
import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 FlavaImageProcessor, FlavaProcessor
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )





@require_vision
class 							A__		(     unittest.TestCase):
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				tempfile.mkdtemp()

       # fmt: off
       lowerCamelCase		:     Dict          =				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
       # fmt: on
       lowerCamelCase		:     str          =				os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]				)

       with open(self.vocab_file   ,  """w"""   ,  encoding="""utf-8"""				) as fp:
        fp.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

       lowerCamelCase		:     int          =				{
           'image_mean': FLAVA_IMAGE_MEAN,
           'image_std': FLAVA_IMAGE_STD,
           'do_normalize': True,
           'do_resize': True,
           'size': 2_2_4,
           'do_center_crop': True,
           'crop_size': 2_2_4,
           'input_size_patches': 1_4,
           'total_mask_patches': 7_5,
           'mask_group_max_patches': None,
           'mask_group_min_patches': 1_6,
           'mask_group_min_aspect_ratio': 0.3,
           'mask_group_max_aspect_ratio': None,
           'codebook_do_resize': True,
           'codebook_size': 1_1_2,
           'codebook_do_center_crop': True,
           'codebook_crop_size': 1_1_2,
           'codebook_do_map_pixels': True,
           'codebook_do_normalize': True,
           'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
           'codebook_image_std': FLAVA_CODEBOOK_STD,
       }

       lowerCamelCase		:     Dict          =				os.path.join(self.tmpdirname   ,  _snake_case				)
       with open(self.image_processor_file   ,  """w"""   ,  encoding="""utf-8"""				) as fp:
        json.dump(_snake_case   ,  _snake_case				)
      def 						UpperCamelCase__							(				self   ,  **__magic_name__				):
       return BertTokenizer.from_pretrained(self.tmpdirname   ,  **_snake_case				)
      def 						UpperCamelCase__							(				self   ,  **__magic_name__				):
       return BertTokenizerFast.from_pretrained(self.tmpdirname   ,  **_snake_case				)
      def 						UpperCamelCase__							(				self   ,  **__magic_name__				):
       return FlavaImageProcessor.from_pretrained(self.tmpdirname   ,  **_snake_case				)
      def 						UpperCamelCase__							(				self				):
       shutil.rmtree(self.tmpdirname				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				[np.random.randint(2_5_5   ,  size=(3, 3_0, 4_0_0)   ,  dtype=np.uinta				)]

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

       return image_inputs
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[Any]          =				self.get_tokenizer()
       lowerCamelCase		:     List[str]          =				self.get_rust_tokenizer()
       lowerCamelCase		:     Dict          =				self.get_image_processor()

       lowerCamelCase		:     int          =				FlavaProcessor(tokenizer=_snake_case   ,  image_processor=_snake_case				)
       processor_slow.save_pretrained(self.tmpdirname				)
       lowerCamelCase		:     Any          =				FlavaProcessor.from_pretrained(self.tmpdirname   ,  use_fast=_snake_case				)

       lowerCamelCase		:     Any          =				FlavaProcessor(tokenizer=_snake_case   ,  image_processor=_snake_case				)
       processor_fast.save_pretrained(self.tmpdirname				)
       lowerCamelCase		:     Union[str, Any]          =				FlavaProcessor.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   ,  _snake_case				)
       self.assertIsInstance(processor_fast.tokenizer   ,  _snake_case				)

       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   ,  _snake_case				)
       self.assertIsInstance(processor_fast.image_processor   ,  _snake_case				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				FlavaProcessor(tokenizer=self.get_tokenizer()   ,  image_processor=self.get_image_processor()				)
       processor.save_pretrained(self.tmpdirname				)

       lowerCamelCase		:     str          =				self.get_tokenizer(bos_token="""(BOS)"""   ,  eos_token="""(EOS)"""				)
       lowerCamelCase		:     str          =				self.get_image_processor(do_normalize=_snake_case   ,  padding_value=1.0				)

       lowerCamelCase		:     Optional[int]          =				FlavaProcessor.from_pretrained(
           self.tmpdirname   ,  bos_token="""(BOS)"""   ,  eos_token="""(EOS)"""   ,  do_normalize=_snake_case   ,  padding_value=1.0				)

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

       self.assertEqual(processor.image_processor.to_json_string()   ,  image_processor_add_kwargs.to_json_string()				)
       self.assertIsInstance(processor.image_processor   ,  _snake_case				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				self.get_image_processor()
       lowerCamelCase		:     List[str]          =				self.get_tokenizer()

       lowerCamelCase		:     Union[str, Any]          =				FlavaProcessor(tokenizer=_snake_case   ,  image_processor=_snake_case				)

