+* To provide a reusable low-level client library, that other packages can then build on top of.
+* To provide a *really clear interface split* between the networking code and client logic,
+  so that each is easier to understand and reason about in isolation.
+
+
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
+
+## 0.17.3 (5th July 2023)
+
+- Support async cancellations, ensuring that the connection pool is left in a clean state when cancellations occur. (#726)
+- The networking backend interface has [been added to the public API](https://www.encode.io/httpcore/network-backends). Some classes which were previously private implementation detail are now part of the top-level public API. (#699)
+- Graceful handling of HTTP/2 GoAway frames, with requests being transparently retried on a new connection. (#730)
+- Add exceptions when a synchronous `trace callback` is passed to an asynchronous request or an asynchronous `trace callback` is passed to a synchronous request. (#717)
+
+## 0.17.2 (May 23th, 2023)
+
+- Add `socket_options` argument to `ConnectionPool` and `HTTProxy` classes. (#668)
+- Improve logging with per-module logger names. (#690)
+- Add `sni_hostname` request extension. (#696)
+- Resolve race condition during import of `anyio` package. (#692)
+- Enable TCP_NODELAY for all synchronous sockets. (#651)
+
+## 0.17.1 (May 17th, 2023)
+
+- If 'retries' is set, then allow retries if an SSL handshake error occurs. (#669)
+- Improve correctness of tracebacks on network exceptions, by raising properly chained exceptions. (#678)
+- Prevent connection-hanging behaviour when HTTP/2 connections are closed by a server-sent 'GoAway' frame. (#679)
+- Fix edge-case exception when removing requests from the connection pool. (#680)
+- Fix pool timeout edge-case. (#688)
+
+## 0.17.0 (March 16th, 2023)
+
+- Add DEBUG level logging. (#648)
+- Respect HTTP/2 max concurrent streams when settings updates are sent by server. (#652)
+- Increase the allowable HTTP header size to 100kB. (#647)
+- Add `retries` option to SOCKS proxy classes. (#643)
+
+## 0.16.3 (December 20th, 2022)
+
+- Allow `ws` and `wss` schemes. Allows us to properly support websocket upgrade connections. (#625)
+- Forwarding HTTP proxies use a connection-per-remote-host. Required by some proxy implementations. (#637)
+- Don't raise `RuntimeError` when closing a connection pool with active connections. Removes some error cases when cancellations are used. (#631)
+- Lazy import `anyio`, so that it's no longer a hard dependancy, and isn't imported if unused. (#639)
+
+## 0.16.2 (November 25th, 2022)
+
+- Revert 'Fix async cancellation behaviour', which introduced race conditions. (#627)
+- Raise `RuntimeError` if attempting to us UNIX domain sockets on Windows. (#619)
+
+## 0.16.1 (November 17th, 2022)
+
+- Fix HTTP/1.1 interim informational responses, such as "100 Continue". (#605)
+
+## 0.16.0 (October 11th, 2022)
+
+- Support HTTP/1.1 informational responses. (#581)
+- Fix async cancellation behaviour. (#580)
+- Support `h11` 0.14. (#579)
+
+## 0.15.0 (May 17th, 2022)
+
+- Drop Python 3.6 support (#535)
+- Ensure HTTP proxy CONNECT requests include `timeout` configuration. (#506)
+- Switch to explicit `typing.Optional` for type hints. (#513)
+- For `trio` map OSError exceptions to `ConnectError`. (#543)
+
+## 0.14.7 (February 4th, 2022)
+
+- Requests which raise a PoolTimeout need to be removed from the pool queue. (#502)
+- Fix AttributeError that happened when Socks5Connection were terminated. (#501)
+
+## 0.14.6 (February 1st, 2022)
+
+- Fix SOCKS support for `http://` URLs. (#492)
+- Resolve race condition around exceptions during streaming a response. (#491)
+
+## 0.14.5 (January 18th, 2022)
+
+- SOCKS proxy support. (#478)
+- Add proxy_auth argument to HTTPProxy. (#481)
+- Improve error message on 'RemoteProtocolError' exception when server disconnects without sending a response. (#479)
+
+## 0.14.4 (January 5th, 2022)
+
+- Support HTTP/2 on HTTPS tunnelling proxies. (#468)
+- Fix proxy headers missing on HTTP forwarding. (#456)
+- Only instantiate SSL context if required. (#457)
+- More robust HTTP/2 handling. (#253, #439, #440, #441)
+
+## 0.14.3 (November 17th, 2021)
+
+- Fix race condition when removing closed connections from the pool. (#437)
+
+## 0.14.2 (November 16th, 2021)
+
+- Failed connections no longer remain in the pool. (Pull #433)
+
+## 0.14.1 (November 12th, 2021)
+
+- `max_connections` becomes optional. (Pull #429)
+- `certifi` is now included in the install dependancies. (Pull #428)
+- `h2` is now strictly optional. (Pull #428)
+
+## 0.14.0 (November 11th, 2021)
+
+The 0.14 release is a complete reworking of `httpcore`, comprehensively addressing some underlying issues in the connection pooling, as well as substantially redesigning the API to be more user friendly.
+
+Some of the lower-level API design also makes the components more easily testable in isolation, and the package now has 100% test coverage.
+
+See [discussion #419](https://github.com/encode/httpcore/discussions/419) for a little more background.
+
+There's some other neat bits in there too, such as the "trace" extension, which gives a hook into inspecting the internal events that occur during the request/response cycle. This extension is needed for the HTTPX cli, in order to...
+
+* Log the point at which the connection is established, and the IP/port on which it is made.
+* Determine if the outgoing request should log as HTTP/1.1 or HTTP/2, rather than having to assume it's HTTP/2 if the --http2 flag was passed. (Which may not actually be true.)
+* Log SSL version info / certificate info.
+
+Note that `curio` support is not currently available in 0.14.0. If you're using `httpcore` with `curio` please get in touch, so we can assess if we ought to prioritize it as a feature or not.
+
+## 0.13.7 (September 13th, 2021)
+
+- Fix broken error messaging when URL scheme is missing, or a non HTTP(S) scheme is used. (Pull #403)
+
+## 0.13.6 (June 15th, 2021)
+
+### Fixed
+
+- Close sockets when read or write timeouts occur. (Pull #365)
+
+## 0.13.5 (June 14th, 2021)
+
+### Fixed
+
+- Resolved niggles with AnyIO EOF behaviours. (Pull #358, #362)
+
+## 0.13.4 (June 9th, 2021)
+
+### Added
+
+- Improved error messaging when URL scheme is missing, or a non HTTP(S) scheme is used. (Pull #354)
+
+### Fixed
+
+- Switched to `anyio` as the default backend implementation when running with `asyncio`. Resolves some awkward [TLS timeout issues](https://github.com/encode/httpx/discussions/1511).
+
+## 0.13.3 (May 6th, 2021)
+
+### Added
+
+- Support HTTP/2 prior knowledge, using `httpcore.SyncConnectionPool(http1=False)`. (Pull #333)
+
+### Fixed
+
+- Handle cases where environment does not provide `select.poll` support. (Pull #331)
+
+## 0.13.2 (April 29th, 2021)
+
+### Added
+
+- Improve error message for specific case of `RemoteProtocolError` where server disconnects without sending a response. (Pull #313)
+
+## 0.13.1 (April 28th, 2021)
+
+### Fixed
+
+- More resiliant testing for closed connections. (Pull #311)
+- Don't raise exceptions on ungraceful connection closes. (Pull #310)
+
+## 0.13.0 (April 21st, 2021)
+
+The 0.13 release updates the core API in order to match the HTTPX Transport API,
+introduced in HTTPX 0.18 onwards.
+
+An example of making requests with the new interface is:
+
+```python
+with httpcore.SyncConnectionPool() as http:
+    status_code, headers, stream, extensions = http.handle_request(
+        method=b'GET',
+        url=(b'https', b'example.org', 443, b'/'),
+        headers=[(b'host', b'example.org'), (b'user-agent', b'httpcore')]
+        stream=httpcore.ByteStream(b''),
+        extensions={}
+    )
+    body = stream.read()
+    print(status_code, body)
+```
+
+### Changed
+
+- The `.request()` method is now `handle_request()`. (Pull #296)
+- The `.arequest()` method is now `.handle_async_request()`. (Pull #296)
+- The `headers` argument is no longer optional. (Pull #296)
+- The `stream` argument is no longer optional. (Pull #296)
+- The `ext` argument is now named `extensions`, and is no longer optional. (Pull #296)
+- The `"reason"` extension keyword is now named `"reason_phrase"`. (Pull #296)
+- The `"reason_phrase"` and `"http_version"` extensions now use byte strings for their values. (Pull #296)
+- The `httpcore.PlainByteStream()` class becomes `httpcore.ByteStream()`. (Pull #296)
+
+### Added
+
+- Streams now support a `.read()` interface. (Pull #296)
+
+### Fixed
+
+- Task cancellation no longer leaks connections from the connection pool. (Pull #305)
+
+## 0.12.3 (December 7th, 2020)
+
+### Fixed
+
+- Abort SSL connections on close rather than waiting for remote EOF when using `asyncio`.  (Pull #167)
+- Fix exception raised in case of connect timeouts when using the `anyio` backend. (Pull #236)
+- Fix `Host` header precedence for `:authority` in HTTP/2. (Pull #241, #243)
+- Handle extra edge case when detecting for socket readability when using `asyncio`. (Pull #242, #244)
+- Fix `asyncio` SSL warning when using proxy tunneling. (Pull #249)
+
+## 0.12.2 (November 20th, 2020)
+
+### Fixed
+
+- Properly wrap connect errors on the asyncio backend. (Pull #235)
+- Fix `ImportError` occurring on Python 3.9 when using the HTTP/1.1 sync client in a multithreaded context. (Pull #237)
+
+## 0.12.1 (November 7th, 2020)
+
+### Added
+
+- Add connect retries. (Pull #221)
+
+### Fixed
+
+- Tweak detection of dropped connections, resolving an issue with open files limits on Linux. (Pull #185)
+- Avoid leaking connections when establishing an HTTP tunnel to a proxy has failed. (Pull #223)
+- Properly wrap OS errors when using `trio`. (Pull #225)
+
+## 0.12.0 (October 6th, 2020)
+
+### Changed
+
+- HTTP header casing is now preserved, rather than always sent in lowercase. (#216 and python-hyper/h11#104)
+
+### Added
+
+- Add Python 3.9 to officially supported versions.
+
+### Fixed
+
+- Gracefully handle a stdlib asyncio bug when a connection is closed while it is in a paused-for-reading state. (#201)
+
+## 0.11.1 (September 28nd, 2020)
+
+### Fixed
+
+- Add await to async semaphore release() coroutine (#197)
+- Drop incorrect curio classifier (#192)
+
+## 0.11.0 (September 22nd, 2020)
+
+The Transport API with 0.11.0 has a couple of significant changes.
+
+Firstly we've moved changed the request interface in order to allow extensions, which will later enable us to support features
+such as trailing headers, HTTP/2 server push, and CONNECT/Upgrade connections.
+
+The interface changes from:
+
+```python
+def request(method, url, headers, stream, timeout):
+    return (http_version, status_code, reason, headers, stream)
+```
+
+To instead including an optional dictionary of extensions on the request and response:
+
+```python
+def request(method, url, headers, stream, ext):
+    return (status_code, headers, stream, ext)
+```
+
+Having an open-ended extensions point will allow us to add later support for various optional features, that wouldn't otherwise be supported without these API changes.
+
+In particular:
+
+* Trailing headers support.
+* HTTP/2 Server Push
+* sendfile.
+* Exposing raw connection on CONNECT, Upgrade, HTTP/2 bi-di streaming.
+* Exposing debug information out of the API, including template name, template context.
+
+Currently extensions are limited to:
+
+* request: `timeout` - Optional. Timeout dictionary.
+* response: `http_version` - Optional. Include the HTTP version used on the response.
+* response: `reason` - Optional. Include the reason phrase used on the response. Only valid with HTTP/1.*.
+
+See https://github.com/encode/httpx/issues/1274#issuecomment-694884553 for the history behind this.
+
+Secondly, the async version of `request` is now namespaced as `arequest`.
+
+This allows concrete transports to support both sync and async implementations on the same class.
+
+### Added
+
+- Add curio support. (Pull #168)
+- Add anyio support, with `backend="anyio"`. (Pull #169)
+
+### Changed
+
+- Update the Transport API to use 'ext' for optional extensions. (Pull #190)
+- Update the Transport API to use `.request` and `.arequest` so implementations can support both sync and async. (Pull #189)
+
+## 0.10.2 (August 20th, 2020)
+
+### Added
+
+- Added Unix Domain Socket support. (Pull #139)
+
+### Fixed
+
+- Always include the port on proxy CONNECT requests. (Pull #154)
+- Fix `max_keepalive_connections` configuration. (Pull #153)
+- Fixes behaviour in HTTP/1.1 where server disconnects can be used to signal the end of the response body. (Pull #164)
+
+## 0.10.1 (August 7th, 2020)
+
+- Include `max_keepalive_connections` on `AsyncHTTPProxy`/`SyncHTTPProxy` classes.
+
+## 0.10.0 (August 7th, 2020)
+
+The most notable change in the 0.10.0 release is that HTTP/2 support is now fully optional.
+
+Use either `pip install httpcore` for HTTP/1.1 support only, or `pip install httpcore[http2]` for HTTP/1.1 and HTTP/2 support.
+
+### Added
+
+- HTTP/2 support becomes optional. (Pull #121, #130)
+- Add `local_address=...` support. (Pull #100, #134)
+- Add `PlainByteStream`, `IteratorByteStream`, `AsyncIteratorByteStream`. The `AsyncByteSteam` and `SyncByteStream` classes are now pure interface classes. (#133)
+- Add `LocalProtocolError`, `RemoteProtocolError` exceptions. (Pull #129)
+- Add `UnsupportedProtocol` exception. (Pull #128)
+- Add `.get_connection_info()` method. (Pull #102, #137)
+- Add better TRACE logs. (Pull #101)
+
+### Changed
+
+- `max_keepalive` is deprecated in favour of `max_keepalive_connections`. (Pull #140)
+
+### Fixed
+
+- Improve handling of server disconnects. (Pull #112)
+
+## 0.9.1 (May 27th, 2020)
+
+### Fixed
+
+- Proper host resolution for sync case, including IPv6 support. (Pull #97)
+- Close outstanding connections when connection pool is closed. (Pull #98)
+
+## 0.9.0 (May 21th, 2020)
+
+### Changed
+
+- URL port becomes an `Optional[int]` instead of `int`. (Pull #92)
+
+### Fixed
+
+- Honor HTTP/2 max concurrent streams settings. (Pull #89, #90)
+- Remove incorrect debug log. (Pull #83)
+
+## 0.8.4 (May 11th, 2020)
+
+### Added
+
+- Logging via HTTPCORE_LOG_LEVEL and HTTPX_LOG_LEVEL environment variables
+and TRACE level logging. (Pull #79)
+
+### Fixed
+
+- Reuse of connections on HTTP/2 in close concurrency situations. (Pull #81)
+
+## 0.8.3 (May 6rd, 2020)
+
+### Fixed
+
+- Include `Host` and `Accept` headers on proxy "CONNECT" requests.
+- De-duplicate any headers also contained in proxy_headers.
+- HTTP/2 flag not being passed down to proxy connections.
+
+## 0.8.2 (May 3rd, 2020)
+
+### Fixed
+
+- Fix connections using proxy forwarding requests not being added to the
+connection pool properly. (Pull #70)
+
+## 0.8.1 (April 30th, 2020)
+
+### Changed
+
+- Allow inherintance of both `httpcore.AsyncByteStream`, `httpcore.SyncByteStream` without type conflicts.
+
+## 0.8.0 (April 30th, 2020)
+
+### Fixed
+
+- Fixed tunnel proxy support.
+
+### Added
+
+- New `TimeoutException` base class.
+
+## 0.7.0 (March 5th, 2020)
+
+- First integration with HTTPX.

