





'''simple docstring'''




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

import black


_SCREAMING_SNAKE_CASE							     =			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.
_SCREAMING_SNAKE_CASE							     =			"    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	):






     """simple docstring"""






     def      lowerCAmelCase			(   self							:			Tuple						)->	Union[str, Any]:
        snake_case 						=   tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir   ,       """models/bert/"""						)						)
        snake_case 						=   self.transformer_dir
        shutil.copy(
            os.path.join(__snake_case   ,       """src/transformers/models/bert/modeling_bert.py"""						)   ,       os.path.join(self.transformer_dir   ,       """models/bert/modeling_bert.py"""						)   ,       )



     def      lowerCAmelCase			(   self							:			Any						)->	Tuple:
        snake_case 						=   """src/transformers"""
        shutil.rmtree(self.transformer_dir						)



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			List[str]   ,       __snake_case							:			Any=None						)->	Union[str, Any]:
        snake_case 						=   comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
        if overwrite_result is not None:
           snake_case 						=   comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
        snake_case 						=   black.Mode(target_versions={black.TargetVersion.PYaa}   ,       line_length=1_19						)
        snake_case 						=   black.format_str(__snake_case   ,       mode=__snake_case						)
        snake_case 						=   os.path.join(self.transformer_dir   ,       """new_code.py"""						)
        with open(__snake_case   ,       """w"""   ,       newline="""\n"""						) as f:
           f.write(__snake_case						)
        if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(__snake_case						)						) == 0						)
        else:
           check_copies.is_copy_consistent(f.name   ,       overwrite=__snake_case						)
           with open(__snake_case   ,       """r"""						) as f:
              self.assertTrue(f.read()   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			str						)->	int:
        snake_case 						=   check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead"""						)
        self.assertEqual(__snake_case   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	List[str]:
        # 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"""   ,       __snake_case   ,       )

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

        # Copy consistency with a really long name
        snake_case 						=   """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"""   ,       __snake_case   ,       __snake_case						)   ,       )

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



     def      lowerCAmelCase			(   self							:			List[str]						)->	Optional[int]:
        snake_case 						=   check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

        snake_case 						=   (
            """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."""
        )
        snake_case 						=   (
            """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"""
        )
        snake_case 						=   (
            """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"""
        )

        snake_case						,    snake_case 						=   check_copies.convert_to_localized_md(
            __snake_case   ,       __snake_case   ,       localized_readme["""format_model_list"""]						)

        self.assertFalse(__snake_case						)
        self.assertEqual(__snake_case   ,       __snake_case						)

        snake_case						,    snake_case 						=   check_copies.convert_to_localized_md(
            __snake_case   ,       __snake_case   ,       localized_readme["""format_model_list"""]						)

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

        snake_case 						=   (
            """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."""
        )
        snake_case 						=   (
            """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"""
        )
        snake_case 						=   (
            """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"""
        )

        snake_case						,    snake_case 						=   check_copies.convert_to_localized_md(
            __snake_case   ,       __snake_case   ,       localized_readme["""format_model_list"""]						)

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








'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_pt_objects import *  # noqa F403
else:
 from .scheduling_consistency_models import CMStochasticIterativeScheduler
 from .scheduling_ddim import DDIMScheduler
 from .scheduling_ddim_inverse import DDIMInverseScheduler
 from .scheduling_ddim_parallel import DDIMParallelScheduler
 from .scheduling_ddpm import DDPMScheduler
 from .scheduling_ddpm_parallel import DDPMParallelScheduler
 from .scheduling_deis_multistep import DEISMultistepScheduler
 from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
 from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
 from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from .scheduling_euler_discrete import EulerDiscreteScheduler
 from .scheduling_heun_discrete import HeunDiscreteScheduler
 from .scheduling_ipndm import IPNDMScheduler
 from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
 from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
 from .scheduling_karras_ve import KarrasVeScheduler
 from .scheduling_pndm import PNDMScheduler
 from .scheduling_repaint import RePaintScheduler
 from .scheduling_sde_ve import ScoreSdeVeScheduler
 from .scheduling_sde_vp import ScoreSdeVpScheduler
 from .scheduling_unclip import UnCLIPScheduler
 from .scheduling_unipc_multistep import UniPCMultistepScheduler
 from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
 from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_flax_objects import *  # noqa F403
else:
 from .scheduling_ddim_flax import FlaxDDIMScheduler
 from .scheduling_ddpm_flax import FlaxDDPMScheduler
 from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
 from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
 from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
 from .scheduling_pndm_flax import FlaxPNDMScheduler
 from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
 from .scheduling_utils_flax import (
     FlaxKarrasDiffusionSchedulers,
     FlaxSchedulerMixin,
     FlaxSchedulerOutput,
     broadcast_to_shape_from_left,
 )


try:
 if not (is_torch_available() and is_scipy_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
 from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
 if not (is_torch_available() and is_torchsde_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
 from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler

