
"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Any						)   ->			List[str]:







								"""simple docstring"""
								A_								=    tempfile.mkdtemp()

								# fmt: off
								A_								=    ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
								# fmt: on
								A_								=    dict(zip(_snake_case							, range(len(_snake_case						)						)						)						)
								A_								=    ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
								A_								=    {"unk_token": "<unk>"}

								A_								=    os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["vocab_file"]						)
								A_								=    os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["merges_file"]						)
								with open(self.vocab_file							, "w"							, encoding="utf-8"						) as fp:
													fp.write(json.dumps(_snake_case						) + "\n"						)
								with open(self.merges_file							, "w"							, encoding="utf-8"						) as fp:
													fp.write("\n".join(_snake_case						)						)

								A_								=    {
								    "do_resize": True,
								    "size": 20,
								    "do_center_crop": True,
								    "crop_size": 18,
								    "do_normalize": True,
								    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
								    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
								}
								A_								=    os.path.join(self.tmpdirname							, _snake_case						)
								with open(self.image_processor_file							, "w"							, encoding="utf-8"						) as fp:
													json.dump(_snake_case							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Optional[int]							, **_snake_case    :       Tuple						)   ->			List[Any]:







								"""simple docstring"""
								return CLIPTokenizer.from_pretrained(self.tmpdirname							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Optional[Any]							, **_snake_case    :       int						)   ->			str:







								"""simple docstring"""
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Any							, **_snake_case    :       Optional[Any]						)   ->			int:







								"""simple docstring"""
								return ViTImageProcessor.from_pretrained(self.tmpdirname							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Optional[Any]:







								"""simple docstring"""
								shutil.rmtree(self.tmpdirname						)




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			str:







								"""simple docstring"""
								A_								=    [np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta						)]

								A_								=    [Image.fromarray(np.moveaxis(_snake_case							, 0							, -1						)						) for x in image_inputs]

								return image_inputs




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.get_tokenizer()
								A_								=    self.get_rust_tokenizer()
								A_								=    self.get_image_processor()

								A_								=    CLIPSegProcessor(tokenizer=_snake_case							, image_processor=_snake_case						)
								processor_slow.save_pretrained(self.tmpdirname						)
								A_								=    CLIPSegProcessor.from_pretrained(self.tmpdirname							, use_fast=_snake_case						)

								A_								=    CLIPSegProcessor(tokenizer=_snake_case							, image_processor=_snake_case						)
								processor_fast.save_pretrained(self.tmpdirname						)
								A_								=    CLIPSegProcessor.from_pretrained(self.tmpdirname						)

								self.assertEqual(processor_slow.tokenizer.get_vocab()							, tokenizer_slow.get_vocab()						)
								self.assertEqual(processor_fast.tokenizer.get_vocab()							, tokenizer_fast.get_vocab()						)
								self.assertEqual(tokenizer_slow.get_vocab()							, tokenizer_fast.get_vocab()						)
								self.assertIsInstance(processor_slow.tokenizer							, _snake_case						)
								self.assertIsInstance(processor_fast.tokenizer							, _snake_case						)

								self.assertEqual(processor_slow.image_processor.to_json_string()							, image_processor.to_json_string()						)
								self.assertEqual(processor_fast.image_processor.to_json_string()							, image_processor.to_json_string()						)
								self.assertIsInstance(processor_slow.image_processor							, _snake_case						)
								self.assertIsInstance(processor_fast.image_processor							, _snake_case						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			Any:







								"""simple docstring"""
								A_								=    CLIPSegProcessor(tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()						)
								processor.save_pretrained(self.tmpdirname						)

								A_								=    self.get_tokenizer(bos_token="(BOS)"							, eos_token="(EOS)"						)
								A_								=    self.get_image_processor(do_normalize=_snake_case							, padding_value=1.0						)

								A_								=    CLIPSegProcessor.from_pretrained(
								    self.tmpdirname							, bos_token="(BOS)"							, eos_token="(EOS)"							, do_normalize=_snake_case							, padding_value=1.0						)

								self.assertEqual(processor.tokenizer.get_vocab()							, tokenizer_add_kwargs.get_vocab()						)
								self.assertIsInstance(processor.tokenizer							, _snake_case						)

								self.assertEqual(processor.image_processor.to_json_string()							, image_processor_add_kwargs.to_json_string()						)
								self.assertIsInstance(processor.image_processor							, _snake_case						)




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			Dict:







								"""simple docstring"""
								A_								=    self.get_image_processor()
								A_								=    self.get_tokenizer()

								A_								=    CLIPSegProcessor(tokenizer=_snake_case							, image_processor=_snake_case						)

								A_								=    self.prepare_image_inputs()

								A_								=    image_processor(_snake_case							, return_tensors="np"						)
								A_								=    processor(images=_snake_case							, return_tensors="np"						)

								for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()							, input_processor[key].sum()							, delta=1e-2						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			str:







								"""simple docstring"""
								A_								=    self.get_image_processor()
								A_								=    self.get_tokenizer()

								A_								=    CLIPSegProcessor(tokenizer=_snake_case							, image_processor=_snake_case						)

								A_								=    "lower newer"

								A_								=    processor(text=_snake_case						)

								A_								=    tokenizer(_snake_case						)

								for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]							, encoded_processor[key]						)




			def   lowerCamelCase__  (					self    :       Dict						)   ->			List[str]:







								"""simple docstring"""
								A_								=    self.get_image_processor()
								A_								=    self.get_tokenizer()

								A_								=    CLIPSegProcessor(tokenizer=_snake_case							, image_processor=_snake_case						)

								A_								=    "lower newer"
								A_								=    self.prepare_image_inputs()

								A_								=    processor(text=_snake_case							, images=_snake_case						)

								self.assertListEqual(list(inputs.keys()						)							, ["input_ids", "attention_mask", "pixel_values"]						)

								# test if it raises when no input is passed
								with pytest.raises(_snake_case						):
													processor()




			def   lowerCamelCase__  (					self    :       Dict						)   ->			str:







								"""simple docstring"""
								A_								=    self.get_image_processor()
								A_								=    self.get_tokenizer()

								A_								=    CLIPSegProcessor(tokenizer=_snake_case							, image_processor=_snake_case						)

								A_								=    self.prepare_image_inputs()
								A_								=    self.prepare_image_inputs()

								A_								=    processor(images=_snake_case							, visual_prompt=_snake_case						)

								self.assertListEqual(list(inputs.keys()						)							, ["pixel_values", "conditional_pixel_values"]						)

								# test if it raises when no input is passed
								with pytest.raises(_snake_case						):
													processor()






			def   lowerCamelCase__  (					self    :       Dict						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    self.get_image_processor()
								A_								=    self.get_tokenizer()

								A_								=    CLIPSegProcessor(tokenizer=_snake_case							, image_processor=_snake_case						)

								A_								=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								A_								=    processor.batch_decode(_snake_case						)
								A_								=    tokenizer.batch_decode(_snake_case						)

								self.assertListEqual(_snake_case							, _snake_case						)






"""simple docstring"""





import argparse
import os
import re


UpperCamelCase_       :    Any						      =			'''src/transformers/models/auto'''


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
UpperCamelCase_       :    Optional[int]						      =			re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
UpperCamelCase_       :    Tuple						      =			re.compile(R'''\s*\(\s*"(\S[^"]+)"''')
def 				A_  (__a					, __a = False					):

					'''simple docstring'''





					with open(__a					, "r"					, encoding="utf-8"					) as f:
										A_								=    f.read()

					A_								=    content.split("\n"					)
					A_								=    []
					A_								=    0
					while line_idx < len(__a					):
										if _re_intro_mapping.search(lines[line_idx]					) is not None:
															A_								=    len(re.search(R"^(\s*)\S"					, lines[line_idx]					).groups()[0]					) + 8
															# Start of a new mapping!
															while not lines[line_idx].startswith(" " * indent + "("					):
																				new_lines.append(lines[line_idx]					)
																				line_idx += 1

															A_								=    []
															while lines[line_idx].strip() != "]":
																				# Blocks either fit in one line or not
																				if lines[line_idx].strip() == "(":
																									A_								=    line_idx
																									while not lines[line_idx].startswith(" " * indent + ")"					):
																														line_idx += 1
																									blocks.append("\n".join(lines[start_idx : line_idx + 1]					)					)
																				else:
																									blocks.append(lines[line_idx]					)
																				line_idx += 1

															# Sort blocks by their identifiers
															A_								=    sorted(__a					, key=lambda __a					: _re_identifier.search(__a					).groups()[0]					)
															new_lines += blocks
										else:
															new_lines.append(lines[line_idx]					)
															line_idx += 1

					if overwrite:
										with open(__a					, "w"					, encoding="utf-8"					) as f:
															f.write("\n".join(__a					)					)
					elif "\n".join(__a					) != content:
										return True
def 				A_  (__a = False					):

					'''simple docstring'''





					A_								=    [os.path.join(__a					, __a					) for f in os.listdir(__a					) if f.endswith(".py"					)]
					A_								=    [sort_auto_mapping(__a					, overwrite=__a					) for fname in fnames]

					if not overwrite and any(__a					):
										A_								=    [f for f, d in zip(__a					, __a					) if d]
										raise ValueError(
										    f'The following files have auto mappings that need sorting: {", ".join(__a					)}. Run `make style` to fix'
										    " this."					)


if __name__ == "__main__":
		UpperCamelCase_       :    str						      =			argparse.ArgumentParser()
		parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
		UpperCamelCase_       :    List[Any]						      =			parser.parse_args()

		sort_all_auto_mappings(not args.check_only)