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llava_next/lib/python3.10/site-packages/httpcore-0.17.3.dist-info/top_level.txt

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llava_next/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/INSTALLER

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

llava_next/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/LICENSE

@@ -0,0 +1,27 @@
+Copyright (c) 2005-2021 Fredrik Johansson and mpmath contributors
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. Redistributions in binary form must reproduce the above copyright
+     notice, this list of conditions and the following disclaimer in the
+     documentation and/or other materials provided with the distribution.
+  c. Neither the name of the copyright holder nor the names of its
+     contributors may be used to endorse or promote products derived
+     from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.

llava_next/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/METADATA

@@ -0,0 +1,233 @@
+Metadata-Version: 2.1
+Name: mpmath
+Version: 1.3.0
+Summary: Python library for arbitrary-precision floating-point arithmetic
+Home-page: http://mpmath.org/
+Author: Fredrik Johansson
+Author-email: fredrik.johansson@gmail.com
+License: BSD
+Project-URL: Source, https://github.com/fredrik-johansson/mpmath
+Project-URL: Tracker, https://github.com/fredrik-johansson/mpmath/issues
+Project-URL: Documentation, http://mpmath.org/doc/current/
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+License-File: LICENSE
+Provides-Extra: develop
+Requires-Dist: pytest (>=4.6) ; extra == 'develop'
+Requires-Dist: pycodestyle ; extra == 'develop'
+Requires-Dist: pytest-cov ; extra == 'develop'
+Requires-Dist: codecov ; extra == 'develop'
+Requires-Dist: wheel ; extra == 'develop'
+Provides-Extra: docs
+Requires-Dist: sphinx ; extra == 'docs'
+Provides-Extra: gmpy
+Requires-Dist: gmpy2 (>=2.1.0a4) ; (platform_python_implementation != "PyPy") and extra == 'gmpy'
+Provides-Extra: tests
+Requires-Dist: pytest (>=4.6) ; extra == 'tests'
+
+mpmath
+======
+
+|pypi version| |Build status| |Code coverage status| |Zenodo Badge|
+
+.. |pypi version| image:: https://img.shields.io/pypi/v/mpmath.svg
+   :target: https://pypi.python.org/pypi/mpmath
+.. |Build status| image:: https://github.com/fredrik-johansson/mpmath/workflows/test/badge.svg
+   :target: https://github.com/fredrik-johansson/mpmath/actions?workflow=test
+.. |Code coverage status| image:: https://codecov.io/gh/fredrik-johansson/mpmath/branch/master/graph/badge.svg
+   :target: https://codecov.io/gh/fredrik-johansson/mpmath
+.. |Zenodo Badge| image:: https://zenodo.org/badge/2934512.svg
+   :target: https://zenodo.org/badge/latestdoi/2934512
+
+A Python library for arbitrary-precision floating-point arithmetic.
+
+Website: http://mpmath.org/
+Main author: Fredrik Johansson <fredrik.johansson@gmail.com>
+
+Mpmath is free software released under the New BSD License (see the
+LICENSE file for details)
+
+0. History and credits
+----------------------
+
+The following people (among others) have contributed major patches
+or new features to mpmath:
+
+* Pearu Peterson <pearu.peterson@gmail.com>
+* Mario Pernici <mario.pernici@mi.infn.it>
+* Ondrej Certik <ondrej@certik.cz>
+* Vinzent Steinberg <vinzent.steinberg@gmail.cm>
+* Nimish Telang <ntelang@gmail.com>
+* Mike Taschuk <mtaschuk@ece.ualberta.ca>
+* Case Van Horsen <casevh@gmail.com>
+* Jorn Baayen <jorn.baayen@gmail.com>
+* Chris Smith <smichr@gmail.com>
+* Juan Arias de Reyna <arias@us.es>
+* Ioannis Tziakos <itziakos@gmail.com>
+* Aaron Meurer <asmeurer@gmail.com>
+* Stefan Krastanov <krastanov.stefan@gmail.com>
+* Ken Allen <ken.allen@sbcglobal.net>
+* Timo Hartmann <thartmann15@gmail.com>
+* Sergey B Kirpichev <skirpichev@gmail.com>
+* Kris Kuhlman <kristopher.kuhlman@gmail.com>
+* Paul Masson <paulmasson@analyticphysics.com>
+* Michael Kagalenko <michael.kagalenko@gmail.com>
+* Jonathan Warner <warnerjon12@gmail.com>
+* Max Gaukler <max.gaukler@fau.de>
+* Guillermo Navas-Palencia <g.navas.palencia@gmail.com>
+* Nike Dattani <nike@hpqc.org>
+
+Numerous other people have contributed by reporting bugs,
+requesting new features, or suggesting improvements to the
+documentation.
+
+For a detailed changelog, including individual contributions,
+see the CHANGES file.
+
+Fredrik's work on mpmath during summer 2008 was sponsored by Google
+as part of the Google Summer of Code program.
+
+Fredrik's work on mpmath during summer 2009 was sponsored by the
+American Institute of Mathematics under the support of the National Science
+Foundation Grant No. 0757627 (FRG: L-functions and Modular Forms).
+
+Any opinions, findings, and conclusions or recommendations expressed in this
+material are those of the author(s) and do not necessarily reflect the
+views of the sponsors.
+
+Credit also goes to:
+
+* The authors of the GMP library and the Python wrapper
+  gmpy, enabling mpmath to become much faster at
+  high precision
+* The authors of MPFR, pari/gp, MPFUN, and other arbitrary-
+  precision libraries, whose documentation has been helpful
+  for implementing many of the algorithms in mpmath
+* Wikipedia contributors; Abramowitz & Stegun; Gradshteyn & Ryzhik;
+  Wolfram Research for MathWorld and the Wolfram Functions site.
+  These are the main references used for special functions
+  implementations.
+* George Brandl for developing the Sphinx documentation tool
+  used to build mpmath's documentation
+
+Release history:
+
+* Version 1.3.0 released on March 7, 2023
+* Version 1.2.0 released on February 1, 2021
+* Version 1.1.0 released on December 11, 2018
+* Version 1.0.0 released on September 27, 2017
+* Version 0.19 released on June 10, 2014
+* Version 0.18 released on December 31, 2013
+* Version 0.17 released on February 1, 2011
+* Version 0.16 released on September 24, 2010
+* Version 0.15 released on June 6, 2010
+* Version 0.14 released on February 5, 2010
+* Version 0.13 released on August 13, 2009
+* Version 0.12 released on June 9, 2009
+* Version 0.11 released on January 26, 2009
+* Version 0.10 released on October 15, 2008
+* Version 0.9 released on August 23, 2008
+* Version 0.8 released on April 20, 2008
+* Version 0.7 released on March 12, 2008
+* Version 0.6 released on January 13, 2008
+* Version 0.5 released on November 24, 2007
+* Version 0.4 released on November 3, 2007
+* Version 0.3 released on October 5, 2007
+* Version 0.2 released on October 2, 2007
+* Version 0.1 released on September 27, 2007
+
+1. Download & installation
+--------------------------
+
+Mpmath requires Python 2.7 or 3.5 (or later versions). It has been tested
+with CPython 2.7, 3.5 through 3.7 and for PyPy.
+
+The latest release of mpmath can be downloaded from the mpmath
+website and from https://github.com/fredrik-johansson/mpmath/releases
+
+It should also be available in the Python Package Index at
+https://pypi.python.org/pypi/mpmath
+
+To install latest release of Mpmath with pip, simply run
+
+``pip install mpmath``
+
+Or unpack the mpmath archive and run
+
+``python setup.py install``
+
+Mpmath can also be installed using
+
+``python -m easy_install mpmath``
+
+The latest development code is available from
+https://github.com/fredrik-johansson/mpmath
+
+See the main documentation for more detailed instructions.
+
+2. Running tests
+----------------
+
+The unit tests in mpmath/tests/ can be run via the script
+runtests.py, but it is recommended to run them with py.test
+(https://pytest.org/), especially
+to generate more useful reports in case there are failures.
+
+You may also want to check out the demo scripts in the demo
+directory.
+
+The master branch is automatically tested by Travis CI.
+
+3. Documentation
+----------------
+
+Documentation in reStructuredText format is available in the
+doc directory included with the source package. These files
+are human-readable, but can be compiled to prettier HTML using
+the build.py script (requires Sphinx, http://sphinx.pocoo.org/).
+
+See setup.txt in the documentation for more information.
+
+The most recent documentation is also available in HTML format:
+
+http://mpmath.org/doc/current/
+
+4. Known problems
+-----------------
+
+Mpmath is a work in progress. Major issues include:
+
+* Some functions may return incorrect values when given extremely
+  large arguments or arguments very close to singularities.
+
+* Directed rounding works for arithmetic operations. It is implemented
+  heuristically for other operations, and their results may be off by one
+  or two units in the last place (even if otherwise accurate).
+
+* Some IEEE 754 features are not available. Inifinities and NaN are
+  partially supported; denormal rounding is currently not available
+  at all.
+
+* The interface for switching precision and rounding is not finalized.
+  The current method is not threadsafe.
+
+5. Help and bug reports
+-----------------------
+
+General questions and comments can be sent to the mpmath mailinglist,
+mpmath@googlegroups.com
+
+You can also report bugs and send patches to the mpmath issue tracker,
+https://github.com/fredrik-johansson/mpmath/issues

