
import argparse
import os
import re


_UpperCAmelCase :			Union[str, Any]              =	"src/transformers/models/auto"


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase :			Tuple              =	re.compile(R"[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict")
# re pattern that matches identifiers in mappings
_UpperCAmelCase :			Dict              =	re.compile(R"\s*\(\s*\"(\S[^\"]+)\"")


def 				A	( lowercase   ,	lowercase = False			)       ->  List[Any]:
		'''simple docstring'''

		with open(lowercase   ,	'r'   ,	encoding='utf-8'			) as f:
				UpperCamelCase          =					f.read()

		UpperCamelCase          =					content.split('\n'			)
		UpperCamelCase          =					[]
		UpperCamelCase          =					0
		while line_idx < len(lowercase			):
				if _re_intro_mapping.search(lines[line_idx]			) is not None:
						UpperCamelCase          =					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

						UpperCamelCase          =					[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										UpperCamelCase          =					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
						UpperCamelCase          =					sorted(lowercase   ,	key=lambda lowercase			: _re_identifier.search(lowercase			).groups()[0]			)
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]			)
						line_idx += 1

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


def 				A	( lowercase = False			)       ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase          =					[os.path.join(lowercase   ,	lowercase			) for f in os.listdir(lowercase			) if f.endswith('.py'			)]
		UpperCamelCase          =					[sort_auto_mapping(lowercase   ,	overwrite=lowercase			) for fname in fnames]

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


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

					sort_all_auto_mappings(not args.check_only)


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 OwlViTImageProcessor, OwlViTProcessor


@require_vision
class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					tempfile.mkdtemp()

				# fmt: off
				UpperCamelCase          =					['', '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
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

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

				UpperCamelCase          =					{
				    'do_resize': True,
				    'size': 20,
				    'do_center_crop': True,
				    'crop_size': 18,
				    'do_normalize': True,
				    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
				    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				A_	)
				with open(self.image_processor_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						json.dump(A_    ,				A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return CLIPTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Tuple:
				"""simple docstring"""
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[np.random.randint(255    ,				size=(3, 30, 400)    ,				dtype=np.uinta	)]

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

				return image_inputs



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_tokenizer()
				UpperCamelCase          =					self.get_rust_tokenizer()
				UpperCamelCase          =					self.get_image_processor()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_slow.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname    ,				use_fast=A_	)

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_fast.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.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    ,				A_	)
				self.assertIsInstance(processor_fast.tokenizer    ,				A_	)

				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    ,				A_	)
				self.assertIsInstance(processor_fast.image_processor    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					OwlViTProcessor(tokenizer=self.get_tokenizer()    ,				image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				UpperCamelCase          =					self.get_tokenizer(bos_token='(BOS)'    ,				eos_token='(EOS)'	)
				UpperCamelCase          =					self.get_image_processor(do_normalize=A_	)

				UpperCamelCase          =					OwlViTProcessor.from_pretrained(
				    self.tmpdirname    ,				bos_token='(BOS)'    ,				eos_token='(EOS)'    ,				do_normalize=A_	)

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

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



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					image_processor(A_    ,				return_tensors='np'	)
				UpperCamelCase          =					processor(images=A_    ,				return_tensors='np'	)

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



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'

				UpperCamelCase          =					processor(text=A_    ,				return_tensors='np'	)

				UpperCamelCase          =					tokenizer(A_    ,				return_tensors='np'	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key][0].tolist()    ,				encoded_processor[key][0].tolist()	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'
				UpperCamelCase          =					self.prepare_image_inputs()

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

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

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



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)

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



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					[['cat', 'nasa badge'], ['person']]
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					len(A_	)
				UpperCamelCase          =					max([len(A_	) for texts in input_texts]	)

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(batch_size * num_max_text_queries, seq_length)	)

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



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					inputs['input_ids']
				UpperCamelCase          =					[
				    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				]

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)
				self.assertListEqual(list(input_ids[0]	)    ,				predicted_ids[0]	)
				self.assertListEqual(list(input_ids[1]	)    ,				predicted_ids[1]	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(images=A_    ,				query_images=A_	)

				self.assertListEqual(list(inputs.keys()	)    ,				['query_pixel_values', 'pixel_values']	)

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



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

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

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

				self.assertListEqual(A_    ,				A_	)
