prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Typo in deberta_v2/__init__.py
  https://github.com/huggingface/transformers/blob/c503a1c15ec1b11e69a3eaaf06edfa87c05a2849/src/transformers/models/deberta_v2/__init__.py#L31
  Should be '' DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST ''.
  
  </issue>
  <code>
  [start of src/transformers/models/deberta_v2/__init__.py]
  1 # flake8: noqa
  2 # There's no way to ignore "F401 '...' imported but unused" warnings in this
  3 # module, but to preserve other warnings. So, don't check this module at all.
  4 
  5 # Copyright 2020 The HuggingFace Team. All rights reserved.
  6 #
  7 # Licensed under the Apache License, Version 2.0 (the "License");
  8 # you may not use this file except in compliance with the License.
  9 # You may obtain a copy of the License at
  10 #
  11 #     http://www.apache.org/licenses/LICENSE-2.0
  12 #
  13 # Unless required by applicable law or agreed to in writing, software
  14 # distributed under the License is distributed on an "AS IS" BASIS,
  15 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  16 # See the License for the specific language governing permissions and
  17 # limitations under the License.
  18 
  19 from typing import TYPE_CHECKING
  20 
  21 from ...file_utils import _BaseLazyModule, is_torch_available
  22 
  23 
  24 _import_structure = {
  25     "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config"],
  26     "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
  27 }
  28 
  29 if is_torch_available():
  30     _import_structure["modeling_deberta_v2"] = [
  31         "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
  32         "DebertaV2ForSequenceClassification",
  33         "DebertaV2Model",
  34         "DebertaV2ForMaskedLM",
  35         "DebertaV2PreTrainedModel",
  36         "DebertaV2ForTokenClassification",
  37         "DebertaV2ForQuestionAnswering",
  38     ]
  39 
  40 
  41 if TYPE_CHECKING:
  42     from .configuration_deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config
  43     from .tokenization_deberta_v2 import DebertaV2Tokenizer
  44 
  45     if is_torch_available():
  46         from .modeling_deberta_v2 import (
  47             DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
  48             DebertaV2ForMaskedLM,
  49             DebertaV2ForQuestionAnswering,
  50             DebertaV2ForSequenceClassification,
  51             DebertaV2ForTokenClassification,
  52             DebertaV2Model,
  53             DebertaV2PreTrainedModel,
  54         )
  55 
  56 else:
  57     import importlib
  58     import os
  59     import sys
  60 
  61     class _LazyModule(_BaseLazyModule):
  62         """
  63         Module class that surfaces all objects but only performs associated imports when the objects are requested.
  64         """
  65 
  66         __file__ = globals()["__file__"]
  67         __path__ = [os.path.dirname(__file__)]
  68 
  69         def _get_module(self, module_name: str):
  70             return importlib.import_module("." + module_name, self.__name__)
  71 
  72     sys.modules[__name__] = _LazyModule(__name__, _import_structure)
  73
  [end of src/transformers/models/deberta_v2/__init__.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/src/transformers/models/deberta_v2/__init__.py b/src/transformers/models/deberta_v2/__init__.py
  --- a/src/transformers/models/deberta_v2/__init__.py
  +++ b/src/transformers/models/deberta_v2/__init__.py
  @@ -28,7 +28,7 @@
   
   if is_torch_available():
       _import_structure["modeling_deberta_v2"] = [
  -        "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
  +        "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DebertaV2ForSequenceClassification",
           "DebertaV2Model",
           "DebertaV2ForMaskedLM",
  
verification_info: {"golden_diff": "diff --git a/src/transformers/models/deberta_v2/__init__.py b/src/transformers/models/deberta_v2/__init__.py\n--- a/src/transformers/models/deberta_v2/__init__.py\n+++ b/src/transformers/models/deberta_v2/__init__.py\n@@ -28,7 +28,7 @@\n \n if is_torch_available():\n     _import_structure[\"modeling_deberta_v2\"] = [\n-        \"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST\",\n+        \"DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST\",\n         \"DebertaV2ForSequenceClassification\",\n         \"DebertaV2Model\",\n         \"DebertaV2ForMaskedLM\",\n", "issue": "Typo in deberta_v2/__init__.py\nhttps://github.com/huggingface/transformers/blob/c503a1c15ec1b11e69a3eaaf06edfa87c05a2849/src/transformers/models/deberta_v2/__init__.py#L31\r\nShould be '' DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST ''.\n", "before_files": [{"content": "# flake8: noqa\n# There's no way to ignore \"F401 '...' imported but unused\" warnings in this\n# module, but to preserve other warnings. So, don't check this module at all.\n\n# Copyright 2020 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom typing import TYPE_CHECKING\n\nfrom ...file_utils import _BaseLazyModule, is_torch_available\n\n\n_import_structure = {\n    \"configuration_deberta_v2\": [\"DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP\", \"DebertaV2Config\"],\n    \"tokenization_deberta_v2\": [\"DebertaV2Tokenizer\"],\n}\n\nif is_torch_available():\n    _import_structure[\"modeling_deberta_v2\"] = [\n        \"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST\",\n        \"DebertaV2ForSequenceClassification\",\n        \"DebertaV2Model\",\n        \"DebertaV2ForMaskedLM\",\n        \"DebertaV2PreTrainedModel\",\n        \"DebertaV2ForTokenClassification\",\n        \"DebertaV2ForQuestionAnswering\",\n    ]\n\n\nif TYPE_CHECKING:\n    from .configuration_deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config\n    from .tokenization_deberta_v2 import DebertaV2Tokenizer\n\n    if is_torch_available():\n        from .modeling_deberta_v2 import (\n            DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,\n            DebertaV2ForMaskedLM,\n            DebertaV2ForQuestionAnswering,\n            DebertaV2ForSequenceClassification,\n            DebertaV2ForTokenClassification,\n            DebertaV2Model,\n            DebertaV2PreTrainedModel,\n        )\n\nelse:\n    import importlib\n    import os\n    import sys\n\n    class _LazyModule(_BaseLazyModule):\n        \"\"\"\n        Module class that surfaces all objects but only performs associated imports when the objects are requested.\n        \"\"\"\n\n        __file__ = globals()[\"__file__\"]\n        __path__ = [os.path.dirname(__file__)]\n\n        def _get_module(self, module_name: str):\n            return importlib.import_module(\".\" + module_name, self.__name__)\n\n    sys.modules[__name__] = _LazyModule(__name__, _import_structure)\n", "path": "src/transformers/models/deberta_v2/__init__.py"}]}