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venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/AUTHORS

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+Original author of astor/codegen.py:
+* Armin Ronacher <armin.ronacher@active-4.com>
+
+And with some modifications based on Armin's code:
+* Paul Dubs <paul.dubs@gmail.com>
+
+* Berker Peksag <berker.peksag@gmail.com>
+* Patrick Maupin <pmaupin@gmail.com>
+* Abhishek L <abhishek.lekshmanan@gmail.com>
+* Bob Tolbert <bob@eyesopen.com>
+* Whyzgeek <whyzgeek@gmail.com>
+* Zack M. Davis <code@zackmdavis.net>
+* Ryan Gonzalez <rymg19@gmail.com>
+* Lenny Truong <leonardtruong@protonmail.com>
+* Radomír Bosák <radomir.bosak@gmail.com>
+* Kodi Arfer <git@arfer.net>
+* Felix Yan <felixonmars@archlinux.org>
+* Chris Rink <chrisrink10@gmail.com>
+* Batuhan Taskaya <batuhanosmantaskaya@gmail.com>

venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/INSTALLER

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+pip

venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/LICENSE

@@ -0,0 +1,29 @@
+Copyright (c) 2012, Patrick Maupin
+Copyright (c) 2013, Berker Peksag
+Copyright (c) 2008, Armin Ronacher
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+this list of conditions and the following disclaimer in the documentation and/or
+other materials provided with the distribution.
+
+3. 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 COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/METADATA

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+Metadata-Version: 2.1
+Name: astor
+Version: 0.8.1
+Summary: Read/rewrite/write Python ASTs
+Home-page: https://github.com/berkerpeksag/astor
+Author: Patrick Maupin
+Author-email: pmaupin@gmail.com
+License: BSD-3-Clause
+Keywords: ast,codegen,PEP 8
+Platform: Independent
+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: Programming Language :: Python
+Classifier: Programming Language :: Python :: 2
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.4
+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 :: Implementation
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Code Generators
+Classifier: Topic :: Software Development :: Compilers
+Requires-Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7
+
+=============================
+astor -- AST observe/rewrite
+=============================
+
+:PyPI: https://pypi.org/project/astor/
+:Documentation: https://astor.readthedocs.io
+:Source: https://github.com/berkerpeksag/astor
+:License: 3-clause BSD
+:Build status:
+    .. image:: https://secure.travis-ci.org/berkerpeksag/astor.svg
+        :alt: Travis CI
+        :target: https://travis-ci.org/berkerpeksag/astor/
+
+astor is designed to allow easy manipulation of Python source via the AST.
+
+There are some other similar libraries, but astor focuses on the following areas:
+
+- Round-trip an AST back to Python [1]_:
+
+  - Modified AST doesn't need linenumbers, ctx, etc. or otherwise
+    be directly compileable for the round-trip to work.
+  - Easy to read generated code as, well, code
+  - Can round-trip two different source trees to compare for functional
+    differences, using the astor.rtrip tool (for example, after PEP8 edits).
+
+- Dump pretty-printing of AST
+
+  - Harder to read than round-tripped code, but more accurate to figure out what
+    is going on.
+
+  - Easier to read than dump from built-in AST module
+
+- Non-recursive treewalk
+
+  - Sometimes you want a recursive treewalk (and astor supports that, starting
+    at any node on the tree), but sometimes you don't need to do that.  astor
+    doesn't require you to explicitly visit sub-nodes unless you want to:
+
+  - You can add code that executes before a node's children are visited, and/or
+  - You can add code that executes after a node's children are visited, and/or
+  - You can add code that executes and keeps the node's children from being
+    visited (and optionally visit them yourself via a recursive call)
+
+  - Write functions to access the tree based on object names and/or attribute names
+  - Enjoy easy access to parent node(s) for tree rewriting
+
+.. [1]
+    The decompilation back to Python is based on code originally written
+    by Armin Ronacher.  Armin's code was well-structured, but failed on
+    some obscure corner cases of the Python language (and even more corner
+    cases when the AST changed on different versions of Python), and its
+    output arguably had cosmetic issues -- for example, it produced
+    parentheses even in some cases where they were not needed, to
+    avoid having to reason about precedence.
+
+    Other derivatives of Armin's code are floating around, and typically
+    have fixes for a few corner cases that happened to be noticed by the
+    maintainers, but most of them have not been tested as thoroughly as
+    astor.  One exception may be the version of codegen
+    `maintained at github by CensoredUsername`__.  This has been tested
+    to work properly on Python 2.7 using astor's test suite, and, as it
+    is a single source file, it may be easier to drop into some applications
+    that do not require astor's other features or Python 3.x compatibility.
+
+__ https://github.com/CensoredUsername/codegen
+
+

venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/RECORD

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venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: bdist_wheel (0.33.6)
+Root-Is-Purelib: true
+Tag: py2-none-any
+Tag: py3-none-any
+

venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/top_level.txt

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venv/lib/python3.10/site-packages/astor-0.8.1.dist-info/zip-safe

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venv/lib/python3.10/site-packages/boto3-1.34.113.dist-info/INSTALLER

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

venv/lib/python3.10/site-packages/boto3-1.34.113.dist-info/LICENSE

@@ -0,0 +1,177 @@
+
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+                           Version 2.0, January 2004
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+      the terms of any separate license agreement you may have executed
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venv/lib/python3.10/site-packages/boto3-1.34.113.dist-info/METADATA

@@ -0,0 +1,183 @@
+Metadata-Version: 2.1
+Name: boto3
+Version: 1.34.113
+Summary: The AWS SDK for Python
+Home-page: https://github.com/boto/boto3
+Author: Amazon Web Services
+License: Apache License 2.0
+Project-URL: Documentation, https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
+Project-URL: Source, https://github.com/boto/boto3
+Platform: UNKNOWN
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Natural Language :: English
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+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
+Requires-Python: >= 3.8
+License-File: LICENSE
+License-File: NOTICE
+Requires-Dist: botocore (<1.35.0,>=1.34.113)
+Requires-Dist: jmespath (<2.0.0,>=0.7.1)
+Requires-Dist: s3transfer (<0.11.0,>=0.10.0)
+Provides-Extra: crt
+Requires-Dist: botocore[crt] (<2.0a0,>=1.21.0) ; extra == 'crt'
+
+===============================
+Boto3 - The AWS SDK for Python
+===============================
+
+|Version| |Python| |License|
+
+Boto3 is the Amazon Web Services (AWS) Software Development Kit (SDK) for
+Python, which allows Python developers to write software that makes use
+of services like Amazon S3 and Amazon EC2. You can find the latest, most
+up to date, documentation at our `doc site`_, including a list of
+services that are supported.
+
+Boto3 is maintained and published by `Amazon Web Services`_.
+
+Boto (pronounced boh-toh) was named after the fresh water dolphin native to the Amazon river. The name was chosen by the author of the original Boto library, Mitch Garnaat, as a reference to the company.
+
+Notices
+-------
+
+On 2023-12-13, support for Python 3.7 ended for Boto3. This follows the
+Python Software Foundation `end of support <https://peps.python.org/pep-0537/#lifespan>`__
+for the runtime which occurred on 2023-06-27.
+For more information, see this `blog post <https://aws.amazon.com/blogs/developer/python-support-policy-updates-for-aws-sdks-and-tools/>`__.
+
+.. _boto: https://docs.pythonboto.org/
+.. _`doc site`: https://boto3.amazonaws.com/v1/documentation/api/latest/index.html
+.. _`Amazon Web Services`: https://aws.amazon.com/what-is-aws/
+.. |Python| image:: https://img.shields.io/pypi/pyversions/boto3.svg?style=flat
+    :target: https://pypi.python.org/pypi/boto3/
+    :alt: Python Versions
+.. |Version| image:: http://img.shields.io/pypi/v/boto3.svg?style=flat
+    :target: https://pypi.python.org/pypi/boto3/
+    :alt: Package Version
+.. |License| image:: http://img.shields.io/pypi/l/boto3.svg?style=flat
+    :target: https://github.com/boto/boto3/blob/develop/LICENSE
+    :alt: License
+
+Getting Started
+---------------
+Assuming that you have a supported version of Python installed, you can first
+set up your environment with:
+
+.. code-block:: sh
+
+    $ python -m venv .venv
+    ...
+    $ . .venv/bin/activate
+
+Then, you can install boto3 from PyPI with:
+
+.. code-block:: sh
+
+    $ python -m pip install boto3
+
+or install from source with:
+
+.. code-block:: sh
+
+    $ git clone https://github.com/boto/boto3.git
+    $ cd boto3
+    $ python -m pip install -r requirements.txt
+    $ python -m pip install -e .
+
+
+Using Boto3
+~~~~~~~~~~~~~~
+After installing boto3
+
+Next, set up credentials (in e.g. ``~/.aws/credentials``):
+
+.. code-block:: ini
+
+    [default]
+    aws_access_key_id = YOUR_KEY
+    aws_secret_access_key = YOUR_SECRET
+
+Then, set up a default region (in e.g. ``~/.aws/config``):
+
+.. code-block:: ini
+
+   [default]
+   region=us-east-1
+
+Other credential configuration methods can be found `here <https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html>`__
+
+Then, from a Python interpreter:
+
+.. code-block:: python
+
+    >>> import boto3
+    >>> s3 = boto3.resource('s3')
+    >>> for bucket in s3.buckets.all():
+            print(bucket.name)
+
+Running Tests
+~~~~~~~~~~~~~
+You can run tests in all supported Python versions using ``tox``. By default,
+it will run all of the unit and functional tests, but you can also specify your own
+``pytest`` options. Note that this requires that you have all supported
+versions of Python installed, otherwise you must pass ``-e`` or run the
+``pytest`` command directly:
+
+.. code-block:: sh
+
+    $ tox
+    $ tox -- unit/test_session.py
+    $ tox -e py26,py33 -- integration/
+
+You can also run individual tests with your default Python version:
+
+.. code-block:: sh
+
+    $ pytest tests/unit
+
+
+Getting Help
+------------
+
+We use GitHub issues for tracking bugs and feature requests and have limited
+bandwidth to address them. Please use these community resources for getting
+help:
+
+* Ask a question on `Stack Overflow <https://stackoverflow.com/>`__ and tag it with `boto3 <https://stackoverflow.com/questions/tagged/boto3>`__
+* Open a support ticket with `AWS Support <https://console.aws.amazon.com/support/home#/>`__
+* If it turns out that you may have found a bug, please `open an issue <https://github.com/boto/boto3/issues/new>`__
+
+
+Contributing
+------------
+
+We value feedback and contributions from our community. Whether it's a bug report, new feature, correction, or additional documentation, we welcome your issues and pull requests. Please read through this `CONTRIBUTING <https://github.com/boto/boto3/blob/develop/CONTRIBUTING.rst>`__ document before submitting any issues or pull requests to ensure we have all the necessary information to effectively respond to your contribution.
+
+
+Maintenance and Support for SDK Major Versions
+----------------------------------------------
+
+Boto3 was made generally available on 06/22/2015 and is currently in the full support phase of the availability life cycle.
+
+For information about maintenance and support for SDK major versions and their underlying dependencies, see the following in the AWS SDKs and Tools Shared Configuration and Credentials Reference Guide:
+
+* `AWS SDKs and Tools Maintenance Policy <https://docs.aws.amazon.com/sdkref/latest/guide/maint-policy.html>`__
+* `AWS SDKs and Tools Version Support Matrix <https://docs.aws.amazon.com/sdkref/latest/guide/version-support-matrix.html>`__
+
+
+More Resources
+--------------
+
+* `NOTICE <https://github.com/boto/boto3/blob/develop/NOTICE>`__
+* `Changelog <https://github.com/boto/boto3/blob/develop/CHANGELOG.rst>`__
+* `License <https://github.com/boto/boto3/blob/develop/LICENSE>`__
+
+

venv/lib/python3.10/site-packages/boto3-1.34.113.dist-info/NOTICE

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+boto3
+Copyright 2013-2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.

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venv/lib/python3.10/site-packages/boto3-1.34.113.dist-info/WHEEL

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+Wheel-Version: 1.0
+Generator: bdist_wheel (0.37.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

venv/lib/python3.10/site-packages/boto3-1.34.113.dist-info/top_level.txt

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+boto3

venv/lib/python3.10/site-packages/hf_olmo/__init__.py

@@ -0,0 +1,3 @@
+from .configuration_olmo import OLMoConfig
+from .modeling_olmo import OLMoForCausalLM
+from .tokenization_olmo_fast import OLMoTokenizerFast