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

       lowerCamelCase		:     Optional[int]          =				image_processor(_snake_case   ,  return_tensors="""np"""				)
       lowerCamelCase		:     List[Any]          =				processor(images=_snake_case   ,  return_tensors="""np"""				)

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

       # With rest of the args
       random.seed(1_2_3_4				)
       lowerCamelCase		:     Optional[Any]          =				image_processor(
           _snake_case   ,  return_image_mask=_snake_case   ,  return_codebook_pixels=_snake_case   ,  return_tensors="""np"""				)
       random.seed(1_2_3_4				)
       lowerCamelCase		:     List[str]          =				processor(
           images=_snake_case   ,  return_image_mask=_snake_case   ,  return_codebook_pixels=_snake_case   ,  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 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				self.get_image_processor()
       lowerCamelCase		:     int          =				self.get_tokenizer()

       lowerCamelCase		:     Dict          =				FlavaProcessor(tokenizer=_snake_case   ,  image_processor=_snake_case				)

       lowerCamelCase		:     str          =				'lower newer'

       lowerCamelCase		:     List[str]          =				processor(text=_snake_case				)

       lowerCamelCase		:     Any          =				tokenizer(_snake_case				)

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]   ,  encoded_processor[key]				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				self.get_image_processor()
       lowerCamelCase		:     Optional[int]          =				self.get_tokenizer()

       lowerCamelCase		:     Dict          =				FlavaProcessor(tokenizer=_snake_case   ,  image_processor=_snake_case				)

       lowerCamelCase		:     str          =				'lower newer'
       lowerCamelCase		:     Optional[int]          =				self.prepare_image_inputs()

       lowerCamelCase		:     Optional[Any]          =				processor(text=_snake_case   ,  images=_snake_case				)

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

       # add extra args
       lowerCamelCase		:     Any          =				processor(text=_snake_case   ,  images=_snake_case   ,  return_codebook_pixels=_snake_case   ,  return_image_mask=_snake_case				)

       self.assertListEqual(
           list(inputs.keys()				)   ,  [
               """input_ids""",
               """token_type_ids""",
               """attention_mask""",
               """pixel_values""",
               """codebook_pixel_values""",
               """bool_masked_pos""",
           ]   ,  )

       # test if it raises when no input is passed
       with pytest.raises(_snake_case				):
        processor()
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				self.get_image_processor()
       lowerCamelCase		:     Tuple          =				self.get_tokenizer()

       lowerCamelCase		:     List[str]          =				FlavaProcessor(tokenizer=_snake_case   ,  image_processor=_snake_case				)

       lowerCamelCase		:     Optional[int]          =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowerCamelCase		:     List[str]          =				processor.batch_decode(_snake_case				)
       lowerCamelCase		:     int          =				tokenizer.batch_decode(_snake_case				)

       self.assertListEqual(_snake_case   ,  _snake_case				)






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				self.get_image_processor()
       lowerCamelCase		:     Any          =				self.get_tokenizer()

       lowerCamelCase		:     Optional[int]          =				FlavaProcessor(tokenizer=_snake_case   ,  image_processor=_snake_case				)

       lowerCamelCase		:     str          =				'lower newer'
       lowerCamelCase		:     str          =				self.prepare_image_inputs()

       lowerCamelCase		:     Optional[int]          =				processor(text=_snake_case   ,  images=_snake_case				)

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






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 							A__		(     __SCREAMING_SNAKE_CASE  ,       unittest.TestCase):
      _UpperCAmelCase    :   Any   	=      KandinskyPipeline
      _UpperCAmelCase    :   Any   	=      [
          """prompt""",
          """image_embeds""",
          """negative_image_embeds""",
      ]
      _UpperCAmelCase    :   Union[str, Any]   	=      ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
      _UpperCAmelCase    :   Dict   	=      [
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      _UpperCAmelCase    :   List[Any]   	=      False
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return 3_2
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim
      @property
      def 						UpperCamelCase__							(				self				):
       return self.time_input_dim * 4
      @property
      def 						UpperCamelCase__							(				self				):
       return 1_0_0
      @property
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[Any]          =				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
       return tokenizer
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Union[str, Any]          =				MCLIPConfig(
           numDims=self.cross_attention_dim   ,  transformerDimensions=self.text_embedder_hidden_size   ,  hidden_size=self.text_embedder_hidden_size   ,  intermediate_size=3_7   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  vocab_size=1_0_0_5   ,  )

