






"""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      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=sys.maxsize							):
		A_		:						str	    =   """bilinear"""
		A_		:						Tuple	    =   max_size
		A_		:						List[Any]	    =   short_edge_length





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

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

		return img_augs






class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




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





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

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





	def __call__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=False							):
		with torch.no_grad():
			if not isinstance(lowerCAmelCase_     ,     lowerCAmelCase_							):
				A_		:						Union[str, Any]	    =   [images]
			if single_image:
				assert len(lowerCAmelCase_							) == 1
			for i in range(len(lowerCAmelCase_							)							):
				if isinstance(images[i]     ,     torch.Tensor							):
					images.insert(lowerCAmelCase_     ,     images.pop(lowerCAmelCase_							).to(self.device							).float()							)
				elif not isinstance(images[i]     ,     torch.Tensor							):
					images.insert(
					    lowerCAmelCase_     ,     torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase_							)     ,     input_format=self.input_format							)							)
					    .to(self.device							)
					    .float()     ,     )
            # resize smallest edge
			A_		:						str	    =   torch.tensor([im.shape[:2] for im in images]							)
			A_		:						List[Any]	    =   self.aug(lowerCAmelCase_							)
			# 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
			A_		:						Optional[int]	    =   [self.normalizer(lowerCAmelCase_							) for x in images]
			# now pad them to do the following operations
			A_    ,     A_		:						Optional[int]	    =   self.pad(lowerCAmelCase_							)
			# Normalize

			if self.size_divisibility > 0:
				raise NotImplementedError()
			# pad
			A_		:						Tuple	    =   torch.true_divide(lowerCAmelCase_     ,     lowerCAmelCase_							)
			if single_image:
				return images[0], sizes[0], scales_yx[0]
			else:
				return images, sizes, scales_yx






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	assert torch.isfinite(snake_case__					).all(), "Box tensor contains infinite or NaN!"
	A_    ,     A_		:						Dict	    =   box_size
	tensor[:, 0].clamp_(min=0   ,   max=snake_case__					)
	tensor[:, 1].clamp_(min=0   ,   max=snake_case__					)
	tensor[:, 2].clamp_(min=0   ,   max=snake_case__					)
	tensor[:, 3].clamp_(min=0   ,   max=snake_case__					)









"""simple docstring"""
import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


_lowerCAmelCase         =							"http://www.mocksite.com/file1.txt"
_lowerCAmelCase         =							"\"text\": [\"foo\", \"foo\"]"
_lowerCAmelCase         =							"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"



class      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	_A       :				Optional[int]    =				200
	_A       :				Dict    =				{"""Content-Length""": """100"""}
	_A       :				List[Any]    =				{}





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return [bytes(lowerCAmelCase_     ,     """utf-8"""							)]






def 			__UpperCamelCase	(		*snake_case__   ,   **snake_case__					):
	return MockResponse()






@pytest.mark.parametrize("""urls_type"""   ,   [str, list, dict]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	import requests

	monkeypatch.setattr(snake_case__   ,   """request"""   ,   snake_case__					)

	A_		:						Optional[int]	    =   URL
	if issubclass(snake_case__   ,   snake_case__					):
		A_		:						str	    =   url
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Union[str, Any]	    =   [url]
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						List[Any]	    =   {"""train""": url}
	A_		:						Tuple	    =   """dummy"""
	A_		:						List[str]	    =   """downloads"""
	A_		:						List[str]	    =   tmp_path
	A_		:						str	    =   DownloadConfig(
	    cache_dir=os.path.join(snake_case__   ,   snake_case__					)   ,   use_etag=snake_case__   ,   )
	A_		:						Any	    =   DownloadManager(dataset_name=snake_case__   ,   download_config=snake_case__					)
	A_		:						Dict	    =   dl_manager.download(snake_case__					)
	A_		:						List[str]	    =   urls
	for downloaded_paths in [downloaded_paths]:
		if isinstance(snake_case__   ,   snake_case__					):
			A_		:						Optional[Any]	    =   [downloaded_paths]
			A_		:						Tuple	    =   [urls]
		elif isinstance(snake_case__   ,   snake_case__					):
			assert "train" in downloaded_paths.keys()
			A_		:						Union[str, Any]	    =   downloaded_paths.values()
			A_		:						Tuple	    =   urls.values()
		assert downloaded_paths
		for downloaded_path, input_url in zip(snake_case__   ,   snake_case__					):
			assert downloaded_path == dl_manager.downloaded_paths[input_url]
			A_		:						Union[str, Any]	    =   Path(snake_case__					)
			A_		:						str	    =   downloaded_path.parts
			assert parts[-1] == HASH
			assert parts[-2] == cache_subdir
			assert downloaded_path.exists()
			A_		:						List[str]	    =   downloaded_path.read_text()
			assert content == CONTENT
			A_		:						Union[str, Any]	    =   downloaded_path.with_suffix(""".json"""					)
			assert metadata_downloaded_path.exists()
			A_		:						List[str]	    =   json.loads(metadata_downloaded_path.read_text()					)
			assert metadata_content == {"url": URL, "etag": None}






@pytest.mark.parametrize("""paths_type"""   ,   [str, list, dict]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   str(snake_case__					)
	if issubclass(snake_case__   ,   snake_case__					):
		A_		:						List[str]	    =   filename
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Tuple	    =   [filename]
	elif issubclass(snake_case__   ,   snake_case__					):
		A_		:						Dict	    =   {"""train""": filename}
	A_		:						Union[str, Any]	    =   """dummy"""
	A_		:						Dict	    =   xz_file.parent
	A_		:						List[str]	    =   """extracted"""
	A_		:						int	    =   DownloadConfig(
	    cache_dir=snake_case__   ,   use_etag=snake_case__   ,   )
	A_		:						str	    =   DownloadManager(dataset_name=snake_case__   ,   download_config=snake_case__					)
	A_		:						int	    =   dl_manager.extract(snake_case__					)
	A_		:						int	    =   paths
	for extracted_paths in [extracted_paths]:
		if isinstance(snake_case__   ,   snake_case__					):
			A_		:						Dict	    =   [extracted_paths]
			A_		:						str	    =   [paths]
		elif isinstance(snake_case__   ,   snake_case__					):
			assert "train" in extracted_paths.keys()
			A_		:						Any	    =   extracted_paths.values()
			A_		:						str	    =   paths.values()
		assert extracted_paths
		for extracted_path, input_path in zip(snake_case__   ,   snake_case__					):
			assert extracted_path == dl_manager.extracted_paths[input_path]
			A_		:						str	    =   Path(snake_case__					)
			A_		:						List[str]	    =   extracted_path.parts
			assert parts[-1] == hash_url_to_filename(snake_case__   ,   etag=snake_case__					)
			assert parts[-2] == extracted_subdir
			assert extracted_path.exists()
			A_		:						Optional[Any]	    =   extracted_path.read_text()
			A_		:						Optional[int]	    =   text_file.read_text()
			assert extracted_file_content == expected_file_content






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	assert path.endswith(""".jsonl"""					)
	for num_items, line in enumerate(snake_case__   ,   start=1					):
		A_		:						List[str]	    =   json.loads(line.decode("""utf-8"""					)					)
		assert item.keys() == {"col_1", "col_2", "col_3"}
	assert num_items == 4






@pytest.mark.parametrize("""archive_jsonl"""   ,   ["""tar_jsonl_path""", """zip_jsonl_path"""]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Optional[int]	    =   request.getfixturevalue(snake_case__					)
	A_		:						List[str]	    =   DownloadManager()
	for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
		_test_jsonl(snake_case__   ,   snake_case__					)
	assert num_jsonl == 2






@pytest.mark.parametrize("""archive_nested_jsonl"""   ,   ["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]					)
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   request.getfixturevalue(snake_case__					)
	A_		:						Union[str, Any]	    =   DownloadManager()
	for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
		for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case__					)   ,   start=1					):
			_test_jsonl(snake_case__   ,   snake_case__					)
	assert num_tar == 1
	assert num_jsonl == 2






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						int	    =   DownloadManager()
	for num_file, file in enumerate(dl_manager.iter_files(snake_case__					)   ,   start=1					):
		assert os.path.basename(snake_case__					) == ("test.txt" if num_file == 1 else "train.txt")
	assert num_file == 2

