




"""simple docstring"""





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

import black


A				=							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.
A				=							"""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""



class   a__					(						unittest.TestCase ):


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       tempfile.mkdtemp()
						os.makedirs(os.path.join(self.transformer_dir   ,	"models/bert/"))
						__UpperCAmelCase						:			List[str]												=       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    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       "src/transformers"
						shutil.rmtree(self.transformer_dir)


			def    a_  (   self   :     Any   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     Optional[Any]   ,	UpperCamelCase_   :     List[Any]   ,	UpperCamelCase_   :     Any=None):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[int]												=       comment + F"\nclass {class_name}(nn.Module):\n" + class_code
						if overwrite_result is not None:
									__UpperCAmelCase						:			Any												=       comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
						__UpperCAmelCase						:			Union[str, Any]												=       black.Mode(target_versions={black.TargetVersion.PYaa}   ,	line_length=119)
						__UpperCAmelCase						:			Any												=       black.format_str(UpperCamelCase_   ,	mode=UpperCamelCase_)
						__UpperCAmelCase						:			Optional[Any]												=       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    a_  (   self   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
						self.assertEqual(UpperCamelCase_   ,	UpperCamelCase_)


			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						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
						__UpperCAmelCase						:			str												=       "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    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       check_copies.LOCALIZED_READMES["README_zh-hans.md"]

						__UpperCAmelCase						:			Dict												=       (
						    "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."
						)
						__UpperCAmelCase						:			List[Any]												=       (
						    "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"
						)
						__UpperCAmelCase						:			Union[str, Any]												=       (
						    "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"
						)

						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       check_copies.convert_to_localized_md(
						    UpperCamelCase_   ,	UpperCamelCase_   ,	localized_readme["format_model_list"])

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

						__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[int]												=       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_)

						__UpperCAmelCase						:			Dict												=       (
						    "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."
						)
						__UpperCAmelCase						:			Optional[int]												=       (
						    "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"
						)
						__UpperCAmelCase						:			Union[str, Any]												=       (
						    "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"
						)

						__UpperCAmelCase						,  __UpperCAmelCase						:			Dict												=       check_copies.convert_to_localized_md(
						    UpperCamelCase_   ,	UpperCamelCase_   ,	localized_readme["format_model_list"])

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



"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