venv/lib/python3.10/site-packages/hf_olmo/configuration_olmo.py

@@ -0,0 +1,44 @@
+"""
+OLMo configuration
+"""
+
+from transformers import AutoConfig, PretrainedConfig
+from transformers.utils import logging
+
+from olmo.config import ModelConfig
+
+logger = logging.get_logger(__name__)
+
+
+class OLMoConfig(PretrainedConfig):
+    model_type = "olmo"
+    keys_to_ignore_at_inference = ["past_key_values"]  # TODO: confirm
+
+    def __init__(self, use_cache: bool = False, **kwargs):
+        model_config = ModelConfig()
+        all_kwargs = model_config.asdict()
+        all_kwargs.update(kwargs)
+        all_kwargs.update({"use_cache": use_cache})
+        all_kwargs.update(
+            {
+                "architectures": all_kwargs.get("architectures", ["OlmoModelForCausalLM"])
+                or ["OlmoModelForCausalLM"]
+            }
+        )
+        super().__init__(**all_kwargs)
+
+    @property
+    def num_attention_heads(self):
+        return self.n_heads
+
+    @property
+    def num_hidden_layers(self):
+        return self.n_layers
+
+    @property
+    def hidden_size(self):
+        return self.d_model
+
+
+# Register the config class so that it is available for transformer pipelines, auto-loading etc.
+AutoConfig.register("olmo", OLMoConfig)

venv/lib/python3.10/site-packages/hf_olmo/convert_olmo_to_hf.py

@@ -0,0 +1,104 @@
+import argparse
+import logging
+import os
+import shutil
+
+from hf_olmo.configuration_olmo import OLMoConfig
+from hf_olmo.tokenization_olmo_fast import OLMoTokenizerFast
+from olmo import ModelConfig, Tokenizer
+
+logger = logging.getLogger(__name__)
+
+
+def write_config(checkpoint_dir: str):
+    # save config as HF config
+    from cached_path import cached_path
+
+    logger.info(f"Loading checkpoint from {checkpoint_dir}")
+
+    config_path = cached_path(os.path.join(checkpoint_dir, "config.yaml"))
+    model_config = ModelConfig.load(config_path, key="model")
+    config_kwargs = model_config.asdict()
+    config_kwargs["use_cache"] = True
+    config = OLMoConfig(**config_kwargs)
+
+    logger.info(f"Saving HF-compatible config to {os.path.join(checkpoint_dir, 'config.json')}")
+    config.save_pretrained(checkpoint_dir)
+
+
+def write_model(checkpoint_dir: str, soft_link: bool = True):
+    if soft_link:
+        try:
+            os.symlink("model.pt", os.path.join(checkpoint_dir, "pytorch_model.bin"))
+        except FileExistsError:
+            pass
+    else:
+        if not os.path.exists(os.path.join(checkpoint_dir, "pytorch_model.bin")):
+            os.rename(os.path.join(checkpoint_dir, "model.pt"), os.path.join(checkpoint_dir, "pytorch_model.bin"))
+
+
+def write_tokenizer(checkpoint_dir: str):
+    tokenizer_raw = Tokenizer.from_checkpoint(checkpoint_dir)
+    tokenizer = OLMoTokenizerFast(
+        tokenizer_object=tokenizer_raw.base_tokenizer,
+        truncation=tokenizer_raw.truncate_direction,
+        max_length=tokenizer_raw.truncate_to,
+        eos_token=tokenizer_raw.decode([tokenizer_raw.eos_token_id], skip_special_tokens=False),
+    )
+    tokenizer.model_input_names = ["input_ids", "attention_mask"]
+    tokenizer.pad_token_id = tokenizer_raw.pad_token_id
+    tokenizer.eos_token_id = tokenizer_raw.eos_token_id
+
+    tokenizer.save_pretrained(checkpoint_dir)
+
+
+def download_remote_checkpoint_and_convert_to_hf(checkpoint_dir: str, local_dir: str):
+    from cached_path import cached_path
+
+    model_name = os.path.basename(checkpoint_dir)
+    local_model_path = os.path.join(local_dir, model_name)
+    os.makedirs(local_model_path, exist_ok=True)
+
+    model_files = ["model.pt", "config.yaml"]  # , "optim.pt", "other.pt"]
+    for filename in model_files:
+        final_location = os.path.join(local_model_path, filename)
+        if not os.path.exists(final_location):
+            remote_file = os.path.join(checkpoint_dir, filename)
+            logger.debug(f"Downloading file {filename}")
+            cached_file = cached_path(remote_file)
+            shutil.copy(cached_file, final_location)
+            logger.debug(f"File at {final_location}")
+        else:
+            logger.info(f"File already present at {final_location}")
+
+    write_config(local_model_path)
+    write_model(local_model_path, soft_link=False)
+    write_tokenizer(local_model_path)
+    return local_model_path
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Adds a config.json to the checkpoint directory, and creates pytorch_model.bin, "
+        "making it easier to load weights as HF models."
+    )
+    parser.add_argument(
+        "--checkpoint-dir",
+        help="Location of OLMo checkpoint.",
+    )
+
+    parser.add_argument(
+        "--ignore-olmo-compatibility",
+        action="store_true",
+        help="Ignore compatibility with the olmo codebase. "
+        "This will rename model.pt --> pytorch_model.bin instead of creating a symlink.",
+    )
+
+    args = parser.parse_args()
+    write_config(checkpoint_dir=args.checkpoint_dir)
+    write_model(checkpoint_dir=args.checkpoint_dir, soft_link=not args.ignore_olmo_compatibility)
+    write_tokenizer(checkpoint_dir=args.checkpoint_dir)
+
+
+if __name__ == "__main__":
+    main()

