





"""simple docstring"""





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class 							snake_case							:

						'''simple docstring'''



						def       A_					(							self       :      Any ):


								'''simple docstring'''




								torch.manual_seed(0 )
								__UpperCAmelCase    :						Optional[int]       =						TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

								torch.manual_seed(0 )
								__UpperCAmelCase    :						Any       =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

								torch.manual_seed(0 )
								__UpperCAmelCase    :						Any       =						UNetaDConditionModel(
								    sample_size=32						,				layers_per_block=1						,				block_out_channels=[32, 64]						,				down_block_types=[
								        '''ResnetDownsampleBlock2D''',
								        '''SimpleCrossAttnDownBlock2D''',
								    ]						,				mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''						,				up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']						,				in_channels=3						,				out_channels=6						,				cross_attention_dim=32						,				encoder_hid_dim=32						,				attention_head_dim=8						,				addition_embed_type='''text'''						,				addition_embed_type_num_heads=2						,				cross_attention_norm='''group_norm'''						,				resnet_time_scale_shift='''scale_shift'''						,				act_fn='''gelu'''						,				)
								unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								torch.manual_seed(0 )
								__UpperCAmelCase    :						List[str]       =						DDPMScheduler(
								    num_train_timesteps=1_000						,				beta_schedule='''squaredcos_cap_v2'''						,				beta_start=0.0_0_0_1						,				beta_end=0.0_2						,				thresholding=__lowercase						,				dynamic_thresholding_ratio=0.9_5						,				sample_max_value=1.0						,				prediction_type='''epsilon'''						,				variance_type='''learned_range'''						,				)

								torch.manual_seed(0 )
								__UpperCAmelCase    :						int       =						IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}

						def       A_					(							self       :      Optional[int] ):


								'''simple docstring'''




								torch.manual_seed(0 )
								__UpperCAmelCase    :						Optional[int]       =						TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

								torch.manual_seed(0 )
								__UpperCAmelCase    :						List[str]       =						AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )

								torch.manual_seed(0 )
								__UpperCAmelCase    :						str       =						UNetaDConditionModel(
								    sample_size=32						,				layers_per_block=[1, 2]						,				block_out_channels=[32, 64]						,				down_block_types=[
								        '''ResnetDownsampleBlock2D''',
								        '''SimpleCrossAttnDownBlock2D''',
								    ]						,				mid_block_type='''UNetMidBlock2DSimpleCrossAttn'''						,				up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D''']						,				in_channels=6						,				out_channels=6						,				cross_attention_dim=32						,				encoder_hid_dim=32						,				attention_head_dim=8						,				addition_embed_type='''text'''						,				addition_embed_type_num_heads=2						,				cross_attention_norm='''group_norm'''						,				resnet_time_scale_shift='''scale_shift'''						,				act_fn='''gelu'''						,				class_embed_type='''timestep'''						,				mid_block_scale_factor=1.4_1_4						,				time_embedding_act_fn='''gelu'''						,				time_embedding_dim=32						,				)
								unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								torch.manual_seed(0 )
								__UpperCAmelCase    :						Optional[Any]       =						DDPMScheduler(
								    num_train_timesteps=1_000						,				beta_schedule='''squaredcos_cap_v2'''						,				beta_start=0.0_0_0_1						,				beta_end=0.0_2						,				thresholding=__lowercase						,				dynamic_thresholding_ratio=0.9_5						,				sample_max_value=1.0						,				prediction_type='''epsilon'''						,				variance_type='''learned_range'''						,				)

								torch.manual_seed(0 )
								__UpperCAmelCase    :						Dict       =						DDPMScheduler(
								    num_train_timesteps=1_000						,				beta_schedule='''squaredcos_cap_v2'''						,				beta_start=0.0_0_0_1						,				beta_end=0.0_2						,				)

								torch.manual_seed(0 )
								__UpperCAmelCase    :						List[str]       =						IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}

						def       A_					(							self       :      Optional[int] ):


								'''simple docstring'''