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llava_next/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.38.4)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

llava_next/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+mpmath

llava_next/lib/python3.10/site-packages/ninja/__init__.py

@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+import os
+import platform
+import subprocess
+import sys
+
+from ._version import version as __version__
+
+__all__ = ["__version__", "DATA", "BIN_DIR", "ninja"]
+
+
+def __dir__():
+    return __all__
+
+
+try:
+    from .ninja_syntax import Writer, escape, expand
+except ImportError:
+    # Support importing `ninja_syntax` from the source tree
+    if not os.path.exists(
+            os.path.join(os.path.dirname(__file__), 'ninja_syntax.py')):
+        sys.path.insert(0, os.path.abspath(os.path.join(
+            os.path.dirname(__file__), '../../Ninja-src/misc')))
+    from ninja_syntax import Writer, escape, expand  # noqa: F401
+
+DATA = os.path.join(os.path.dirname(__file__), 'data')
+
+# Support running tests from the source tree
+if not os.path.exists(DATA):
+    from skbuild.constants import CMAKE_INSTALL_DIR as SKBUILD_CMAKE_INSTALL_DIR
+    from skbuild.constants import set_skbuild_plat_name
+
+    if platform.system().lower() == "darwin":
+        # Since building the project specifying --plat-name or CMAKE_OSX_* variables
+        # leads to different SKBUILD_DIR, the code below attempt to guess the most
+        # likely plat-name.
+        _skbuild_dirs = os.listdir(os.path.join(os.path.dirname(__file__), '..', '..', '_skbuild'))
+        if _skbuild_dirs:
+            _likely_plat_name = '-'.join(_skbuild_dirs[0].split('-')[:3])
+            set_skbuild_plat_name(_likely_plat_name)
+
+    _data = os.path.abspath(os.path.join(
+        os.path.dirname(__file__), '..', '..', SKBUILD_CMAKE_INSTALL_DIR(), 'src/ninja/data'))
+    if os.path.exists(_data):
+        DATA = _data
+
+BIN_DIR = os.path.join(DATA, 'bin')
+
+
+def _program(name, args):
+    return subprocess.call([os.path.join(BIN_DIR, name)] + args, close_fds=False)
+
+
+def ninja():
+    raise SystemExit(_program('ninja', sys.argv[1:]))

llava_next/lib/python3.10/site-packages/ninja/__main__.py

@@ -0,0 +1,5 @@
+# -*- coding: utf-8 -*-
+from ninja import ninja
+
+if __name__ == '__main__':
+    ninja()

llava_next/lib/python3.10/site-packages/ninja/_version.py

@@ -0,0 +1,16 @@
+# file generated by setuptools_scm
+# don't change, don't track in version control
+TYPE_CHECKING = False
+if TYPE_CHECKING:
+    from typing import Tuple, Union
+    VERSION_TUPLE = Tuple[Union[int, str], ...]
+else:
+    VERSION_TUPLE = object
+
+version: str
+__version__: str
+__version_tuple__: VERSION_TUPLE
+version_tuple: VERSION_TUPLE
+
+__version__ = version = '1.11.1.1'
+__version_tuple__ = version_tuple = (1, 11, 1, 1)

llava_next/lib/python3.10/site-packages/ninja/ninja_syntax.py

@@ -0,0 +1,199 @@
+#!/usr/bin/python
+
+# Copyright 2011 Google Inc. 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.
+
+"""Python module for generating .ninja files.
+
+Note that this is emphatically not a required piece of Ninja; it's
+just a helpful utility for build-file-generation systems that already
+use Python.
+"""
+
+import re
+import textwrap
+
+def escape_path(word):
+    return word.replace('$ ', '$$ ').replace(' ', '$ ').replace(':', '$:')
+
+class Writer(object):
+    def __init__(self, output, width=78):
+        self.output = output
+        self.width = width
+
+    def newline(self):
+        self.output.write('\n')
+
+    def comment(self, text):
+        for line in textwrap.wrap(text, self.width - 2, break_long_words=False,
+                                  break_on_hyphens=False):
+            self.output.write('# ' + line + '\n')
+
+    def variable(self, key, value, indent=0):
+        if value is None:
+            return
+        if isinstance(value, list):
+            value = ' '.join(filter(None, value))  # Filter out empty strings.
+        self._line('%s = %s' % (key, value), indent)
+
+    def pool(self, name, depth):
+        self._line('pool %s' % name)
+        self.variable('depth', depth, indent=1)
+
+    def rule(self, name, command, description=None, depfile=None,
+             generator=False, pool=None, restat=False, rspfile=None,
+             rspfile_content=None, deps=None):
+        self._line('rule %s' % name)
+        self.variable('command', command, indent=1)
+        if description:
+            self.variable('description', description, indent=1)
+        if depfile:
+            self.variable('depfile', depfile, indent=1)
+        if generator:
+            self.variable('generator', '1', indent=1)
+        if pool:
+            self.variable('pool', pool, indent=1)
+        if restat:
+            self.variable('restat', '1', indent=1)
+        if rspfile:
+            self.variable('rspfile', rspfile, indent=1)
+        if rspfile_content:
+            self.variable('rspfile_content', rspfile_content, indent=1)
+        if deps:
+            self.variable('deps', deps, indent=1)
+
+    def build(self, outputs, rule, inputs=None, implicit=None, order_only=None,
+              variables=None, implicit_outputs=None, pool=None, dyndep=None):
+        outputs = as_list(outputs)
+        out_outputs = [escape_path(x) for x in outputs]
+        all_inputs = [escape_path(x) for x in as_list(inputs)]
+
+        if implicit:
+            implicit = [escape_path(x) for x in as_list(implicit)]
+            all_inputs.append('|')
+            all_inputs.extend(implicit)
+        if order_only:
+            order_only = [escape_path(x) for x in as_list(order_only)]
+            all_inputs.append('||')
+            all_inputs.extend(order_only)
+        if implicit_outputs:
+            implicit_outputs = [escape_path(x)
+                                for x in as_list(implicit_outputs)]
+            out_outputs.append('|')
+            out_outputs.extend(implicit_outputs)
+
+        self._line('build %s: %s' % (' '.join(out_outputs),
+                                     ' '.join([rule] + all_inputs)))
+        if pool is not None:
+            self._line('  pool = %s' % pool)
+        if dyndep is not None:
+            self._line('  dyndep = %s' % dyndep)
+
+        if variables:
+            if isinstance(variables, dict):
+                iterator = iter(variables.items())
+            else:
+                iterator = iter(variables)
+
+            for key, val in iterator:
+                self.variable(key, val, indent=1)
+
+        return outputs
+
+    def include(self, path):
+        self._line('include %s' % path)
+
+    def subninja(self, path):
+        self._line('subninja %s' % path)
+
+    def default(self, paths):
+        self._line('default %s' % ' '.join(as_list(paths)))
+
+    def _count_dollars_before_index(self, s, i):
+        """Returns the number of '$' characters right in front of s[i]."""
+        dollar_count = 0
+        dollar_index = i - 1
+        while dollar_index > 0 and s[dollar_index] == '$':
+            dollar_count += 1
+            dollar_index -= 1
+        return dollar_count
+
+    def _line(self, text, indent=0):
+        """Write 'text' word-wrapped at self.width characters."""
+        leading_space = '  ' * indent
+        while len(leading_space) + len(text) > self.width:
+            # The text is too wide; wrap if possible.
+
+            # Find the rightmost space that would obey our width constraint and
+            # that's not an escaped space.
+            available_space = self.width - len(leading_space) - len(' $')
+            space = available_space
+            while True:
+                space = text.rfind(' ', 0, space)
+                if (space < 0 or
+                    self._count_dollars_before_index(text, space) % 2 == 0):
+                    break
+
+            if space < 0:
+                # No such space; just use the first unescaped space we can find.
+                space = available_space - 1
+                while True:
+                    space = text.find(' ', space + 1)
+                    if (space < 0 or
+                        self._count_dollars_before_index(text, space) % 2 == 0):
+                        break
+            if space < 0:
+                # Give up on breaking.
+                break
+
+            self.output.write(leading_space + text[0:space] + ' $\n')
+            text = text[space+1:]
+
+            # Subsequent lines are continuations, so indent them.
+            leading_space = '  ' * (indent+2)
+
+        self.output.write(leading_space + text + '\n')
+
+    def close(self):
+        self.output.close()
+
+
+def as_list(input):
+    if input is None:
+        return []
+    if isinstance(input, list):
+        return input
+    return [input]
+
+
+def escape(string):
+    """Escape a string such that it can be embedded into a Ninja file without
+    further interpretation."""
+    assert '\n' not in string, 'Ninja syntax does not allow newlines'
+    # We only have one special metacharacter: '$'.
+    return string.replace('$', '$$')
+
+
+def expand(string, vars, local_vars={}):
+    """Expand a string containing $vars as Ninja would.
+
+    Note: doesn't handle the full Ninja variable syntax, but it's enough
+    to make configure.py's use of it work.
+    """
+    def exp(m):
+        var = m.group(1)
+        if var == '$':
+            return '$'
+        return local_vars.get(var, vars.get(var, ''))
+    return re.sub(r'\$(\$|\w*)', exp, string)