venv/lib/python3.10/site-packages/hf_olmo/modeling_olmo.py

@@ -0,0 +1,142 @@
+from typing import List, Optional, Tuple, Union
+
+import torch
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.auto import AutoModelForCausalLM
+
+from olmo.config import ModelConfig
+from olmo.model import Olmo
+
+from .configuration_olmo import OLMoConfig
+
+
+def create_model_config_from_pretrained_config(config: OLMoConfig):
+    """
+    Utility function
+    """
+
+    kwargs = {}
+    for key in ModelConfig.__match_args__:
+        kwargs[key] = getattr(config, key)
+
+    model_config = ModelConfig(**kwargs)
+    return model_config
+
+
+class OLMoForCausalLM(PreTrainedModel):
+    """
+    Extremely barebones HF model wrapper.
+    """
+
+    config_class = OLMoConfig
+    base_model_prefix = "model"
+
+    def __init__(self, config: OLMoConfig, model: Optional[Olmo] = None):
+        super().__init__(config)
+
+        if not model:
+            model_config = create_model_config_from_pretrained_config(config)
+            self.model = Olmo(model_config, init_params=True)
+        else:
+            self.model = model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+        )
+
+        logits = outputs.logits
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = torch.nn.CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.embedding_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.attn_key_values,
+        )
+
+    def can_generate(self) -> bool:
+        return True
+
+    def prepare_inputs_for_generation(
+        self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple]] = None, **kwargs
+    ):
+        if past_key_values:
+            # This is because we want the model to only process the last generated token.
+            input_ids = input_ids[:, -1:]
+        model_inputs = {"input_ids": input_ids, "past_key_values": past_key_values}
+
+        model_inputs.update(kwargs)
+        model_inputs["use_cache"] = kwargs.pop("use_cache", self.config.use_cache)
+        return model_inputs
+
+    # TODO: these are required to make the implementation complete.
+    # def resize_position_embeddings(self, new_num_position_embeddings: int):
+    #     pass
+    #
+    # def get_position_embeddings(self) -> Union[nn.Embedding, Tuple[nn.Embedding]]:
+    #     pass
+    #
+    # def _reorder_cache(self, past_key_values, beam_idx):
+    #     pass
+
+    def get_input_embeddings(self) -> torch.nn.Module:
+        return self.model.transformer.wte
+
+    def set_input_embeddings(self, value: torch.nn.Module):
+        self.model.transformer.wte = value
+
+    def get_output_embeddings(self):
+        if self.config.weight_tying:
+            return self.model.transformer.wte
+        else:
+            return self.model.transformer.ff_out
+
+    def set_output_embeddings(self, value: torch.nn.Module):
+        if self.config.weight_tying:
+            self.model.transformer.wte = value
+        else:
+            self.model.transformer.ff_out = value
+
+    def tie_weights(self):
+        if self.config.weight_tying:
+            self.model.transformer.ff_out = self.model.transformer.wte
+
+
+# Register the model so that it is available for transformer pipelines, auto-loading, etc.
+AutoModelForCausalLM.register(OLMoConfig, OLMoForCausalLM)