								__UpperCAmelCase    :						Any       =						self.get_dummy_components()
								__UpperCAmelCase    :						Optional[Any]       =						self.pipeline_class(**__lowercase )
								pipe.to(__lowercase )
								pipe.set_progress_bar_config(disable=__lowercase )

								__UpperCAmelCase    :						Optional[Any]       =						self.get_dummy_inputs(__lowercase )

								__UpperCAmelCase    :						List[Any]       =						inputs['''prompt''']
								__UpperCAmelCase    :						Optional[int]       =						inputs['''generator''']
								__UpperCAmelCase    :						Optional[int]       =						inputs['''num_inference_steps''']
								__UpperCAmelCase    :						List[str]       =						inputs['''output_type''']

								if "image" in inputs:
										__UpperCAmelCase    :						int       =						inputs['''image''']
								else:
										__UpperCAmelCase    :						Any       =						None

								if "mask_image" in inputs:
										__UpperCAmelCase    :						List[str]       =						inputs['''mask_image''']
								else:
										__UpperCAmelCase    :						str       =						None

								if "original_image" in inputs:
										__UpperCAmelCase    :						Union[str, Any]       =						inputs['''original_image''']
								else:
										__UpperCAmelCase    :						int       =						None

								__UpperCAmelCase    :						Union[str, Any]       =						pipe.encode_prompt(__lowercase )

								# inputs with prompt converted to embeddings
								__UpperCAmelCase    :						int       =						{
								    '''prompt_embeds''': prompt_embeds,
								    '''negative_prompt_embeds''': negative_prompt_embeds,
								    '''generator''': generator,
								    '''num_inference_steps''': num_inference_steps,
								    '''output_type''': output_type,
								}

								if image is not None:
										__UpperCAmelCase    :						Any       =						image

								if mask_image is not None:
										__UpperCAmelCase    :						int       =						mask_image

								if original_image is not None:
										__UpperCAmelCase    :						List[Any]       =						original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
										setattr(__lowercase						,				__lowercase						,				__lowercase )

								__UpperCAmelCase    :						Optional[Any]       =						pipe(**__lowercase )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__lowercase )
										__UpperCAmelCase    :						Optional[int]       =						self.pipeline_class.from_pretrained(__lowercase )
										pipe_loaded.to(__lowercase )
										pipe_loaded.set_progress_bar_config(disable=__lowercase )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(__lowercase						,				__lowercase ) is None						,				f'''`{optional_component}` did not stay set to None after loading.'''						,				)

								__UpperCAmelCase    :						str       =						self.get_dummy_inputs(__lowercase )

								__UpperCAmelCase    :						Union[str, Any]       =						inputs['''generator''']
								__UpperCAmelCase    :						str       =						inputs['''num_inference_steps''']
								__UpperCAmelCase    :						Any       =						inputs['''output_type''']

								# inputs with prompt converted to embeddings
								__UpperCAmelCase    :						List[Any]       =						{
								    '''prompt_embeds''': prompt_embeds,
								    '''negative_prompt_embeds''': negative_prompt_embeds,
								    '''generator''': generator,
								    '''num_inference_steps''': num_inference_steps,
								    '''output_type''': output_type,
								}

								if image is not None:
										__UpperCAmelCase    :						Union[str, Any]       =						image

								if mask_image is not None:
										__UpperCAmelCase    :						Tuple       =						mask_image

								if original_image is not None:
										__UpperCAmelCase    :						Optional[int]       =						original_image

								__UpperCAmelCase    :						Tuple       =						pipe_loaded(**__lowercase )[0]

								__UpperCAmelCase    :						Optional[Any]       =						np.abs(to_np(__lowercase ) - to_np(__lowercase ) ).max()
								self.assertLess(__lowercase						,				1e-4 )







						def       A_					(							self       :      Dict ):


								'''simple docstring'''




								__UpperCAmelCase    :						Any       =						self.get_dummy_components()
								__UpperCAmelCase    :						Union[str, Any]       =						self.pipeline_class(**__lowercase )
								pipe.to(__lowercase )
								pipe.set_progress_bar_config(disable=__lowercase )