llava_next/lib/python3.10/site-packages/pandas/tests/indexing/__pycache__/test_loc.cpython-310.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c0c347411d047dee0c308f2da6fd90327ea505d0e64771384f6806c2aecd5ff8
+size 107549

parrot/lib/python3.10/site-packages/transformers/models/gptj/configuration_gptj.py

@@ -0,0 +1,215 @@
+# coding=utf-8
+# Copyright 2021 The EleutherAI and HuggingFace Teams. 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.
+""" GPT-J model configuration"""
+from collections import OrderedDict
+from typing import Any, List, Mapping, Optional
+
+from ... import PreTrainedTokenizer, TensorType, is_torch_available
+from ...configuration_utils import PretrainedConfig
+from ...onnx import OnnxConfigWithPast, PatchingSpec
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class GPTJConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
+    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 GPT-J
+    [EleutherAI/gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B) architecture. Configuration objects inherit from
+    [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`]
+    for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50400):
+            Vocabulary size of the GPT-J model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GPTJModel`].
+        n_positions (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        n_embd (`int`, *optional*, defaults to 4096):
+            Dimensionality of the embeddings and hidden states.
+        n_layer (`int`, *optional*, defaults to 28):
+            Number of hidden layers in the Transformer encoder.
+        n_head (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        rotary_dim (`int`, *optional*, defaults to 64):
+            Number of dimensions in the embedding that Rotary Position Embedding is applied to.
+        n_inner (`int`, *optional*, defaults to None):
+            Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
+        activation_function (`str`, *optional*, defaults to `"gelu_new"`):
+            Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
+        resid_pdrop (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        embd_pdrop (`int`, *optional*, defaults to 0.1):
+            The dropout ratio for the embeddings.
+        attn_pdrop (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
+            The epsilon to use in the layer normalization layers.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
+
+    Example:
+
+    ```python
+    >>> from transformers import GPTJModel, GPTJConfig
+
+    >>> # Initializing a GPT-J 6B configuration
+    >>> configuration = GPTJConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = GPTJModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "gptj"
+    attribute_map = {
+        "max_position_embeddings": "n_positions",
+        "hidden_size": "n_embd",
+        "num_attention_heads": "n_head",
+        "num_hidden_layers": "n_layer",
+    }
+
+    def __init__(
+        self,
+        vocab_size=50400,
+        n_positions=2048,
+        n_embd=4096,
+        n_layer=28,
+        n_head=16,
+        rotary_dim=64,
+        n_inner=None,
+        activation_function="gelu_new",
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attn_pdrop=0.0,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=50256,
+        eos_token_id=50256,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_inner = n_inner
+        self.rotary_dim = rotary_dim
+        self.activation_function = activation_function
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attn_pdrop = attn_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        super().__init__(
+            bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
+        )
+
+
+# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
+class GPTJOnnxConfig(OnnxConfigWithPast):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        task: str = "default",
+        patching_specs: List[PatchingSpec] = None,
+        use_past: bool = False,
+    ):
+        super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
+        if not getattr(self._config, "pad_token_id", None):
+            # TODO: how to do that better?
+            self._config.pad_token_id = 0
+
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
+        if self.use_past:
+            self.fill_with_past_key_values_(common_inputs, direction="inputs")
+            common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
+        else:
+            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
+
+        return common_inputs
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.n_layer
+
+    @property
+    def num_attention_heads(self) -> int:
+        return self._config.n_head
+
+    def generate_dummy_inputs(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
+            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+        )
+
+        # We need to order the input in the way they appears in the forward()
+        ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
+
+        # Need to add the past_keys
+        if self.use_past:
+            if not is_torch_available():
+                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+            else:
+                import torch
+
+                batch, seqlen = common_inputs["input_ids"].shape
+                # Not using the same length for past_key_values
+                past_key_values_length = seqlen + 2
+                past_shape = (
+                    batch,
+                    self.num_attention_heads,
+                    past_key_values_length,
+                    self._config.hidden_size // self.num_attention_heads,
+                )
+                ordered_inputs["past_key_values"] = [
+                    (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
+                ]
+
+        ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
+        if self.use_past:
+            mask_dtype = ordered_inputs["attention_mask"].dtype
+            ordered_inputs["attention_mask"] = torch.cat(
+                [ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
+            )
+
+        return ordered_inputs
+
+    @property
+    def default_onnx_opset(self) -> int:
+        return 13

parrot/lib/python3.10/site-packages/transformers/models/gptj/modeling_tf_gptj.py

@@ -0,0 +1,1099 @@
+# coding=utf-8
+# Copyright 2022 The EleutherAI and HuggingFace Teams. 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.
+""" TF 2.0 GPT-J model."""
+
+from __future__ import annotations
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...file_utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+)
+from ...modeling_tf_outputs import (
+    TFBaseModelOutputWithPast,
+    TFCausalLMOutputWithPast,
+    TFQuestionAnsweringModelOutput,
+    TFSequenceClassifierOutputWithPast,
+)
+from ...modeling_tf_utils import (
+    TFCausalLanguageModelingLoss,
+    TFModelInputType,
+    TFPreTrainedModel,
+    TFQuestionAnsweringLoss,
+    TFSequenceClassificationLoss,
+    TFSharedEmbeddings,
+    get_initializer,
+    keras,
+    keras_serializable,
+    unpack_inputs,
+)
+from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
+from ...utils import logging
+from .configuration_gptj import GPTJConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-j-6B"
+_CONFIG_FOR_DOC = "GPTJConfig"
+
+
+def create_sinusoidal_positions(num_pos: int, dim: int) -> tf.Tensor:
+    inv_freq = tf.cast(1.0 / (10000 ** (tf.range(0, dim, 2) / dim)), tf.float32)
+    sinusoid_inp = tf.cast(tf.einsum("i , j -> i j", tf.range(num_pos, dtype=tf.float32), inv_freq), tf.float32)
+    sin, cos = tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)
+    out = tf.concat((sin, cos), axis=1)
+    return out
+
+
+def rotate_every_two(x: tf.Tensor) -> tf.Tensor:
+    rotate_half_tensor = tf.stack((-x[:, :, :, 1::2], x[:, :, :, ::2]), axis=-1)
+    new_shape = shape_list(rotate_half_tensor)[:-2] + [tf.math.reduce_prod(shape_list(rotate_half_tensor)[-2:])]
+    rotate_half_tensor = tf.reshape(rotate_half_tensor, new_shape)
+    return rotate_half_tensor
+
+
+def apply_rotary_pos_emb(tensor: tf.Tensor, sincos: tf.Tensor) -> tf.Tensor:
+    sin_pos, cos_pos = sincos
+    sin_pos = tf.repeat(sin_pos[:, :, None, :], 2, 3)
+    cos_pos = tf.repeat(cos_pos[:, :, None, :], 2, 3)
+    return (tensor * cos_pos) + (rotate_every_two(tensor) * sin_pos)
+
+
+class TFGPTJAttention(keras.layers.Layer):
+    def __init__(self, config: GPTJConfig, **kwargs):
+        super().__init__(**kwargs)
+
+        self.embed_dim = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_attention_heads
+        if self.head_dim * self.num_attention_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and"
+                f" `num_attention_heads`: {self.num_attention_heads})."
+            )
+        self.scale_attn = self.head_dim**0.5
+        self.rotary_dim = config.rotary_dim
+
+        self.attn_dropout = keras.layers.Dropout(config.attn_pdrop)
+        self.resid_dropout = keras.layers.Dropout(config.resid_pdrop)
+
+        self.q_proj = keras.layers.Dense(
+            self.embed_dim,
+            use_bias=False,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="q_proj",
+        )
+        self.k_proj = keras.layers.Dense(
+            self.embed_dim,
+            use_bias=False,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="k_proj",
+        )
+        self.v_proj = keras.layers.Dense(
+            self.embed_dim,
+            use_bias=False,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="v_proj",
+        )
+        self.out_proj = keras.layers.Dense(
+            self.embed_dim,
+            use_bias=False,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="out_proj",
+        )
+
+        self.max_positions = config.max_position_embeddings
+        self.lower_triangle_mask = tf.reshape(
+            tf.cast(tf.experimental.numpy.tril(tf.ones((self.max_positions, self.max_positions))), tf.int8),
+            (1, 1, self.max_positions, self.max_positions),
+        )
+        pos_embd_dim = self.rotary_dim or self.embed_dim
+        self.embed_positions = create_sinusoidal_positions(self.max_positions, pos_embd_dim)
+
+    def get_causal_mask(self, key_length, query_length) -> tf.Tensor:
+        return tf.cast(self.lower_triangle_mask[:, :, key_length - query_length : key_length, :key_length], tf.bool)
+
+    @staticmethod
+    def get_masked_bias(dtype: tf.DType) -> tf.Tensor:
+        return tf.cast(tf.constant(-1e9), dtype)
+
+    def _split_heads(self, hidden_states: tf.Tensor, rotary: bool) -> tf.Tensor:
+        """
+        Splits hidden dim into attn_head_size and num_attention_heads
+        """
+        new_shape = shape_list(hidden_states)[:-1] + [self.num_attention_heads, self.head_dim]
+        hidden_states = tf.reshape(hidden_states, new_shape)
+        if rotary:
+            return hidden_states
+        if len(shape_list(hidden_states)) == 4:
+            return tf.transpose(hidden_states, (0, 2, 1, 3))  # (batch, head, seq_length, head_features)
+        if len(shape_list(hidden_states)) == 5:
+            return tf.transpose(hidden_states, (0, 1, 3, 2, 4))  # (batch, blocks, head, block_length, head_features)
+        raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}")
+
+    def _merge_heads(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        """
+        Merges attn_head_size dim and num_attn_heads dim into hidden dim
+        """
+        if len(shape_list(hidden_states)) == 4:
+            hidden_states = tf.transpose(hidden_states, (0, 2, 1, 3))
+        elif len(shape_list(hidden_states)) == 5:
+            hidden_states = tf.transpose(hidden_states, (0, 1, 3, 2, 4))
+        else:
+            raise ValueError(f"Input tensor rank should be one of [4, 5], but is: {len(shape_list(hidden_states))}")
+        new_shape = shape_list(hidden_states)[:-2] + [self.num_attention_heads * self.head_dim]
+        return tf.reshape(hidden_states, new_shape)
+
+    def _attn(
+        self,
+        query: tf.Tensor,
+        key: tf.Tensor,
+        value: tf.Tensor,
+        attention_mask: tf.Tensor | None = None,
+        head_mask: tf.Tensor | None = None,
+    ) -> Tuple[tf.Tensor, tf.Tensor]:
