





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    snake_case__(						self:						List[Any]			):
			lowercase__					:      Optional[Any]      =		tempfile.mkdtemp()

			# fmt: off
			lowercase__					:      int      =		['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__					:      Union[str, Any]      =		dict(zip(lowerCamelCase_,					range(len(lowerCamelCase_			)			)			)			)
			lowercase__					:      Dict      =		['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
			lowercase__					:      str      =		{'unk_token': '<unk>'}

			lowercase__					:      str      =		os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['vocab_file']			)
			lowercase__					:      List[Any]      =		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_			)			)

			lowercase__					:      Optional[Any]      =		{
			    '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],
			}
			lowercase__					:      List[str]      =		os.path.join(self.tmpdirname,					lowerCamelCase_			)
			with open(self.image_processor_file,					'w',					encoding='utf-8'			) as fp:
					json.dump(lowerCamelCase_,					lowerCamelCase_			)

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

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						str			):
			return CLIPTokenizerFast.from_pretrained(self.tmpdirname,					**lowerCamelCase_			)

	def    snake_case__(						self:						int,					**lowerCamelCase_:						Tuple			):
			return ViTImageProcessor.from_pretrained(self.tmpdirname,					**lowerCamelCase_			)

	def    snake_case__(						self:						Tuple			):
			shutil.rmtree(self.tmpdirname			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      str      =		[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta			)]

			lowercase__					:      Any      =		[Image.fromarray(np.moveaxis(lowerCamelCase_,					0,					-1			)			) for x in image_inputs]

			return image_inputs

	def    snake_case__(						self:						str			):
			lowercase__					:      List[Any]      =		self.get_tokenizer()
			lowercase__					:      Optional[Any]      =		self.get_rust_tokenizer()
			lowercase__					:      Dict      =		self.get_image_processor()

			lowercase__					:      str      =		CLIPSegProcessor(tokenizer=lowerCamelCase_,					image_processor=lowerCamelCase_			)
			processor_slow.save_pretrained(self.tmpdirname			)
			lowercase__					:      Union[str, Any]      =		CLIPSegProcessor.from_pretrained(self.tmpdirname,					use_fast=lowerCamelCase_			)

			lowercase__					:      Tuple      =		CLIPSegProcessor(tokenizer=lowerCamelCase_,					image_processor=lowerCamelCase_			)
			processor_fast.save_pretrained(self.tmpdirname			)
			lowercase__					:      Dict      =		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    snake_case__(						self:						Dict			):
			lowercase__					:      Tuple      =		CLIPSegProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()			)
			processor.save_pretrained(self.tmpdirname			)

			lowercase__					:      Dict      =		self.get_tokenizer(bos_token='(BOS)',					eos_token='(EOS)'			)
			lowercase__					:      List[Any]      =		self.get_image_processor(do_normalize=lowerCamelCase_,					padding_value=1.0			)

			lowercase__					:      Optional[int]      =		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    snake_case__(						self:						int			):
			lowercase__					:      List[str]      =		self.get_image_processor()
			lowercase__					:      str      =		self.get_tokenizer()

			lowercase__					:      Optional[int]      =		CLIPSegProcessor(tokenizer=lowerCamelCase_,					image_processor=lowerCamelCase_			)

			lowercase__					:      List[Any]      =		self.prepare_image_inputs()

			lowercase__					:      List[str]      =		image_processor(lowerCamelCase_,					return_tensors='np'			)
			lowercase__					:      int      =		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    snake_case__(						self:						int			):
			lowercase__					:      Any      =		self.get_image_processor()
			lowercase__					:      Tuple      =		self.get_tokenizer()

			lowercase__					:      Tuple      =		CLIPSegProcessor(tokenizer=lowerCamelCase_,					image_processor=lowerCamelCase_			)

			lowercase__					:      Tuple      =		'lower newer'

			lowercase__					:      Union[str, Any]      =		processor(text=lowerCamelCase_			)

			lowercase__					:      str      =		tokenizer(lowerCamelCase_			)

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

	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Any      =		self.get_image_processor()
			lowercase__					:      List[Any]      =		self.get_tokenizer()

			lowercase__					:      int      =		CLIPSegProcessor(tokenizer=lowerCamelCase_,					image_processor=lowerCamelCase_			)

			lowercase__					:      List[Any]      =		'lower newer'
			lowercase__					:      Optional[Any]      =		self.prepare_image_inputs()

			lowercase__					:      Optional[Any]      =		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    snake_case__(						self:						str			):
			lowercase__					:      int      =		self.get_image_processor()
			lowercase__					:      str      =		self.get_tokenizer()

			lowercase__					:      List[str]      =		CLIPSegProcessor(tokenizer=lowerCamelCase_,					image_processor=lowerCamelCase_			)

			lowercase__					:      Union[str, Any]      =		self.prepare_image_inputs()
			lowercase__					:      str      =		self.prepare_image_inputs()

			lowercase__					:      Tuple      =		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    snake_case__(						self:						Dict			):
			lowercase__					:      Union[str, Any]      =		self.get_image_processor()
			lowercase__					:      Dict      =		self.get_tokenizer()

			lowercase__					:      Union[str, Any]      =		CLIPSegProcessor(tokenizer=lowerCamelCase_,					image_processor=lowerCamelCase_			)

			lowercase__					:      Tuple      =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			lowercase__					:      Tuple      =		processor.batch_decode(lowerCamelCase_			)
			lowercase__					:      Tuple      =		tokenizer.batch_decode(lowerCamelCase_			)

			self.assertListEqual(lowerCamelCase_,					lowerCamelCase_			)









from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42
	_A													=				42


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