								__UpperCAmelCase    :						List[str]       =						self.get_dummy_inputs(__lowercase )
								__UpperCAmelCase    :						int       =						pipe(**__lowercase )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__lowercase )
										__UpperCAmelCase    :						str       =						self.pipeline_class.from_pretrained(__lowercase )
										pipe_loaded.to(__lowercase )
										pipe_loaded.set_progress_bar_config(disable=__lowercase )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

								__UpperCAmelCase    :						List[Any]       =						self.get_dummy_inputs(__lowercase )
								__UpperCAmelCase    :						Optional[Any]       =						pipe_loaded(**__lowercase )[0]

								__UpperCAmelCase    :						Tuple       =						np.abs(to_np(__lowercase ) - to_np(__lowercase ) ).max()
								self.assertLess(__lowercase						,				1e-4 )






"""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 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						tempfile.mkdtemp()

        # fmt: off
        __UpperCAmelCase    :						Optional[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
        __UpperCAmelCase    :						List[str]       =						dict(zip(__lowercase						,				range(len(__lowercase ) ) ) )
        __UpperCAmelCase    :						Any       =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        __UpperCAmelCase    :						Dict       =						{'''unk_token''': '''<unk>'''}

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

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

      def       A_					(							self       :      Optional[Any]						,				**__lowercase       :      Tuple ):


        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Any						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Dict						,				**__lowercase       :      str ):


        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        shutil.rmtree(self.tmpdirname )

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						str       =						[np.random.randint(255						,				size=(3, 30, 400)						,				dtype=np.uinta )]

        __UpperCAmelCase    :						int       =						[Image.fromarray(np.moveaxis(__lowercase						,				0						,				-1 ) ) for x in image_inputs]

        return image_inputs

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_tokenizer()
        __UpperCAmelCase    :						Dict       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Optional[Any]       =						self.get_image_processor()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_slow.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor.from_pretrained(self.tmpdirname						,				use_fast=__lowercase )

        __UpperCAmelCase    :						Tuple       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_fast.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						str       =						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						,				__lowercase )
        self.assertIsInstance(processor_fast.tokenizer						,				__lowercase )

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

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

        __UpperCAmelCase    :						Any       =						self.get_tokenizer(bos_token='''(BOS)'''						,				eos_token='''(EOS)''' )
        __UpperCAmelCase    :						Tuple       =						self.get_image_processor(do_normalize=__lowercase						,				padding_value=1.0 )

        __UpperCAmelCase    :						List[str]       =						CLIPSegProcessor.from_pretrained(
            self.tmpdirname						,				bos_token='''(BOS)'''						,				eos_token='''(EOS)'''						,				do_normalize=__lowercase						,				padding_value=1.0 )

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

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

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.get_image_processor()
        __UpperCAmelCase    :						List[str]       =						self.get_tokenizer()

        __UpperCAmelCase    :						Optional[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

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

        __UpperCAmelCase    :						Dict       =						image_processor(__lowercase						,				return_tensors='''np''' )
        __UpperCAmelCase    :						Optional[int]       =						processor(images=__lowercase						,				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       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						int       =						'''lower newer'''

        __UpperCAmelCase    :						int       =						processor(text=__lowercase )

        __UpperCAmelCase    :						Any       =						tokenizer(__lowercase )

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Union[str, Any]       =						self.get_tokenizer()

        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						str       =						'''lower newer'''
        __UpperCAmelCase    :						int       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						str       =						processor(text=__lowercase						,				images=__lowercase )

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

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.get_image_processor()
        __UpperCAmelCase    :						str       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

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

        __UpperCAmelCase    :						Tuple       =						processor(images=__lowercase						,				visual_prompt=__lowercase )

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

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







      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Dict       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase    :						int       =						processor.batch_decode(__lowercase )
        __UpperCAmelCase    :						int       =						tokenizer.batch_decode(__lowercase )

        self.assertListEqual(__lowercase						,				__lowercase )