+        # compute causal mask from causal mask buffer
+        query_length, key_length = shape_list(query)[-2], shape_list(key)[-2]
+        causal_mask = self.get_causal_mask(key_length, query_length)
+
+        # Keep the attention weights computation in fp32 to avoid overflow issues
+        query = tf.cast(query, tf.float32)
+        key = tf.cast(key, tf.float32)
+
+        attn_weights = tf.matmul(query, key, transpose_b=True)
+        attn_weights = tf.where(causal_mask, attn_weights, self.get_masked_bias(attn_weights.dtype))
+
+        attn_weights = attn_weights / self.scale_attn
+
+        if attention_mask is not None:
+            # Apply the attention mask
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = stable_softmax(attn_weights, axis=-1)
+        attn_weights = tf.cast(attn_weights, value.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attn_weights = attn_weights * head_mask
+
+        attn_output = tf.matmul(attn_weights, value)
+
+        return attn_output, attn_weights
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        layer_past: Optional[Tuple[tf.Tensor, tf.Tensor]] = None,
+        attention_mask: tf.Tensor | None = None,
+        position_ids: tf.Tensor | None = None,
+        head_mask: tf.Tensor | None = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+    ):
+        query = self.q_proj(hidden_states)
+        key = self.k_proj(hidden_states)
+        value = self.v_proj(hidden_states)
+
+        query = self._split_heads(query, True)
+        key = self._split_heads(key, True)
+        value = self._split_heads(value, False)
+
+        sincos = tf.cast(tf.gather(self.embed_positions, position_ids, axis=0), hidden_states.dtype)
+        sincos = tf.split(sincos, 2, axis=-1)
+        if self.rotary_dim is not None:
+            k_rot = key[:, :, :, : self.rotary_dim]
+            k_pass = key[:, :, :, self.rotary_dim :]
+
+            q_rot = query[:, :, :, : self.rotary_dim]
+            q_pass = query[:, :, :, self.rotary_dim :]
+
+            k_rot = apply_rotary_pos_emb(k_rot, sincos)
+            q_rot = apply_rotary_pos_emb(q_rot, sincos)
+
+            key = tf.concat((k_rot, k_pass), axis=-1)
+            query = tf.concat((q_rot, q_pass), axis=-1)
+        else:
+            key = apply_rotary_pos_emb(key, sincos)
+            query = apply_rotary_pos_emb(query, sincos)
+
+        key = tf.transpose(key, (0, 2, 1, 3))
+        query = tf.transpose(query, (0, 2, 1, 3))
+
+        if layer_past is not None:
+            past_key = layer_past[0]
+            past_value = layer_past[1]
+            key = tf.concat((past_key, key), axis=-2)
+            value = tf.concat((past_value, value), axis=-2)
+
+        if use_cache is True:
+            present = (key, value)
+        else:
+            present = None
+
+        # compute self-attention: V x Softmax(QK^T)
+        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
+
+        attn_output = self._merge_heads(attn_output)
+        attn_output = self.out_proj(attn_output)
+        attn_output = self.resid_dropout(attn_output)
+
+        outputs = (attn_output, present)
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs  # a, present, (attentions)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build([None, None, self.embed_dim])
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build([None, None, self.embed_dim])
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build([None, None, self.embed_dim])
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build([None, None, self.embed_dim])
+
+
+class TFGPTJMLP(keras.layers.Layer):
+    def __init__(self, intermediate_size: int, config: GPTJConfig, **kwargs):
+        super().__init__(**kwargs)
+        embed_dim = config.n_embd
+
+        self.fc_in = keras.layers.Dense(
+            intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="fc_in"
+        )
+        self.fc_out = keras.layers.Dense(
+            embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="fc_out"
+        )
+
+        self.act = get_tf_activation(config.activation_function)
+        self.dropout = keras.layers.Dropout(config.embd_pdrop)
+        self.embed_dim = config.n_embd
+        self.intermediate_size = intermediate_size
+
+    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
+        hidden_states = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc_out(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "fc_in", None) is not None:
+            with tf.name_scope(self.fc_in.name):
+                self.fc_in.build([None, None, self.embed_dim])
+        if getattr(self, "fc_out", None) is not None:
+            with tf.name_scope(self.fc_out.name):
+                self.fc_out.build([None, None, self.intermediate_size])
+
+
+class TFGPTJBlock(keras.layers.Layer):
+    def __init__(self, config: GPTJConfig, **kwargs):
+        super().__init__(**kwargs)
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
+        self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
+        self.attn = TFGPTJAttention(config, name="attn")
+        self.mlp = TFGPTJMLP(inner_dim, config, name="mlp")
+        self.config = config
+
+    def call(
+        self,
+        hidden_states: tf.Tensor,
+        layer_past: tf.Tensor | None = None,
+        attention_mask: tf.Tensor | None = None,
+        position_ids: tf.Tensor | None = None,
+        head_mask: tf.Tensor | None = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+    ):
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_outputs = self.attn(
+            hidden_states=hidden_states,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )  # attn_outputs: attn_output, present, (attentions)
+        attn_output = attn_outputs[0]
+        outputs = attn_outputs[1:]
+
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        hidden_states = attn_output + feed_forward_hidden_states + residual
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+        return outputs  # hidden_states, present, (attentions)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "ln_1", None) is not None:
+            with tf.name_scope(self.ln_1.name):
+                self.ln_1.build([None, None, self.config.n_embd])
+        if getattr(self, "attn", None) is not None:
+            with tf.name_scope(self.attn.name):
+                self.attn.build(None)
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+
+
+@keras_serializable
+class TFGPTJMainLayer(keras.layers.Layer):
+    config_class = GPTJConfig
+
+    def __init__(self, config: GPTJConfig, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+        self.config = config
+        self.output_attentions = config.output_attentions
+        self.output_hidden_states = config.output_hidden_states
+        self.use_cache = config.use_cache
+        self.return_dict = config.use_return_dict
+
+        self.num_hidden_layers = config.n_layer
+        self.n_embd = config.n_embd
+        self.n_positions = config.n_positions
+        self.initializer_range = config.initializer_range
+
+        self.wte = TFSharedEmbeddings(
+            config.vocab_size, config.hidden_size, initializer_range=config.initializer_range, name="wte"
+        )
+        self.drop = keras.layers.Dropout(config.embd_pdrop)
+        self.h = [TFGPTJBlock(config, name=f"h_._{i}") for i in range(config.n_layer)]
+        self.ln_f = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_f")
+        self.embed_dim = config.n_embd
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, value: tf.Tensor):
+        self.wte.weight = value
+        self.wte.vocab_size = shape_list(value)[0]
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+        """
+        raise NotImplementedError
+
+    @unpack_inputs
+    def call(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        training=False,
+    ) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = shape_list(input_ids)
+            input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
+        elif inputs_embeds is not None:
+            input_shape = shape_list(inputs_embeds)[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = [None] * len(self.h)
+        else:
+            past_length = shape_list(past_key_values[0][0])[-2]
+
+        if position_ids is None:
+            position_ids = tf.expand_dims(tf.range(past_length, input_shape[-1] + past_length), axis=0)
+
+        if attention_mask is not None:
+            # We create a 3D attention mask from a 2D tensor mask.
+            # Sizes are [batch_size, 1, 1, to_seq_length]
+            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+            # this attention mask is more simple than the triangular masking of causal attention
+            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+            attention_mask_shape = shape_list(attention_mask)
+            attention_mask = tf.reshape(attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_shape[1]))
+
+            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+            # masked positions, this operation will create a tensor which is 0.0 for
+            # positions we want to attend and -10000.0 for masked positions.
+            # Since we are adding it to the raw scores before the softmax, this is
+            # effectively the same as removing these entirely.
+            one_cst = tf.constant(1.0)
+            attention_mask = tf.cast(attention_mask, dtype=one_cst.dtype)
+            attention_mask = tf.multiply(tf.subtract(one_cst, attention_mask), tf.constant(-10000.0))
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        if head_mask is not None:
+            raise NotImplementedError
+        else:
+            head_mask = [None] * self.num_hidden_layers
+            # head_mask = tf.constant([0] * self.num_hidden_layers)
+
+        position_ids = tf.reshape(position_ids, [-1, shape_list(position_ids)[-1]])
+
+        if inputs_embeds is None:
+            check_embeddings_within_bounds(input_ids, self.wte.vocab_size)
+            inputs_embeds = self.wte(input_ids, mode="embedding")
+
+        if token_type_ids is not None:
+            token_type_ids = tf.reshape(token_type_ids, [-1, shape_list(token_type_ids)[-1]])
+            token_type_embeds = self.wte(token_type_ids, mode="embedding")
+        else:
+            token_type_embeds = tf.constant(0.0)
+
+        token_type_embeds = tf.cast(token_type_embeds, dtype=inputs_embeds.dtype)
+        hidden_states = inputs_embeds + token_type_embeds
+        hidden_states = self.drop(hidden_states, training=training)
+
+        output_shape = input_shape + [shape_list(hidden_states)[-1]]
+
+        presents = () if use_cache else None
+        all_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (tf.reshape(hidden_states, output_shape),)
+
+            outputs = block(
+                hidden_states=hidden_states,
+                layer_past=layer_past,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                head_mask=head_mask[i],
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                training=training,
+            )
+
+            hidden_states = outputs[0]
+            if use_cache:
+                presents = presents + (outputs[1],)
+
+            if output_attentions:
+                all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
+
+        hidden_states = self.ln_f(hidden_states)
+
+        hidden_states = tf.reshape(hidden_states, output_shape)
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if output_attentions:
+            # let the number of heads free (-1) so we can extract attention even after head pruning
+            attention_output_shape = input_shape[:-1] + [-1] + shape_list(all_attentions[0])[-2:]
+            all_attentions = tuple(tf.reshape(t, attention_output_shape) for t in all_attentions)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
+
+        return TFBaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wte", None) is not None:
+            with tf.name_scope(self.wte.name):
+                self.wte.build(None)
+        if getattr(self, "ln_f", None) is not None:
+            with tf.name_scope(self.ln_f.name):
+                self.ln_f.build([None, None, self.embed_dim])
+        if getattr(self, "h", None) is not None:
