




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  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    tempfile.mkdtemp()

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

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

											SCREAMING_SNAKE_CASE_   	=    {
											    '''do_resize''': True,
											    '''size''': 20,
											    '''do_center_crop''': True,
											    '''crop_size''': 18,
											    '''do_normalize''': True,
											    '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
											    '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
											}
											SCREAMING_SNAKE_CASE_   	=    os.path.join(self.tmpdirname		,					_lowerCamelCase		)
											with open(self.image_processor_file		,					'''w'''		,					encoding='''utf-8'''		) as fp:
																		json.dump(_lowerCamelCase		,					_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					**_lowerCamelCase:   str		):
											return CLIPTokenizer.from_pretrained(self.tmpdirname		,					**_lowerCamelCase		)


				def     _A							(							self:   Dict		,					**_lowerCamelCase:   int		):
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,					**_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					**_lowerCamelCase:   Optional[Any]		):
											return ViTImageProcessor.from_pretrained(self.tmpdirname		,					**_lowerCamelCase		)


				def     _A							(							self:   int		):
											shutil.rmtree(self.tmpdirname		)


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    [np.random.randint(2_55		,					size=(3, 30, 4_00)		,					dtype=np.uinta		)]

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

											return image_inputs


				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()
											SCREAMING_SNAKE_CASE_   	=    self.get_rust_tokenizer()
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)
											processor_slow.save_pretrained(self.tmpdirname		)
											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor.from_pretrained(self.tmpdirname		,					use_fast=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)
											processor_fast.save_pretrained(self.tmpdirname		)
											SCREAMING_SNAKE_CASE_   	=    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		,					_lowerCamelCase		)
											self.assertIsInstance(processor_fast.tokenizer		,					_lowerCamelCase		)

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


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=self.get_tokenizer()		,					image_processor=self.get_image_processor()		)
											processor.save_pretrained(self.tmpdirname		)

											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer(bos_token='''(BOS)'''		,					eos_token='''(EOS)'''		)
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor(do_normalize=_lowerCamelCase		,					padding_value=1.0		)

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor.from_pretrained(
											    self.tmpdirname		,					bos_token='''(BOS)'''		,					eos_token='''(EOS)'''		,					do_normalize=_lowerCamelCase		,					padding_value=1.0		)

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

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


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_   	=    image_processor(_lowerCamelCase		,					return_tensors='''np'''		)
											SCREAMING_SNAKE_CASE_   	=    processor(images=_lowerCamelCase		,					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     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    '''lower newer'''

											SCREAMING_SNAKE_CASE_   	=    processor(text=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    tokenizer(_lowerCamelCase		)

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


				def     _A							(							self:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

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

											SCREAMING_SNAKE_CASE_   	=    processor(text=_lowerCamelCase		,					images=_lowerCamelCase		)

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

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


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    self.prepare_image_inputs()
											SCREAMING_SNAKE_CASE_   	=    self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE_   	=    processor(images=_lowerCamelCase		,					visual_prompt=_lowerCamelCase		)

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

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





				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    self.get_image_processor()
											SCREAMING_SNAKE_CASE_   	=    self.get_tokenizer()

											SCREAMING_SNAKE_CASE_   	=    CLIPSegProcessor(tokenizer=_lowerCamelCase		,					image_processor=_lowerCamelCase		)

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

											SCREAMING_SNAKE_CASE_   	=    processor.batch_decode(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    tokenizer.batch_decode(_lowerCamelCase		)

											self.assertListEqual(_lowerCamelCase		,					_lowerCamelCase		)








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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					"swin2sr"

				SCREAMING_SNAKE_CASE__					:							str	      =					{
				    "hidden_size": "embed_dim",
				    "num_attention_heads": "num_heads",
				    "num_hidden_layers": "num_layers",
				}


				def __init__(							self:   Optional[int]		,					_lowerCamelCase:   Dict=64		,					_lowerCamelCase:   str=1		,					_lowerCamelCase:   Optional[int]=3		,					_lowerCamelCase:   List[Any]=1_80		,					_lowerCamelCase:   Optional[int]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Union[str, Any]=[6, 6, 6, 6, 6, 6]		,					_lowerCamelCase:   Tuple=8		,					_lowerCamelCase:   List[Any]=2.0		,					_lowerCamelCase:   Dict=True		,					_lowerCamelCase:   Dict=0.0		,					_lowerCamelCase:   int=0.0		,					_lowerCamelCase:   Optional[int]=0.1		,					_lowerCamelCase:   Any="gelu"		,					_lowerCamelCase:   int=False		,					_lowerCamelCase:   Tuple=0.02		,					_lowerCamelCase:   List[str]=1E-5		,					_lowerCamelCase:   List[Any]=2		,					_lowerCamelCase:   str=1.0		,					_lowerCamelCase:   List[Any]="1conv"		,					_lowerCamelCase:   str="pixelshuffle"		,					**_lowerCamelCase:   List[str]		,					):
											super().__init__(**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    image_size
											SCREAMING_SNAKE_CASE_   	=    patch_size
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    embed_dim
											SCREAMING_SNAKE_CASE_   	=    depths
											SCREAMING_SNAKE_CASE_   	=    len(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    num_heads
											SCREAMING_SNAKE_CASE_   	=    window_size
											SCREAMING_SNAKE_CASE_   	=    mlp_ratio
											SCREAMING_SNAKE_CASE_   	=    qkv_bias
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    drop_path_rate
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    use_absolute_embeddings
											SCREAMING_SNAKE_CASE_   	=    layer_norm_eps
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    upscale
											SCREAMING_SNAKE_CASE_   	=    img_range
											SCREAMING_SNAKE_CASE_   	=    resi_connection
											SCREAMING_SNAKE_CASE_   	=    upsampler


