"""simple docstring"""





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 					UpperCAmelCase_     (   unittest.TestCase):


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
        lowercase__      :		List[str]          =     tempfile.mkdtemp()

        # fmt: off
        lowercase__      :		List[Any]          =     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
        # fmt: on
        lowercase__      :		Optional[Any]          =     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]							)							)

        lowercase__      :		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,
        }

        lowercase__      :		Dict          =     os.path.join(self.tmpdirname		,      __lowercase							)
        with open(self.image_processor_file		,      'w'		,      encoding='utf-8'							) as fp:
          json.dump(__lowercase		,      __lowercase							)


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Optional[int]:
        return BertTokenizer.from_pretrained(self.tmpdirname		,      **__lowercase							)


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Optional[Any]:
        return BertTokenizerFast.from_pretrained(self.tmpdirname		,      **__lowercase							)


      def 		_UpperCAmelCase	(       self		,      **a							)	-> Tuple:
        return FlavaImageProcessor.from_pretrained(self.tmpdirname		,      **__lowercase							)


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
        shutil.rmtree(self.tmpdirname							)


      def 		_UpperCAmelCase	(       self							)	-> Any:
        lowercase__      :		Dict          =     [np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

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

        return image_inputs


      def 		_UpperCAmelCase	(       self							)	-> Any:
        lowercase__      :		Optional[Any]          =     self.get_tokenizer()
        lowercase__      :		List[Any]          =     self.get_rust_tokenizer()
        lowercase__      :		Dict          =     self.get_image_processor()

        lowercase__      :		Tuple          =     FlavaProcessor(tokenizer=__lowercase		,      image_processor=__lowercase							)
        processor_slow.save_pretrained(self.tmpdirname							)
        lowercase__      :		Tuple          =     FlavaProcessor.from_pretrained(self.tmpdirname		,      use_fast=__lowercase							)

        lowercase__      :		List[Any]          =     FlavaProcessor(tokenizer=__lowercase		,      image_processor=__lowercase							)
        processor_fast.save_pretrained(self.tmpdirname							)
        lowercase__      :		str          =     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		,      __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 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
        lowercase__      :		Any          =     FlavaProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

        lowercase__      :		Optional[int]          =     self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)'							)
        lowercase__      :		Optional[Any]          =     self.get_image_processor(do_normalize=__lowercase		,      padding_value=1.0							)

        lowercase__      :		List[Any]          =     FlavaProcessor.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 		_UpperCAmelCase	(       self							)	-> Any:
        lowercase__      :		int          =     self.get_image_processor()
        lowercase__      :		Union[str, Any]          =     self.get_tokenizer()

        lowercase__      :		Optional[int]          =     FlavaProcessor(tokenizer=__lowercase		,      image_processor=__lowercase							)

        lowercase__      :		Any          =     self.prepare_image_inputs()

        lowercase__      :		str          =     image_processor(__lowercase		,      return_tensors='np'							)
        lowercase__      :		Optional[Any]          =     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							)

        # With rest of the args
        random.seed(1_2_3_4							)
        lowercase__      :		int          =     image_processor(
            __lowercase		,      return_image_mask=__lowercase		,      return_codebook_pixels=__lowercase		,      return_tensors='np'							)
        random.seed(1_2_3_4							)
        lowercase__      :		Union[str, Any]          =     processor(
            images=__lowercase		,      return_image_mask=__lowercase		,      return_codebook_pixels=__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 		_UpperCAmelCase	(       self							)	-> int:
        lowercase__      :		List[str]          =     self.get_image_processor()
        lowercase__      :		Optional[int]          =     self.get_tokenizer()

        lowercase__      :		Optional[int]          =     FlavaProcessor(tokenizer=__lowercase		,      image_processor=__lowercase							)

        lowercase__      :		Dict          =     'lower newer'

        lowercase__      :		Any          =     processor(text=__lowercase							)

        lowercase__      :		List[Any]          =     tokenizer(__lowercase							)

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


      def 		_UpperCAmelCase	(       self							)	-> int:
        lowercase__      :		int          =     self.get_image_processor()
        lowercase__      :		int          =     self.get_tokenizer()

        lowercase__      :		Optional[int]          =     FlavaProcessor(tokenizer=__lowercase		,      image_processor=__lowercase							)

        lowercase__      :		str          =     'lower newer'
        lowercase__      :		Dict          =     self.prepare_image_inputs()

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

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

        # add extra args
        lowercase__      :		List[str]          =     processor(text=__lowercase		,      images=__lowercase		,      return_codebook_pixels=__lowercase		,      return_image_mask=__lowercase							)

        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(__lowercase							):
          processor()


      def 		_UpperCAmelCase	(       self							)	-> str:
        lowercase__      :		int          =     self.get_image_processor()
        lowercase__      :		List[Any]          =     self.get_tokenizer()

        lowercase__      :		Tuple          =     FlavaProcessor(tokenizer=__lowercase		,      image_processor=__lowercase							)

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

        lowercase__      :		List[str]          =     processor.batch_decode(__lowercase							)
        lowercase__      :		List[str]          =     tokenizer.batch_decode(__lowercase							)

        self.assertListEqual(__lowercase		,      __lowercase							)


      def 		_UpperCAmelCase	(       self							)	-> Any:
        lowercase__      :		Optional[Any]          =     self.get_image_processor()
        lowercase__      :		str          =     self.get_tokenizer()

        lowercase__      :		Any          =     FlavaProcessor(tokenizer=__lowercase		,      image_processor=__lowercase							)