+            for layer in self.h:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
+
+class TFGPTJPreTrainedModel(TFPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = GPTJConfig
+    base_model_prefix = "transformer"
+    # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
+    _keys_to_ignore_on_load_unexpected = [r"h.\d+.attn.bias"]
+
+
+GPTJ_START_DOCSTRING = r"""
+
+    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+    behavior.
+
+    <Tip>
+
+    TensorFlow models and layers in `transformers` accept two formats as input:
+
+    - having all inputs as keyword arguments (like PyTorch models), or
+    - having all inputs as a list, tuple or dict in the first positional argument.
+
+    The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
+    and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just
+    pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second
+    format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with
+    the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first
+    positional argument:
+
+    - a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
+    - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
+    `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
+    - a dictionary with one or several input Tensors associated to the input names given in the docstring:
+    `model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
+
+    Note that when creating models and layers with
+    [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry
+    about any of this, as you can just pass inputs like you would to any other Python function!
+
+    </Tip>
+
+    Parameters:
+        config ([`GPTJConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+GPTJ_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`Numpy array` or `tf.Tensor` of shape `(batch_size, input_ids_length)`):
+            `input_ids_length` = `sequence_length` if `past` is `None` else `past[0].shape[-2]` (`sequence_length` of
+            input past key value states). Indices of input sequence tokens in the vocabulary.
+
+            If `past` is used, only input IDs that do not have their past calculated should be passed as `input_ids`.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
+            [`PreTrainedTokenizer.encode`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        past_key_values (`List[tf.Tensor]` of length `config.n_layers`):
+            Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model (see
+            `past` output below). Can be used to speed up sequential decoding. The token ids which have their past
+            given to this model should not be passed as input ids as they have already been computed.
+        attention_mask (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        token_type_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
+            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+            1]`:
+
+            - 0 corresponds to a *sentence A* token,
+            - 1 corresponds to a *sentence B* token.
+
+            [What are token type IDs?](../glossary#token-type-ids)
+        position_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.max_position_embeddings - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
+            config will be used instead.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+            used instead.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
+            in eager mode, in graph mode the value will always be set to True.
+        training (`bool`, *optional*, defaults to `False`):
+            Whether or not to use the model in training mode (some modules like dropout modules have different
+            behaviors between training and evaluation).
+"""
+
+
+@add_start_docstrings(
+    "The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.",
+    GPTJ_START_DOCSTRING,
+)
+class TFGPTJModel(TFGPTJPreTrainedModel):
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.transformer = TFGPTJMainLayer(config, name="transformer")
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFBaseModelOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: TFModelInputType | None = None,
+        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
+        attention_mask: np.ndarray | tf.Tensor | None = None,
+        token_type_ids: np.ndarray | tf.Tensor | None = None,
+        position_ids: np.ndarray | tf.Tensor | None = None,
+        head_mask: np.ndarray | tf.Tensor | None = None,
+        inputs_embeds: np.ndarray | tf.Tensor | None = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = False,
+    ) -> Union[TFBaseModelOutputWithPast, Tuple[tf.Tensor]]:
+        r"""
+        use_cache (`bool`, *optional*, defaults to `True`):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past`). Set to `False` during training, `True` during generation
+        """
+
+        outputs = self.transformer(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+
+        return outputs
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
+
+@add_start_docstrings(
+    """
+    The GPT-J Model transformer with a language modeling head on top.
+    """,
+    GPTJ_START_DOCSTRING,
+)
+class TFGPTJForCausalLM(TFGPTJPreTrainedModel, TFCausalLanguageModelingLoss):
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.transformer = TFGPTJMainLayer(config, name="transformer")
+        self.lm_head = keras.layers.Dense(
+            config.vocab_size, kernel_initializer=get_initializer(config.initializer_range), name="lm_head"
+        )
+        self.config = config
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def prepare_inputs_for_generation(self, inputs, past_key_values=None, use_cache=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past_key_values:
+            inputs = tf.expand_dims(inputs[:, -1], -1)
+            if token_type_ids is not None:
+                token_type_ids = tf.expand_dims(token_type_ids[:, -1], -1)
+
+        position_ids = kwargs.get("position_ids", None)
+        attention_mask = kwargs.get("attention_mask", None)
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = tf.math.cumsum(attention_mask, axis=-1, exclusive=True)
+            if past_key_values:
+                position_ids = tf.expand_dims(position_ids[:, -1], -1)
+
+        return {
+            "input_ids": inputs,
+            "attention_mask": attention_mask,
+            "position_ids": position_ids,
+            "past_key_values": past_key_values,
+            "use_cache": use_cache,
+            "token_type_ids": token_type_ids,
+        }
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFCausalLMOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: TFModelInputType | None = None,
+        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
+        attention_mask: np.ndarray | tf.Tensor | None = None,
+        token_type_ids: np.ndarray | tf.Tensor | None = None,
+        position_ids: np.ndarray | tf.Tensor | None = None,
+        head_mask: np.ndarray | tf.Tensor | None = None,
+        inputs_embeds: np.ndarray | tf.Tensor | None = None,
+        labels: np.ndarray | tf.Tensor | None = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = False,
+    ) -> Union[TFCausalLMOutputWithPast, Tuple[tf.Tensor]]:
+        r"""
+        labels (`np.ndarray` or `tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+        hidden_states = transformer_outputs[0]
+        lm_logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # shift labels to the left and cut last logit token
+            shifted_logits = lm_logits[:, :-1]
+            labels = labels[:, 1:]
+            loss = self.hf_compute_loss(labels, shifted_logits)
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFCausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build([None, None, self.config.n_embd])
+
+
+@add_start_docstrings(
+    """
+    The GPT-J Model transformer with a sequence classification head on top (linear layer).
+
+    [`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT, GPT-2, GPT-Neo) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    GPTJ_START_DOCSTRING,
+)
+class TFGPTJForSequenceClassification(TFGPTJPreTrainedModel, TFSequenceClassificationLoss):
+    _keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.num_labels = config.num_labels
+        self.transformer = TFGPTJMainLayer(config, name="transformer")
+        self.score = keras.layers.Dense(
+            self.num_labels,
+            use_bias=False,
+            kernel_initializer=get_initializer(config.initializer_range),
+            name="score",
+        )
+        self.config = config
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFSequenceClassifierOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: TFModelInputType | None = None,
+        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
+        attention_mask: np.ndarray | tf.Tensor | None = None,
+        token_type_ids: np.ndarray | tf.Tensor | None = None,
+        position_ids: np.ndarray | tf.Tensor | None = None,
+        head_mask: np.ndarray | tf.Tensor | None = None,
+        inputs_embeds: np.ndarray | tf.Tensor | None = None,
+        labels: np.ndarray | tf.Tensor | None = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = False,
+    ) -> Union[TFSequenceClassifierOutputWithPast, Tuple[tf.Tensor]]:
+        r"""
+        labels (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+        logits_shape = shape_list(logits)
+        in_logits = None
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (
+                    tf.argmax(tf.cast(tf.math.equal(input_ids, self.config.pad_token_id), input_ids.dtype), axis=-1)
+                    - 1
+                )
+                sequence_lengths = tf.where(
+                    sequence_lengths >= 0,
+                    sequence_lengths,
+                    tf.cast(shape_list(input_ids[-1]), sequence_lengths.dtype) - 1,
+                )
+                in_logits = tf.gather(logits, sequence_lengths, batch_dims=1, axis=1)
+            else:
+                sequence_lengths = -1
+                logger.warning(
+                    f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                    "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+                )
+        loss = None
+
+        if labels is not None:
+            if self.config.pad_token_id is None and logits_shape[0] != 1:
+                raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+
+            if not tf.is_tensor(sequence_lengths):
+                in_logits = logits[0 : logits_shape[0], sequence_lengths]
+
+            loss = self.hf_compute_loss(tf.reshape(labels, [-1]), tf.reshape(in_logits, [-1, self.num_labels]))
+        pooled_logits = in_logits if in_logits is not None else logits
+
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFSequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "score", None) is not None:
+            with tf.name_scope(self.score.name):
+                self.score.build([None, None, self.config.n_embd])
+
+
+@add_start_docstrings(
+    """
+    The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
+    SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    GPTJ_START_DOCSTRING,
+)
+class TFGPTJForQuestionAnswering(TFGPTJPreTrainedModel, TFQuestionAnsweringLoss):
+    _keys_to_ignore_on_load_missing = [r"h.\d+.attn.masked_bias", r"h.\d+.attn.bias", r"lm_head.weight"]
+
+    def __init__(self, config, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.num_labels = config.num_labels
+        self.transformer = TFGPTJMainLayer(config, name="transformer")
+        self.qa_outputs = keras.layers.Dense(
+            self.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
+        )
+        self.config = config
+
+    @unpack_inputs
+    @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=TFQuestionAnsweringModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def call(
+        self,
+        input_ids: TFModelInputType | None = None,
+        past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
+        attention_mask: np.ndarray | tf.Tensor | None = None,
+        token_type_ids: np.ndarray | tf.Tensor | None = None,
+        position_ids: np.ndarray | tf.Tensor | None = None,
