




"""simple docstring"""





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class      a__				:


			def __init__(      self      :    str      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    List[Any]=sys.maxsize):


				"""simple docstring"""




				__UpperCAmelCase	: str		=					"bilinear"
				__UpperCAmelCase	: Optional[int]		=					max_size
				__UpperCAmelCase	: List[Any]		=					short_edge_length


			def __call__(      self      :    Dict      ,  UpperCamelCase_      :    Dict):


				"""simple docstring"""




				__UpperCAmelCase	: Dict		=					[]
				for img in imgs:
					__UpperCAmelCase	: Any		=					img.shape[:2]
					# later: provide list and randomly choose index for resize
					__UpperCAmelCase	: Dict		=					np.random.randint(self.short_edge_length[0]      ,  self.short_edge_length[1] + 1)
					if size == 0:
						return img
					__UpperCAmelCase	: List[Any]		=					size * 1.0 / min(UpperCamelCase_      ,  UpperCamelCase_)
					if h < w:
						__UpperCAmelCase	: int		=					size, scale * w
					else:
						__UpperCAmelCase	: Dict		=					scale * h, size
					if max(UpperCamelCase_      ,  UpperCamelCase_) > self.max_size:
						__UpperCAmelCase	: Optional[int]		=					self.max_size * 1.0 / max(UpperCamelCase_      ,  UpperCamelCase_)
						__UpperCAmelCase	: str		=					newh * scale
						__UpperCAmelCase	: str		=					neww * scale
					__UpperCAmelCase	: int		=					int(neww + 0.5)
					__UpperCAmelCase	: Tuple		=					int(newh + 0.5)

					if img.dtype == np.uinta:
						__UpperCAmelCase	: Optional[Any]		=					Image.fromarray(UpperCamelCase_)
						__UpperCAmelCase	: int		=					pil_image.resize((neww, newh)      ,  PILImageResampling.BILINEAR)
						__UpperCAmelCase	: List[str]		=					np.asarray(UpperCamelCase_)
					else:
						__UpperCAmelCase	: Optional[Any]		=					img.permute(2      ,  0      ,  1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
						__UpperCAmelCase	: Optional[Any]		=					nn.functional.interpolate(
						    UpperCamelCase_      ,  (newh, neww)      ,  mode=self.interp_method      ,  align_corners=UpperCamelCase_).squeeze(0)
					img_augs.append(UpperCamelCase_)

				return img_augs






class      a__				:


			def __init__(      self      :    str      ,  UpperCamelCase_      :    Tuple):


				"""simple docstring"""




				__UpperCAmelCase	: Optional[int]		=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      ,  cfg.INPUT.MAX_SIZE_TEST)
				__UpperCAmelCase	: Any		=					cfg.INPUT.FORMAT
				__UpperCAmelCase	: Tuple		=					cfg.SIZE_DIVISIBILITY
				__UpperCAmelCase	: Union[str, Any]		=					cfg.PAD_VALUE
				__UpperCAmelCase	: Tuple		=					cfg.INPUT.MAX_SIZE_TEST
				__UpperCAmelCase	: List[str]		=					cfg.MODEL.DEVICE
				__UpperCAmelCase	: str		=					torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)      ,  1      ,  1)
				__UpperCAmelCase	: str		=					torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)      ,  1      ,  1)
				__UpperCAmelCase	: Dict		=					lambda UpperCamelCase_: (x - self.pixel_mean) / self.pixel_std


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


				"""simple docstring"""




				__UpperCAmelCase	: Optional[int]		=					tuple(max(UpperCamelCase_) for s in zip(*[img.shape for img in images]))
				__UpperCAmelCase	: List[str]		=					[im.shape[-2:] for im in images]
				__UpperCAmelCase	: Tuple		=					[
				    nn.functional.pad(
				        UpperCamelCase_      ,  [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      ,  value=self.pad_value      ,  )
				    for size, im in zip(UpperCamelCase_      ,  UpperCamelCase_)
				]

				return torch.stack(UpperCamelCase_), torch.tensor(UpperCamelCase_)


			def __call__(      self      :    Any      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Optional[int]=False):


				"""simple docstring"""




				with torch.no_grad():
					if not isinstance(UpperCamelCase_      ,  UpperCamelCase_):
						__UpperCAmelCase	: Union[str, Any]		=					[images]
					if single_image:
						assert len(UpperCamelCase_) == 1
					for i in range(len(UpperCamelCase_)):
						if isinstance(images[i]      ,  torch.Tensor):
							images.insert(UpperCamelCase_      ,  images.pop(UpperCamelCase_).to(self.device).float())
						elif not isinstance(images[i]      ,  torch.Tensor):
							images.insert(
							    UpperCamelCase_      ,  torch.as_tensor(img_tensorize(images.pop(UpperCamelCase_)      ,  input_format=self.input_format))
							    .to(self.device)
							    .float()      ,  )
            # resize smallest edge
					__UpperCAmelCase	: Optional[int]		=					torch.tensor([im.shape[:2] for im in images])
					__UpperCAmelCase	: Dict		=					self.aug(UpperCamelCase_)
					# transpose images and convert to torch tensors
					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
					# now normalize before pad to avoid useless arithmetic
					__UpperCAmelCase	: Optional[Any]		=					[self.normalizer(UpperCamelCase_) for x in images]
					# now pad them to do the following operations
					__UpperCAmelCase	: Tuple		=					self.pad(UpperCamelCase_)
					# Normalize

					if self.size_divisibility > 0:
						raise NotImplementedError()
					# pad
					__UpperCAmelCase	: List[Any]		=					torch.true_divide(UpperCamelCase_      ,  UpperCamelCase_)
					if single_image:
						return images[0], sizes[0], scales_yx[0]
					else:
						return images, sizes, scales_yx






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


	"""simple docstring"""
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Union[str, Any]:


	"""simple docstring"""
	assert torch.isfinite(UpperCamelCase							).all(), "Box tensor contains infinite or NaN!"
	__UpperCAmelCase	: Any		=					box_size
	tensor[:, 0].clamp_(min=0				,       max=UpperCamelCase							)
	tensor[:, 1].clamp_(min=0				,       max=UpperCamelCase							)
	tensor[:, 2].clamp_(min=0				,       max=UpperCamelCase							)
	tensor[:, 3].clamp_(min=0				,       max=UpperCamelCase							)








"""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      :    Optional[Any]):


						"""simple docstring"""




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


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					"src/transformers"
						shutil.rmtree(self.transformer_dir)


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


						"""simple docstring"""




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


						"""simple docstring"""




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


			def       a_     (      self      :    Optional[int]):


						"""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	: Optional[Any]		=					"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      :    Dict):


						"""simple docstring"""




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

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

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

						__UpperCAmelCase	,     __UpperCAmelCase	: 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	: str		=					(
						    "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	: int		=					(
						    "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	: List[str]		=					check_copies.convert_to_localized_md(
						    UpperCamelCase_      ,  UpperCamelCase_      ,  localized_readme["format_model_list"])

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


