




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase_									=	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
UpperCAmelCase_									=	'    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'





class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						tempfile.mkdtemp()
             os.makedirs(os.path.join(self.transformer_dir    ,       """models/bert/"""   )   )
             __lowerCamelCase   =						self.transformer_dir
             shutil.copy(
                 os.path.join(UpperCamelCase_    ,       """src/transformers/models/bert/modeling_bert.py"""   )    ,       os.path.join(self.transformer_dir    ,       """models/bert/modeling_bert.py"""   )    ,       )
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						"""src/transformers"""
             shutil.rmtree(self.transformer_dir   )
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: List[str]=None   ):
             __lowerCamelCase   =						comment + F'\nclass {class_name}(nn.Module):\n' + class_code
             if overwrite_result is not None:
                    __lowerCamelCase   =						comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
             __lowerCamelCase   =						black.Mode(target_versions={black.TargetVersion.PYaa}    ,       line_length=1_19   )
             __lowerCamelCase   =						black.format_str(UpperCamelCase_    ,       mode=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(self.transformer_dir    ,       """new_code.py"""   )
             with open(UpperCamelCase_    ,       """w"""    ,       newline="""\n"""   ) as f:
                    f.write(UpperCamelCase_   )
             if overwrite_result is None:
                    self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase_   )   ) == 0   )
             else:
                    check_copies.is_copy_consistent(f.name    ,       overwrite=UpperCamelCase_   )
                    with open(UpperCamelCase_    ,       """r"""   ) as f:
                           self.assertTrue(f.read()    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""   )
             self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             # Base copy consistency
             self.check_copy_consistency(
                 """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""    ,       """BertLMPredictionHead"""    ,       REFERENCE_CODE + """\n"""    ,       )

             # With no empty line at the end
             self.check_copy_consistency(
                 """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""    ,       """BertLMPredictionHead"""    ,       UpperCamelCase_    ,       )

             # Copy consistency with rename
             self.check_copy_consistency(
                 """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""    ,       """TestModelLMPredictionHead"""    ,       re.sub("""Bert"""    ,       """TestModel"""    ,       UpperCamelCase_   )    ,       )

             # Copy consistency with a really long name
             __lowerCamelCase   =						"""TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
             self.check_copy_consistency(
                 F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'    ,       F'{long_class_name}LMPredictionHead'    ,       re.sub("""Bert"""    ,       UpperCamelCase_    ,       UpperCamelCase_   )    ,       )

             # Copy consistency with overwrite
             self.check_copy_consistency(
                 """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""    ,       """TestModelLMPredictionHead"""    ,       UpperCamelCase_    ,       overwrite_result=re.sub("""Bert"""    ,       """TestModel"""    ,       UpperCamelCase_   )    ,       )




      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

             __lowerCamelCase   =						(
                 """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
                 """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
                 """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
                 """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."""
                 """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"""
                 """ released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
                 """ lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"""
                 """ method has been applied to compress GPT2 into"""
                 """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
                 """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
                 """ Multilingual BERT into"""
                 """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
                 """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"""
                 """ (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"""
                 """ as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"""
                 """ Luong, Quoc V. Le, Christopher D. Manning."""
             )
             __lowerCamelCase   =						(
                 """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
                 """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                 """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                 """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
             )
             __lowerCamelCase   =						(
                 """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
                 """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                 """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                 """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."""
                 """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"""
                 """ [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
                 """ lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"""
                 """ method has been applied to compress GPT2 into"""
                 """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
                 """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
                 """ Multilingual BERT into"""
                 """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
                 """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"""
                 """ Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"""
                 """ than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"""
                 """ Christopher D. Manning 发布。\n"""
             )

             __lowerCamelCase,   __lowerCamelCase   =						check_copies.convert_to_localized_md(
                 UpperCamelCase_    ,       UpperCamelCase_    ,       localized_readme["""format_model_list"""]   )

             self.assertFalse(UpperCamelCase_   )
             self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )

             __lowerCamelCase,   __lowerCamelCase   =						check_copies.convert_to_localized_md(
                 UpperCamelCase_    ,       UpperCamelCase_    ,       localized_readme["""format_model_list"""]   )

             # Check whether the number of models is equal to README.md after conversion.
             self.assertTrue(UpperCamelCase_   )

             __lowerCamelCase   =						(
                 """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
                 """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
                 """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
                 """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."""
             )
             __lowerCamelCase   =						(
                 """1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"""
                 """ the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                 """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                 """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
             )
             __lowerCamelCase   =						(
                 """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
                 """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
                 """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
                 """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
             )

             __lowerCamelCase,   __lowerCamelCase   =						check_copies.convert_to_localized_md(
                 UpperCamelCase_    ,       UpperCamelCase_    ,       localized_readme["""format_model_list"""]   )

             # Check if the model link is synchronized.
             self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}





class     lowerCamelCase__:
      def __init__(				self: str    ,       UpperCamelCase_: List[Any]=None    ,       **UpperCamelCase_: str   ):
             logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""   )
             __lowerCamelCase   =						model
             __lowerCamelCase   =						kwargs.get("""model_save_dir"""    ,       UpperCamelCase_   )
             __lowerCamelCase   =						kwargs.get("""latest_model_name"""    ,       UpperCamelCase_   )
      def __call__(				self: Dict    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{k: np.array(UpperCamelCase_   ) for k, v in kwargs.items()}
             return self.model.run(UpperCamelCase_    ,       UpperCamelCase_   )
      @staticmethod
      def 			lowerCAmelCase__						(				UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Tuple=None    ,       UpperCamelCase_: Tuple=None   ):
             if provider is None:
                    logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""   )
                    __lowerCamelCase   =						"""CPUExecutionProvider"""

             return ort.InferenceSession(UpperCamelCase_    ,       providers=[provider]    ,       sess_options=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

             __lowerCamelCase   =						self.model_save_dir.joinpath(self.latest_model_name   )
             __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
             try:
                    shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
             except shutil.SameFileError:
                    pass

             # copy external weights (for models >2GB)
             __lowerCamelCase   =						self.model_save_dir.joinpath(UpperCamelCase_   )
             if src_path.exists():
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
                    try:
                           shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
                    except shutil.SameFileError:
                           pass
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, os.PathLike]    ,       **UpperCamelCase_: Optional[Any]    ,       ):
             if os.path.isfile(UpperCamelCase_   ):
                    logger.error(F'Provided path ({save_directory}) should be a directory, not a file'   )
                    return

             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )

             # saving model weights/files
             self._save_pretrained(UpperCamelCase_    ,       **UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: str    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[Union[bool, str, None]] = None    ,       UpperCamelCase_: Optional[Union[str, None]] = None    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional["ort.SessionOptions"] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
             # load model from local directory
             if os.path.isdir(UpperCamelCase_   ):
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(
                        os.path.join(UpperCamelCase_    ,       UpperCamelCase_   )    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
                    __lowerCamelCase   =						Path(UpperCamelCase_   )
             # load model from hub
             else:
                    # download model
                    __lowerCamelCase   =						hf_hub_download(
                        repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       )
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).parent
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).name
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(UpperCamelCase_    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
             return cls(model=UpperCamelCase_    ,       **UpperCamelCase_   )




      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[int]    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						None
             if len(str(UpperCamelCase_   ).split("""@"""   )   ) == 2:
                    __lowerCamelCase,   __lowerCamelCase   =						model_id.split("""@"""   )

             return cls._from_pretrained(
                 model_id=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       **UpperCamelCase_    ,       )