       lowerCamelCase		:     Dict          =				MultilingualCLIP(__magic_name__				)
       lowerCamelCase		:     Optional[Any]          =				text_encoder.eval()

       return text_encoder
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)

       lowerCamelCase		:     Any          =				{
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       lowerCamelCase		:     Optional[int]          =				UNetaDConditionModel(**__magic_name__				)
       return model
      @property
      def 						UpperCamelCase__							(				self				):
       return {
           "block_out_channels": [3_2, 6_4],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 1_2,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
      @property
      def 						UpperCamelCase__							(				self				):
       torch.manual_seed(0				)
       lowerCamelCase		:     Dict          =				VQModel(**self.dummy_movq_kwargs				)
       return model
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				self.dummy_text_encoder
       lowerCamelCase		:     int          =				self.dummy_tokenizer
       lowerCamelCase		:     Optional[int]          =				self.dummy_unet
       lowerCamelCase		:     str          =				self.dummy_movq

       lowerCamelCase		:     Optional[int]          =				DDIMScheduler(
           num_train_timesteps=1_0_0_0   ,  beta_schedule="""linear"""   ,  beta_start=0.00_085   ,  beta_end=0.012   ,  clip_sample=__magic_name__   ,  set_alpha_to_one=__magic_name__   ,  steps_offset=1   ,  prediction_type="""epsilon"""   ,  thresholding=__magic_name__   ,  )

       lowerCamelCase		:     Any          =				{
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }
       return components
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__=0				):
       lowerCamelCase		:     str          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(__magic_name__				)				).to(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				floats_tensor((1, self.cross_attention_dim)   ,  rng=random.Random(seed + 1				)				).to(__magic_name__				)
       if str(__magic_name__				).startswith("""mps"""				):
        lowerCamelCase		:     Union[str, Any]          =				torch.manual_seed(__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
       lowerCamelCase		:     Optional[int]          =				{
           """prompt""": """horse""",
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 6_4,
           """width""": 6_4,
           """guidance_scale""": 4.0,
           """num_inference_steps""": 2,
           """output_type""": """np""",
       }
       return inputs






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				"""cpu"""

       lowerCamelCase		:     str          =				self.get_dummy_components()

       lowerCamelCase		:     Optional[Any]          =				self.pipeline_class(**__magic_name__				)
       lowerCamelCase		:     str          =				pipe.to(__magic_name__				)

       pipe.set_progress_bar_config(disable=__magic_name__				)

       lowerCamelCase		:     str          =				pipe(**self.get_dummy_inputs(__magic_name__				)				)
       lowerCamelCase		:     str          =				output.images

       lowerCamelCase		:     Dict          =				pipe(
           **self.get_dummy_inputs(__magic_name__				)   ,  return_dict=__magic_name__   ,  )[0]

       lowerCamelCase		:     int          =				image[0, -3:, -3:, -1]
       lowerCamelCase		:     Tuple          =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       lowerCamelCase		:     int          =				np.array(
           [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]				)

       assert (
           np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2
       ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class 							A__		(     unittest.TestCase):
      def 						UpperCamelCase__							(				self				):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_text2img_cat_fp16.npy"""				)

       lowerCamelCase		:     List[Any]          =				KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""   ,  torch_dtype=torch.floataa				)
       pipe_prior.to(__magic_name__				)

       lowerCamelCase		:     List[str]          =				KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""   ,  torch_dtype=torch.floataa				)
       lowerCamelCase		:     str          =				pipeline.to(__magic_name__				)
       pipeline.set_progress_bar_config(disable=__magic_name__				)

       lowerCamelCase		:     Dict          =				"""red cat, 4k photo"""

       lowerCamelCase		:     Tuple          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
       lowerCamelCase      ,   lowerCamelCase		:     Any          =				pipe_prior(
           __magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=5   ,  negative_prompt=""""""   ,  ).to_tuple()

       lowerCamelCase		:     Dict          =				torch.Generator(device="""cuda"""				).manual_seed(0				)
       lowerCamelCase		:     Optional[Any]          =				pipeline(
           __magic_name__   ,  image_embeds=__magic_name__   ,  negative_image_embeds=__magic_name__   ,  generator=__magic_name__   ,  num_inference_steps=1_0_0   ,  output_type="""np"""   ,  )

       lowerCamelCase		:     str          =				output.images[0]

       assert image.shape == (5_1_2, 5_1_2, 3)

       assert_mean_pixel_difference(__magic_name__   ,  __magic_name__				)