venv/lib/python3.10/site-packages/hf_olmo/requirements.txt

@@ -0,0 +1 @@
+transformers

venv/lib/python3.10/site-packages/hf_olmo/tokenization_olmo_fast.py

@@ -0,0 +1,16 @@
+from transformers import AutoTokenizer, PreTrainedTokenizerFast
+
+from hf_olmo.configuration_olmo import OLMoConfig
+
+
+class OLMoTokenizerFast(PreTrainedTokenizerFast):
+    # Note: Olmo's tokenizer is already a wrapper around huggingface. This is potentially unnecessary.
+    pass
+
+    # def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+    #     # This is required to make the implementation complete.
+    #     pass
+
+
+# Register the tokenizer class so that it is available for transformer pipelines, auto-loading etc.
+AutoTokenizer.register(OLMoConfig, fast_tokenizer_class=OLMoTokenizerFast)

venv/lib/python3.10/site-packages/networkx/__init__.py

@@ -0,0 +1,53 @@
+"""
+NetworkX
+========
+
+NetworkX is a Python package for the creation, manipulation, and study of the
+structure, dynamics, and functions of complex networks.
+
+See https://networkx.org for complete documentation.
+"""
+
+__version__ = "3.4.2"
+
+
+# These are imported in order as listed
+from networkx.lazy_imports import _lazy_import
+
+from networkx.exception import *
+
+from networkx import utils
+from networkx.utils import _clear_cache, _dispatchable
+
+# load_and_call entry_points, set configs
+config = utils.backends._set_configs_from_environment()
+utils.config = utils.configs.config = config  # type: ignore[attr-defined]
+
+from networkx import classes
+from networkx.classes import filters
+from networkx.classes import *
+
+from networkx import convert
+from networkx.convert import *
+
+from networkx import convert_matrix
+from networkx.convert_matrix import *
+
+from networkx import relabel
+from networkx.relabel import *
+
+from networkx import generators
+from networkx.generators import *
+
+from networkx import readwrite
+from networkx.readwrite import *
+
+# Need to test with SciPy, when available
+from networkx import algorithms
+from networkx.algorithms import *
+
+from networkx import linalg
+from networkx.linalg import *
+
+from networkx import drawing
+from networkx.drawing import *