+        head_mask: np.ndarray | tf.Tensor | None = None,
+        inputs_embeds: np.ndarray | tf.Tensor | None = None,
+        start_positions: np.ndarray | tf.Tensor | None = None,
+        end_positions: np.ndarray | tf.Tensor | None = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: Optional[bool] = False,
+    ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
+        r"""
+        start_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`np.ndarray` or `tf.Tensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+        )
+        sequence_output = transformer_outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = tf.split(logits, 2, axis=-1)
+        start_logits = tf.squeeze(start_logits, axis=-1)
+        end_logits = tf.squeeze(end_logits, axis=-1)
+
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            labels = {"start_position": start_positions}
+            labels["end_position"] = end_positions
+            loss = self.hf_compute_loss(labels, (start_logits, end_logits))
+
+        if not return_dict:
+            output = (start_logits, end_logits) + transformer_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFQuestionAnsweringModelOutput(
+            loss=loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build([None, None, self.config.hidden_size])

parrot/lib/python3.10/site-packages/transformers/utils/__init__.py

@@ -0,0 +1,261 @@
+#!/usr/bin/env python
+# 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.
+
+from huggingface_hub import get_full_repo_name  # for backward compatibility
+from huggingface_hub.constants import HF_HUB_DISABLE_TELEMETRY as DISABLE_TELEMETRY  # for backward compatibility
+from packaging import version
+
+from .. import __version__
+from .backbone_utils import BackboneConfigMixin, BackboneMixin
+from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
+from .doc import (
+    add_code_sample_docstrings,
+    add_end_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    copy_func,
+    replace_return_docstrings,
+)
+from .generic import (
+    ContextManagers,
+    ExplicitEnum,
+    ModelOutput,
+    PaddingStrategy,
+    TensorType,
+    add_model_info_to_auto_map,
+    cached_property,
+    can_return_loss,
+    expand_dims,
+    find_labels,
+    flatten_dict,
+    infer_framework,
+    is_jax_tensor,
+    is_numpy_array,
+    is_tensor,
+    is_tf_symbolic_tensor,
+    is_tf_tensor,
+    is_torch_device,
+    is_torch_dtype,
+    is_torch_tensor,
+    reshape,
+    squeeze,
+    strtobool,
+    tensor_size,
+    to_numpy,
+    to_py_obj,
+    transpose,
+    working_or_temp_dir,
+)
+from .hub import (
+    CLOUDFRONT_DISTRIB_PREFIX,
+    HF_MODULES_CACHE,
+    HUGGINGFACE_CO_PREFIX,
+    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
+    PYTORCH_PRETRAINED_BERT_CACHE,
+    PYTORCH_TRANSFORMERS_CACHE,
+    S3_BUCKET_PREFIX,
+    TRANSFORMERS_CACHE,
+    TRANSFORMERS_DYNAMIC_MODULE_NAME,
+    EntryNotFoundError,
+    PushInProgress,
+    PushToHubMixin,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+    cached_file,
+    default_cache_path,
+    define_sagemaker_information,
+    download_url,
+    extract_commit_hash,
+    get_cached_models,
+    get_file_from_repo,
+    has_file,
+    http_user_agent,
+    is_offline_mode,
+    is_remote_url,
+    move_cache,
+    send_example_telemetry,
+    try_to_load_from_cache,
+)
+from .import_utils import (
+    ACCELERATE_MIN_VERSION,
+    ENV_VARS_TRUE_AND_AUTO_VALUES,
+    ENV_VARS_TRUE_VALUES,
+    TORCH_FX_REQUIRED_VERSION,
+    USE_JAX,
+    USE_TF,
+    USE_TORCH,
+    XLA_FSDPV2_MIN_VERSION,
+    DummyObject,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    ccl_version,
+    direct_transformers_import,
+    get_torch_version,
+    is_accelerate_available,
+    is_apex_available,
+    is_aqlm_available,
+    is_auto_awq_available,
+    is_auto_gptq_available,
+    is_av_available,
+    is_bitsandbytes_available,
+    is_bs4_available,
+    is_coloredlogs_available,
+    is_cv2_available,
+    is_cython_available,
+    is_datasets_available,
+    is_decord_available,
+    is_detectron2_available,
+    is_eetq_available,
+    is_essentia_available,
+    is_faiss_available,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    is_flax_available,
+    is_fsdp_available,
+    is_ftfy_available,
+    is_g2p_en_available,
+    is_galore_torch_available,
+    is_gguf_available,
+    is_hqq_available,
+    is_in_notebook,
+    is_ipex_available,
+    is_jieba_available,
+    is_jinja_available,
+    is_jumanpp_available,
+    is_kenlm_available,
+    is_keras_nlp_available,
+    is_levenshtein_available,
+    is_librosa_available,
+    is_mlx_available,
+    is_natten_available,
+    is_ninja_available,
+    is_nltk_available,
+    is_onnx_available,
+    is_openai_available,
+    is_optimum_available,
+    is_pandas_available,
+    is_peft_available,
+    is_phonemizer_available,
+    is_pretty_midi_available,
+    is_protobuf_available,
+    is_psutil_available,
+    is_py3nvml_available,
+    is_pyctcdecode_available,
+    is_pytesseract_available,
+    is_pytest_available,
+    is_pytorch_quantization_available,
+    is_quanto_available,
+    is_rjieba_available,
+    is_sacremoses_available,
+    is_safetensors_available,
+    is_sagemaker_dp_enabled,
+    is_sagemaker_mp_enabled,
+    is_scipy_available,
+    is_sentencepiece_available,
+    is_seqio_available,
+    is_sklearn_available,
+    is_soundfile_availble,
+    is_spacy_available,
+    is_speech_available,
+    is_sudachi_available,
+    is_sudachi_projection_available,
+    is_tensorflow_probability_available,
+    is_tensorflow_text_available,
+    is_tf2onnx_available,
+    is_tf_available,
+    is_timm_available,
+    is_tokenizers_available,
+    is_torch_available,
+    is_torch_bf16_available,
+    is_torch_bf16_available_on_device,
+    is_torch_bf16_cpu_available,
+    is_torch_bf16_gpu_available,
+    is_torch_compile_available,
+    is_torch_cuda_available,
+    is_torch_fp16_available_on_device,
+    is_torch_fx_available,
+    is_torch_fx_proxy,
+    is_torch_mlu_available,
+    is_torch_mps_available,
+    is_torch_neuroncore_available,
+    is_torch_npu_available,
+    is_torch_sdpa_available,
+    is_torch_tensorrt_fx_available,
+    is_torch_tf32_available,
+    is_torch_tpu_available,
+    is_torch_xla_available,
+    is_torch_xpu_available,
+    is_torchaudio_available,
+    is_torchdistx_available,
+    is_torchdynamo_available,
+    is_torchdynamo_compiling,
+    is_torchvision_available,
+    is_training_run_on_sagemaker,
+    is_vision_available,
+    requires_backends,
+    torch_only_method,
+)
+from .peft_utils import (
+    ADAPTER_CONFIG_NAME,
+    ADAPTER_SAFE_WEIGHTS_NAME,
+    ADAPTER_WEIGHTS_NAME,
+    check_peft_version,
+    find_adapter_config_file,
+)
+
+
+WEIGHTS_NAME = "pytorch_model.bin"
+WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json"
+TF2_WEIGHTS_NAME = "tf_model.h5"
+TF2_WEIGHTS_INDEX_NAME = "tf_model.h5.index.json"
+TF_WEIGHTS_NAME = "model.ckpt"
+FLAX_WEIGHTS_NAME = "flax_model.msgpack"
+FLAX_WEIGHTS_INDEX_NAME = "flax_model.msgpack.index.json"
+SAFE_WEIGHTS_NAME = "model.safetensors"
+SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json"
+CONFIG_NAME = "config.json"
+FEATURE_EXTRACTOR_NAME = "preprocessor_config.json"
+IMAGE_PROCESSOR_NAME = FEATURE_EXTRACTOR_NAME
+PROCESSOR_NAME = "processor_config.json"
+GENERATION_CONFIG_NAME = "generation_config.json"
+MODEL_CARD_NAME = "modelcard.json"
+
+SENTENCEPIECE_UNDERLINE = "▁"
+SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility
+
+MULTIPLE_CHOICE_DUMMY_INPUTS = [
+    [[0, 1, 0, 1], [1, 0, 0, 1]]
+] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
+DUMMY_INPUTS = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
+DUMMY_MASK = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
+
+
+def check_min_version(min_version):
+    if version.parse(__version__) < version.parse(min_version):
+        if "dev" in min_version:
+            error_message = (
+                "This example requires a source install from HuggingFace Transformers (see "
+                "`https://huggingface.co/docs/transformers/installation#install-from-source`),"
+            )
+        else:
+            error_message = f"This example requires a minimum version of {min_version},"
+        error_message += f" but the version found is {__version__}.\n"
+        raise ImportError(
+            error_message
+            + "Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other "
+            "versions of HuggingFace Transformers."
+        )

parrot/lib/python3.10/site-packages/transformers/utils/backbone_utils.py

@@ -0,0 +1,350 @@
+# 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.
+
+""" Collection of utils to be used by backbones and their components."""
+
+import enum
+import inspect
+from typing import Iterable, List, Optional, Tuple, Union
+
+
+class BackboneType(enum.Enum):
+    TIMM = "timm"
+    TRANSFORMERS = "transformers"
+
+
+def verify_out_features_out_indices(
+    out_features: Optional[Iterable[str]], out_indices: Optional[Iterable[int]], stage_names: Optional[Iterable[str]]
+):
+    """
+    Verify that out_indices and out_features are valid for the given stage_names.
+    """
+    if stage_names is None:
+        raise ValueError("Stage_names must be set for transformers backbones")
+
+    if out_features is not None:
+        if not isinstance(out_features, (list,)):
+            raise ValueError(f"out_features must be a list got {type(out_features)}")
+        if any(feat not in stage_names for feat in out_features):
+            raise ValueError(f"out_features must be a subset of stage_names: {stage_names} got {out_features}")
+        if len(out_features) != len(set(out_features)):
+            raise ValueError(f"out_features must not contain any duplicates, got {out_features}")
+        if out_features != (sorted_feats := [feat for feat in stage_names if feat in out_features]):
+            raise ValueError(
+                f"out_features must be in the same order as stage_names, expected {sorted_feats} got {out_features}"
+            )
+
+    if out_indices is not None:
+        if not isinstance(out_indices, (list, tuple)):
+            raise ValueError(f"out_indices must be a list or tuple, got {type(out_indices)}")
+        # Convert negative indices to their positive equivalent: [-1,] -> [len(stage_names) - 1,]
+        positive_indices = tuple(idx % len(stage_names) if idx < 0 else idx for idx in out_indices)
+        if any(idx for idx in positive_indices if idx not in range(len(stage_names))):
+            raise ValueError(f"out_indices must be valid indices for stage_names {stage_names}, got {out_indices}")
+        if len(positive_indices) != len(set(positive_indices)):
+            msg = f"out_indices must not contain any duplicates, got {out_indices}"
+            msg += f"(equivalent to {positive_indices}))" if positive_indices != out_indices else ""
+            raise ValueError(msg)
+        if positive_indices != tuple(sorted(positive_indices)):
+            sorted_negative = tuple(idx for _, idx in sorted(zip(positive_indices, out_indices), key=lambda x: x[0]))
+            raise ValueError(
+                f"out_indices must be in the same order as stage_names, expected {sorted_negative} got {out_indices}"
+            )
+
+    if out_features is not None and out_indices is not None:
+        if len(out_features) != len(out_indices):
+            raise ValueError("out_features and out_indices should have the same length if both are set")
+        if out_features != [stage_names[idx] for idx in out_indices]:
+            raise ValueError("out_features and out_indices should correspond to the same stages if both are set")
+
+
+def _align_output_features_output_indices(
+    out_features: Optional[List[str]],
+    out_indices: Optional[Union[List[int], Tuple[int]]],
+    stage_names: List[str],
+):
+    """
+    Finds the corresponding `out_features` and `out_indices` for the given `stage_names`.
+
+    The logic is as follows:
+        - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the
+        `out_indices`.
+        - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the
+        `out_features`.
