
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 CLIPImageProcessor, CLIPProcessor







@require_vision
class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												lowercase							=						tempfile.mkdtemp()

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

												lowercase							=						os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['''vocab_file'''])
												lowercase							=						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__))

												lowercase							=						{
												    '''do_resize''': True,
												    '''size''': 2_0,
												    '''do_center_crop''': True,
												    '''crop_size''': 1_8,
												    '''do_normalize''': True,
												    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
												    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
												}
												lowercase							=						os.path.join(self.tmpdirname   ,A__)
												with open(self.image_processor_file   ,'''w'''   ,encoding='''utf-8''') as fp:
																	json.dump(A__   ,A__)




							def  A__      (				self   ,**A__):
												return CLIPTokenizer.from_pretrained(self.tmpdirname   ,**A__)




							def  A__      (				self   ,**A__):
												return CLIPTokenizerFast.from_pretrained(self.tmpdirname   ,**A__)




							def  A__      (				self   ,**A__):
												return CLIPImageProcessor.from_pretrained(self.tmpdirname   ,**A__)




							def  A__      (				self):
												shutil.rmtree(self.tmpdirname)




							def  A__      (				self):
												lowercase							=						[np.random.randint(2_5_5   ,size=(3, 3_0, 4_0_0)   ,dtype=np.uinta)]

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

												return image_inputs




							def  A__      (				self):
												lowercase							=						self.get_tokenizer()
												lowercase							=						self.get_rust_tokenizer()
												lowercase							=						self.get_image_processor()

												lowercase							=						CLIPProcessor(tokenizer=A__   ,image_processor=A__)
												processor_slow.save_pretrained(self.tmpdirname)
												lowercase							=						CLIPProcessor.from_pretrained(self.tmpdirname   ,use_fast=A__)

												lowercase							=						CLIPProcessor(tokenizer=A__   ,image_processor=A__)
												processor_fast.save_pretrained(self.tmpdirname)
												lowercase							=						CLIPProcessor.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  A__      (				self):
												lowercase							=						CLIPProcessor(tokenizer=self.get_tokenizer()   ,image_processor=self.get_image_processor())
												processor.save_pretrained(self.tmpdirname)

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

												lowercase							=						CLIPProcessor.from_pretrained(
												    self.tmpdirname   ,bos_token='''(BOS)'''   ,eos_token='''(EOS)'''   ,do_normalize=A__   ,padding_value=1.0)

												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  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						CLIPProcessor(tokenizer=A__   ,image_processor=A__)

												lowercase							=						self.prepare_image_inputs()

												lowercase							=						image_processor(A__   ,return_tensors='''np''')
												lowercase							=						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  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						CLIPProcessor(tokenizer=A__   ,image_processor=A__)

												lowercase							=						'''lower newer'''

												lowercase							=						processor(text=A__)

												lowercase							=						tokenizer(A__)

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




							def  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						CLIPProcessor(tokenizer=A__   ,image_processor=A__)

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

												lowercase							=						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  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						CLIPProcessor(tokenizer=A__   ,image_processor=A__)

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

												lowercase							=						processor.batch_decode(A__)
												lowercase							=						tokenizer.batch_decode(A__)

												self.assertListEqual(A__   ,A__)





							def  A__      (				self):
												lowercase							=						self.get_image_processor()
												lowercase							=						self.get_tokenizer()

												lowercase							=						CLIPProcessor(tokenizer=A__   ,image_processor=A__)

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

												lowercase							=						processor(text=A__   ,images=A__)

												self.assertListEqual(list(inputs.keys())   ,processor.model_input_names)






import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       List[Any]       					=IFInpaintingPipeline
							lowercase_       :       Optional[int]       					=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							lowercase_       :       Any       					=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_       :       str       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, 3, 3_2, 3_2)   ,rng=random.Random(A__)).to(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''image''': image,
												    '''mask_image''': mask_image,
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()





							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)