venv/lib/python3.10/site-packages/networkx/algorithms/__init__.py

@@ -0,0 +1,133 @@
+from networkx.algorithms.assortativity import *
+from networkx.algorithms.asteroidal import *
+from networkx.algorithms.boundary import *
+from networkx.algorithms.broadcasting import *
+from networkx.algorithms.bridges import *
+from networkx.algorithms.chains import *
+from networkx.algorithms.centrality import *
+from networkx.algorithms.chordal import *
+from networkx.algorithms.cluster import *
+from networkx.algorithms.clique import *
+from networkx.algorithms.communicability_alg import *
+from networkx.algorithms.components import *
+from networkx.algorithms.coloring import *
+from networkx.algorithms.core import *
+from networkx.algorithms.covering import *
+from networkx.algorithms.cycles import *
+from networkx.algorithms.cuts import *
+from networkx.algorithms.d_separation import *
+from networkx.algorithms.dag import *
+from networkx.algorithms.distance_measures import *
+from networkx.algorithms.distance_regular import *
+from networkx.algorithms.dominance import *
+from networkx.algorithms.dominating import *
+from networkx.algorithms.efficiency_measures import *
+from networkx.algorithms.euler import *
+from networkx.algorithms.graphical import *
+from networkx.algorithms.hierarchy import *
+from networkx.algorithms.hybrid import *
+from networkx.algorithms.link_analysis import *
+from networkx.algorithms.link_prediction import *
+from networkx.algorithms.lowest_common_ancestors import *
+from networkx.algorithms.isolate import *
+from networkx.algorithms.matching import *
+from networkx.algorithms.minors import *
+from networkx.algorithms.mis import *
+from networkx.algorithms.moral import *
+from networkx.algorithms.non_randomness import *
+from networkx.algorithms.operators import *
+from networkx.algorithms.planarity import *
+from networkx.algorithms.planar_drawing import *
+from networkx.algorithms.polynomials import *
+from networkx.algorithms.reciprocity import *
+from networkx.algorithms.regular import *
+from networkx.algorithms.richclub import *
+from networkx.algorithms.shortest_paths import *
+from networkx.algorithms.similarity import *
+from networkx.algorithms.graph_hashing import *
+from networkx.algorithms.simple_paths import *
+from networkx.algorithms.smallworld import *
+from networkx.algorithms.smetric import *
+from networkx.algorithms.structuralholes import *
+from networkx.algorithms.sparsifiers import *
+from networkx.algorithms.summarization import *
+from networkx.algorithms.swap import *
+from networkx.algorithms.time_dependent import *
+from networkx.algorithms.traversal import *
+from networkx.algorithms.triads import *
+from networkx.algorithms.vitality import *
+from networkx.algorithms.voronoi import *
+from networkx.algorithms.walks import *
+from networkx.algorithms.wiener import *
+
+# Make certain subpackages available to the user as direct imports from
+# the `networkx` namespace.
+from networkx.algorithms import approximation
+from networkx.algorithms import assortativity
+from networkx.algorithms import bipartite
+from networkx.algorithms import node_classification
+from networkx.algorithms import centrality
+from networkx.algorithms import chordal
+from networkx.algorithms import cluster
+from networkx.algorithms import clique
+from networkx.algorithms import components
+from networkx.algorithms import connectivity
+from networkx.algorithms import community
+from networkx.algorithms import coloring
+from networkx.algorithms import flow
+from networkx.algorithms import isomorphism
+from networkx.algorithms import link_analysis
+from networkx.algorithms import lowest_common_ancestors
+from networkx.algorithms import operators
+from networkx.algorithms import shortest_paths
+from networkx.algorithms import tournament
+from networkx.algorithms import traversal
+from networkx.algorithms import tree
+
+# Make certain functions from some of the previous subpackages available
+# to the user as direct imports from the `networkx` namespace.
+from networkx.algorithms.bipartite import complete_bipartite_graph
+from networkx.algorithms.bipartite import is_bipartite
+from networkx.algorithms.bipartite import projected_graph
+from networkx.algorithms.connectivity import all_pairs_node_connectivity
+from networkx.algorithms.connectivity import all_node_cuts
+from networkx.algorithms.connectivity import average_node_connectivity
+from networkx.algorithms.connectivity import edge_connectivity
+from networkx.algorithms.connectivity import edge_disjoint_paths
+from networkx.algorithms.connectivity import k_components
+from networkx.algorithms.connectivity import k_edge_components
+from networkx.algorithms.connectivity import k_edge_subgraphs
+from networkx.algorithms.connectivity import k_edge_augmentation
+from networkx.algorithms.connectivity import is_k_edge_connected
+from networkx.algorithms.connectivity import minimum_edge_cut
+from networkx.algorithms.connectivity import minimum_node_cut
+from networkx.algorithms.connectivity import node_connectivity
+from networkx.algorithms.connectivity import node_disjoint_paths
+from networkx.algorithms.connectivity import stoer_wagner
+from networkx.algorithms.flow import capacity_scaling
+from networkx.algorithms.flow import cost_of_flow
+from networkx.algorithms.flow import gomory_hu_tree
+from networkx.algorithms.flow import max_flow_min_cost
+from networkx.algorithms.flow import maximum_flow
+from networkx.algorithms.flow import maximum_flow_value
+from networkx.algorithms.flow import min_cost_flow
+from networkx.algorithms.flow import min_cost_flow_cost
+from networkx.algorithms.flow import minimum_cut
+from networkx.algorithms.flow import minimum_cut_value
+from networkx.algorithms.flow import network_simplex
+from networkx.algorithms.isomorphism import could_be_isomorphic
+from networkx.algorithms.isomorphism import fast_could_be_isomorphic
+from networkx.algorithms.isomorphism import faster_could_be_isomorphic
+from networkx.algorithms.isomorphism import is_isomorphic
+from networkx.algorithms.isomorphism.vf2pp import *
+from networkx.algorithms.tree.branchings import maximum_branching
+from networkx.algorithms.tree.branchings import maximum_spanning_arborescence
+from networkx.algorithms.tree.branchings import minimum_branching
+from networkx.algorithms.tree.branchings import minimum_spanning_arborescence
+from networkx.algorithms.tree.branchings import ArborescenceIterator
+from networkx.algorithms.tree.coding import *
+from networkx.algorithms.tree.decomposition import *
+from networkx.algorithms.tree.mst import *
+from networkx.algorithms.tree.operations import *
+from networkx.algorithms.tree.recognition import *
+from networkx.algorithms.tournament import is_tournament