+        - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.
+        - `out_indices` and `out_features` set: input `out_indices` and `out_features` are returned.
+
+    Args:
+        out_features (`List[str]`): The names of the features for the backbone to output.
+        out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.
+        stage_names (`List[str]`): The names of the stages of the backbone.
+    """
+    if out_indices is None and out_features is None:
+        out_indices = [len(stage_names) - 1]
+        out_features = [stage_names[-1]]
+    elif out_indices is None and out_features is not None:
+        out_indices = [stage_names.index(layer) for layer in out_features]
+    elif out_features is None and out_indices is not None:
+        out_features = [stage_names[idx] for idx in out_indices]
+    return out_features, out_indices
+
+
+def get_aligned_output_features_output_indices(
+    out_features: Optional[List[str]],
+    out_indices: Optional[Union[List[int], Tuple[int]]],
+    stage_names: List[str],
+) -> Tuple[List[str], List[int]]:
+    """
+    Get the `out_features` and `out_indices` so that they are aligned.
+
+    The logic is as follows:
+        - `out_features` not set, `out_indices` set: `out_features` is set to the `out_features` corresponding to the
+        `out_indices`.
+        - `out_indices` not set, `out_features` set: `out_indices` is set to the `out_indices` corresponding to the
+        `out_features`.
+        - `out_indices` and `out_features` not set: `out_indices` and `out_features` are set to the last stage.
+        - `out_indices` and `out_features` set: they are verified to be aligned.
+
+    Args:
+        out_features (`List[str]`): The names of the features for the backbone to output.
+        out_indices (`List[int]` or `Tuple[int]`): The indices of the features for the backbone to output.
+        stage_names (`List[str]`): The names of the stages of the backbone.
+    """
+    # First verify that the out_features and out_indices are valid
+    verify_out_features_out_indices(out_features=out_features, out_indices=out_indices, stage_names=stage_names)
+    output_features, output_indices = _align_output_features_output_indices(
+        out_features=out_features, out_indices=out_indices, stage_names=stage_names
+    )
+    # Verify that the aligned out_features and out_indices are valid
+    verify_out_features_out_indices(out_features=output_features, out_indices=output_indices, stage_names=stage_names)
+    return output_features, output_indices
+
+
+class BackboneMixin:
+    backbone_type: Optional[BackboneType] = None
+
+    def _init_timm_backbone(self, config) -> None:
+        """
+        Initialize the backbone model from timm The backbone must already be loaded to self._backbone
+        """
+        if getattr(self, "_backbone", None) is None:
+            raise ValueError("self._backbone must be set before calling _init_timm_backbone")
+
+        # These will diagree with the defaults for the transformers models e.g. for resnet50
+        # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
+        # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
+        self.stage_names = [stage["module"] for stage in self._backbone.feature_info.info]
+        self.num_features = [stage["num_chs"] for stage in self._backbone.feature_info.info]
+        out_indices = self._backbone.feature_info.out_indices
+        out_features = self._backbone.feature_info.module_name()
+
+        # We verify the out indices and out features are valid
+        verify_out_features_out_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
+        )
+        self._out_features, self._out_indices = out_features, out_indices
+
+    def _init_transformers_backbone(self, config) -> None:
+        stage_names = getattr(config, "stage_names")
+        out_features = getattr(config, "out_features", None)
+        out_indices = getattr(config, "out_indices", None)
+
+        self.stage_names = stage_names
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=out_indices, stage_names=stage_names
+        )
+        # Number of channels for each stage. This is set in the transformer backbone model init
+        self.num_features = None
+
+    def _init_backbone(self, config) -> None:
+        """
+        Method to initialize the backbone. This method is called by the constructor of the base class after the
+        pretrained model weights have been loaded.
+        """
+        self.config = config
+
+        self.use_timm_backbone = getattr(config, "use_timm_backbone", False)
+        self.backbone_type = BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS
+
+        if self.backbone_type == BackboneType.TIMM:
+            self._init_timm_backbone(config)
+        elif self.backbone_type == BackboneType.TRANSFORMERS:
+            self._init_transformers_backbone(config)
+        else:
+            raise ValueError(f"backbone_type {self.backbone_type} not supported.")
+
+    @property
+    def out_features(self):
+        return self._out_features
+
+    @out_features.setter
+    def out_features(self, out_features: List[str]):
+        """
+        Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.
+        """
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=None, stage_names=self.stage_names
+        )
+
+    @property
+    def out_indices(self):
+        return self._out_indices
+
+    @out_indices.setter
+    def out_indices(self, out_indices: Union[Tuple[int], List[int]]):
+        """
+        Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.
+        """
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=None, out_indices=out_indices, stage_names=self.stage_names
+        )
+
+    @property
+    def out_feature_channels(self):
+        # the current backbones will output the number of channels for each stage
+        # even if that stage is not in the out_features list.
+        return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names)}
+
+    @property
+    def channels(self):
+        return [self.out_feature_channels[name] for name in self.out_features]
+
+    def forward_with_filtered_kwargs(self, *args, **kwargs):
+        signature = dict(inspect.signature(self.forward).parameters)
+        filtered_kwargs = {k: v for k, v in kwargs.items() if k in signature}
+        return self(*args, **filtered_kwargs)
+
+    def forward(
+        self,
+        pixel_values,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        raise NotImplementedError("This method should be implemented by the derived class.")
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to
+        include the `out_features` and `out_indices` attributes.
+        """
+        output = super().to_dict()
+        output["out_features"] = output.pop("_out_features")
+        output["out_indices"] = output.pop("_out_indices")
+        return output
+
+
+class BackboneConfigMixin:
+    """
+    A Mixin to support handling the `out_features` and `out_indices` attributes for the backbone configurations.
+    """
+
+    @property
+    def out_features(self):
+        return self._out_features
+
+    @out_features.setter
+    def out_features(self, out_features: List[str]):
+        """
+        Set the out_features attribute. This will also update the out_indices attribute to match the new out_features.
+        """
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=out_features, out_indices=None, stage_names=self.stage_names
+        )
+
+    @property
+    def out_indices(self):
+        return self._out_indices
+
+    @out_indices.setter
+    def out_indices(self, out_indices: Union[Tuple[int], List[int]]):
+        """
+        Set the out_indices attribute. This will also update the out_features attribute to match the new out_indices.
+        """
+        self._out_features, self._out_indices = get_aligned_output_features_output_indices(
+            out_features=None, out_indices=out_indices, stage_names=self.stage_names
+        )
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default `to_dict()` from `PretrainedConfig` to
+        include the `out_features` and `out_indices` attributes.
+        """
+        output = super().to_dict()
+        output["out_features"] = output.pop("_out_features")
+        output["out_indices"] = output.pop("_out_indices")
+        return output
+
+
+def load_backbone(config):
+    """
+    Loads the backbone model from a config object.
+
+    If the config is from the backbone model itself, then we return a backbone model with randomly initialized
+    weights.
+
+    If the config is from the parent model of the backbone model itself, then we load the pretrained backbone weights
+    if specified.
+    """
+    from transformers import AutoBackbone, AutoConfig
+
+    backbone_config = getattr(config, "backbone_config", None)
+    use_timm_backbone = getattr(config, "use_timm_backbone", None)
+    use_pretrained_backbone = getattr(config, "use_pretrained_backbone", None)
+    backbone_checkpoint = getattr(config, "backbone", None)
+    backbone_kwargs = getattr(config, "backbone_kwargs", None)
+
+    backbone_kwargs = {} if backbone_kwargs is None else backbone_kwargs
+
+    if backbone_kwargs and backbone_config is not None:
+        raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
+
+    # If there is a backbone_config and a backbone checkpoint, and use_pretrained_backbone=False then the desired
+    # behaviour is ill-defined: do you want to load from the checkpoint's config or the backbone_config?
+    if backbone_config is not None and backbone_checkpoint is not None and use_pretrained_backbone is not None:
+        raise ValueError("Cannot specify both config.backbone_config and config.backbone")
+
+    # If any of thhe following are set, then the config passed in is from a model which contains a backbone.
+    if (
+        backbone_config is None
+        and use_timm_backbone is None
+        and backbone_checkpoint is None
+        and backbone_checkpoint is None
+    ):
+        return AutoBackbone.from_config(config=config, **backbone_kwargs)
+
+    # config from the parent model that has a backbone
+    if use_timm_backbone:
+        if backbone_checkpoint is None:
+            raise ValueError("config.backbone must be set if use_timm_backbone is True")
+        # Because of how timm backbones were originally added to models, we need to pass in use_pretrained_backbone
+        # to determine whether to load the pretrained weights.
+        backbone = AutoBackbone.from_pretrained(
+            backbone_checkpoint,
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            **backbone_kwargs,
+        )
+    elif use_pretrained_backbone:
+        if backbone_checkpoint is None:
+            raise ValueError("config.backbone must be set if use_pretrained_backbone is True")
+        backbone = AutoBackbone.from_pretrained(backbone_checkpoint, **backbone_kwargs)
+    else:
+        if backbone_config is None and backbone_checkpoint is None:
+            raise ValueError("Either config.backbone_config or config.backbone must be set")
+        if backbone_config is None:
+            backbone_config = AutoConfig.from_pretrained(backbone_checkpoint, **backbone_kwargs)
+        backbone = AutoBackbone.from_config(config=backbone_config)
+    return backbone

parrot/lib/python3.10/site-packages/transformers/utils/bitsandbytes.py

@@ -0,0 +1,28 @@
+# Copyright 2023 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.
+import warnings
+
+
+warnings.warn(
+    "transformers.utils.bitsandbytes module is deprecated and will be removed in a future version. Please import bitsandbytes modules directly from transformers.integrations",
+    FutureWarning,
+)
+
+from ..integrations import (  # noqa
+    get_keys_to_not_convert,
+    replace_8bit_linear,
+    replace_with_bnb_linear,
+    set_module_8bit_tensor_to_device,
+    set_module_quantized_tensor_to_device,
+)

parrot/lib/python3.10/site-packages/transformers/utils/constants.py

@@ -0,0 +1,6 @@
+IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]
+IMAGENET_STANDARD_MEAN = [0.5, 0.5, 0.5]
+IMAGENET_STANDARD_STD = [0.5, 0.5, 0.5]
+OPENAI_CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073]
+OPENAI_CLIP_STD = [0.26862954, 0.26130258, 0.27577711]