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

        lowercase__      :		Dict          =     processor(text=__lowercase		,      images=__lowercase							)

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


"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 					UpperCAmelCase_     (   _a			,							unittest.TestCase):
      lowerCamelCase__		:   Tuple			  =  KandinskyPriorPipeline
      lowerCamelCase__		:   Tuple			  =  ["prompt"]
      lowerCamelCase__		:   List[str]			  =  ["prompt", "negative_prompt"]
      lowerCamelCase__		:   List[Any]			  =  [
          "num_images_per_prompt",
          "generator",
          "num_inference_steps",
          "latents",
          "negative_prompt",
          "guidance_scale",
          "output_type",
          "return_dict",
      ]
      lowerCamelCase__		:   Optional[Any]			  =  False


      @property
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> str:
             return 3_2


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             return self.time_input_dim


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             return self.time_input_dim * 4


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             return 1_0_0


      @property
      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
             return tokenizer


      @property
      def 		_UpperCAmelCase	(       self							)	-> Any:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     CLIPTextConfig(
                 bos_token_id=0		,      eos_token_id=2		,      hidden_size=self.text_embedder_hidden_size		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=3_7		,      layer_norm_eps=1e-05		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_0_0_0		,      )
             return CLIPTextModelWithProjection(a							)


      @property
      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             torch.manual_seed(0							)

             lowercase__      :		int          =     {
                 'num_attention_heads': 2,
                 'attention_head_dim': 1_2,
                 'embedding_dim': self.text_embedder_hidden_size,
                 'num_layers': 1,
             }

             lowercase__      :		Tuple          =     PriorTransformer(**a							)
             # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
             lowercase__      :		int          =     nn.Parameter(torch.ones(model.clip_std.shape							)							)
             return model


      @property
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             torch.manual_seed(0							)
             lowercase__      :		Optional[int]          =     CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size		,      image_size=2_2_4		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=3_7		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1_4		,      )

             lowercase__      :		Optional[Any]          =     CLIPVisionModelWithProjection(a							)
             return model


      @property
      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		Dict          =     CLIPImageProcessor(
                 crop_size=2_2_4		,      do_center_crop=a		,      do_normalize=a		,      do_resize=a		,      image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]		,      image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]		,      resample=3		,      size=2_2_4		,      )

             return image_processor


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		List[str]          =     self.dummy_prior
             lowercase__      :		Tuple          =     self.dummy_image_encoder
             lowercase__      :		Optional[Any]          =     self.dummy_text_encoder
             lowercase__      :		List[str]          =     self.dummy_tokenizer
             lowercase__      :		Optional[int]          =     self.dummy_image_processor

             lowercase__      :		List[Any]          =     UnCLIPScheduler(
                 variance_type='fixed_small_log'		,      prediction_type='sample'		,      num_train_timesteps=1_0_0_0		,      clip_sample=a		,      clip_sample_range=10.0		,      )

             lowercase__      :		List[str]          =     {
                 'prior': prior,
                 'image_encoder': image_encoder,
                 'text_encoder': text_encoder,
                 'tokenizer': tokenizer,
                 'scheduler': scheduler,
                 'image_processor': image_processor,
             }

             return components


      def 		_UpperCAmelCase	(       self		,      a		,      a=0							)	-> Union[str, Any]:
             if str(a							).startswith('mps'							):
                    lowercase__      :		Optional[Any]          =     torch.manual_seed(a							)
             else:
                    lowercase__      :		Any          =     torch.Generator(device=a							).manual_seed(a							)
             lowercase__      :		str          =     {
                 'prompt': 'horse',
                 'generator': generator,
                 'guidance_scale': 4.0,
                 'num_inference_steps': 2,
                 'output_type': 'np',
             }
             return inputs


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Optional[int]          =     'cpu'

             lowercase__      :		Optional[Any]          =     self.get_dummy_components()

             lowercase__      :		Optional[Any]          =     self.pipeline_class(**a							)
             lowercase__      :		Optional[int]          =     pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__      :		Optional[int]          =     pipe(**self.get_dummy_inputs(a							)							)
             lowercase__      :		Optional[Any]          =     output.image_embeds

             lowercase__      :		Dict          =     pipe(
                 **self.get_dummy_inputs(a							)		,      return_dict=a		,      )[0]

             lowercase__      :		List[str]          =     image[0, -1_0:]
             lowercase__      :		Optional[Any]          =     image_from_tuple[0, -1_0:]

             assert image.shape == (1, 3_2)

             lowercase__      :		Union[str, Any]          =     np.array(
                 [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]							)

             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2


      @skip_mps
      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Union[str, Any]          =     True
             lowercase__      :		int          =     False

             self._test_inference_batch_single_identical(
                 test_max_difference=a		,      relax_max_difference=a		,      test_mean_pixel_difference=a		,      )


      @skip_mps
      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Tuple          =     torch_device == 'cpu'
             lowercase__      :		Any          =     False

             self._test_attention_slicing_forward_pass(
                 test_max_difference=a		,      test_mean_pixel_difference=a		,      )

