Delete files *.py with huggingface_hub

configuration_minicpm.py

@@ -1,118 +0,0 @@
-# coding=utf-8
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# 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.
-""" MiniCPM model configuration"""
-
-import os
-from typing import Union
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-from .modeling_navit_siglip import SiglipVisionConfig
-
-from transformers import LlamaConfig
-
-logger = logging.get_logger(__name__)
-
-
-class MiniCPMVSliceConfig(PretrainedConfig):
-    model_type = "minicpmv"
-
-    def __init__(
-        self,
-        patch_size=14,
-        max_slice_nums=9,
-        scale_resolution=448,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.patch_size = patch_size
-        self.max_slice_nums = max_slice_nums
-        self.scale_resolution = scale_resolution
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
-        cls._set_token_in_kwargs(kwargs)
-
-        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-
-        if config_dict.get("model_type") == "minicpmv":
-            config_dict = config_dict["slice_config"]
-
-        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
-            logger.warning(
-                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
-                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
-            )
-
-        return cls.from_dict(config_dict, **kwargs)
-
-class MiniCPMVConfig(LlamaConfig):
-    model_type = "minicpmv"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    default_vision_config = {
-        "hidden_size": 1152,
-        "image_size": 980,
-        "intermediate_size": 4304,
-        "model_type": "siglip",
-        "num_attention_heads": 16,
-        "num_hidden_layers": 27,
-        "patch_size": 14,
-    }
-
-    def __init__(
-        self,
-        use_cache=True,
-        query_num=64,
-        image_size=448,
-        drop_vision_last_layer=True,
-        batch_vision_input=True,
-        slice_config=None,
-        vision_config=None,
-        use_image_id=True,
-        vision_batch_size=16,
-        **kwargs,
-    ):
-        self.use_cache = use_cache
-        self.query_num = query_num
-        self.image_size = image_size
-        self.drop_vision_last_layer = drop_vision_last_layer
-        self.batch_vision_input = batch_vision_input
-        self.use_image_id = use_image_id
-        self.vision_batch_size = vision_batch_size
-
-        if slice_config is None:
-            self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1)
-        else:
-            self.slice_config = MiniCPMVSliceConfig(**slice_config)
-        self.slice_mode = True
-
-        # same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit add tgt_sizes
-        if vision_config is None:
-            self.vision_config = SiglipVisionConfig(**self.default_vision_config)
-            logger.info("vision_config is None, using default vision config")
-        elif isinstance(vision_config, dict):
-            self.vision_config = SiglipVisionConfig(**vision_config)
-        elif isinstance(vision_config, SiglipVisionConfig):
-            self.vision_config = vision_config
-
-        self.patch_size = self.vision_config.patch_size
-
-        super().__init__(**kwargs)

tokenization_minicpmv_fast.py

@@ -1,66 +0,0 @@
-from transformers import LlamaTokenizerFast
-
-
-class MiniCPMVTokenizerFast(LlamaTokenizerFast):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.im_start = "<image>"
-        self.im_end = "</image>"
-        self.ref_start = "<ref>"
-        self.ref_end = "</ref>"
-        self.box_start = "<box>"
-        self.box_end = "</box>"
-        self.quad_start = "<quad>"
-        self.quad_end = "</quad>"
-        self.slice_start = "<slice>"
-        self.slice_end = "</slice>"
-        self.im_id_start = "<image_id>"
-        self.im_id_end = "</image_id>"
-
-    @property
-    def eos_id(self):
-        return self.eos_token_id
-
-    @property
-    def bos_id(self):
-        return self.bos_token_id
-
-    @property
-    def unk_id(self):
-        return self.unk_token_id
-
-    @property
-    def im_start_id(self):
-        return self.convert_tokens_to_ids(self.im_start)
-
-    @property
-    def im_end_id(self):
-        return self.convert_tokens_to_ids(self.im_end)
-
-    @property
-    def slice_start_id(self):
-        return self.convert_tokens_to_ids(self.slice_start)
-    
-    @property
-    def slice_end_id(self):
-        return self.convert_tokens_to_ids(self.slice_end)
-
-    @property
-    def im_id_start_id(self):
-        return self.convert_tokens_to_ids(self.im_id_start)
-    
-    @property
-    def im_id_end_id(self):
-        return self.convert_tokens_to_ids(self.im_id_end)
-    
-    @property
-    def newline_id(self):
-        return self.convert_tokens_to_ids('\n')
-
-    @staticmethod
-    def escape(text: str) -> str:
-        return text
-
-    @staticmethod
-    def unescape(text: str) -> str:
-        return text