
"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						KandinskyInpaintPipeline
      __SCREAMING_SNAKE_CASE :				List[str]				   =						["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
      __SCREAMING_SNAKE_CASE :				List[str]				   =						[
          "prompt",
          "negative_prompt",
          "image_embeds",
          "negative_image_embeds",
          "image",
          "mask_image",
      ]
      __SCREAMING_SNAKE_CASE :				List[str]				   =						[
          "generator",
          "height",
          "width",
          "latents",
          "guidance_scale",
          "negative_prompt",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						False




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




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




      @property
      def      a_   (				self				)    ->       str:
          return self.time_input_dim




      @property
      def      a_   (				self				)    ->       int:
          return self.time_input_dim * 4




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




      @property
      def      a_   (				self				)    ->       str:
          UpperCAmelCase          =       XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'				)
          return tokenizer




      @property
      def      a_   (				self				)    ->       str:
          torch.manual_seed(0				)
          UpperCAmelCase          =       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 ,							)

          UpperCAmelCase          =       MultilingualCLIP(lowercase_				)
          UpperCAmelCase          =       text_encoder.eval()

          return text_encoder




      @property
      def      a_   (				self				)    ->       str:
          torch.manual_seed(0				)

          UpperCAmelCase          =       {
              'in_channels': 9,
              # 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,
          }

          UpperCAmelCase          =       UNetaDConditionModel(**lowercase_				)
          return model




      @property
      def      a_   (				self				)    ->       Union[str, Any]:
          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      a_   (				self				)    ->       List[Any]:
          torch.manual_seed(0				)
          UpperCAmelCase          =       VQModel(**self.dummy_movq_kwargs				)
          return model




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.dummy_text_encoder
          UpperCAmelCase          =       self.dummy_tokenizer
          UpperCAmelCase          =       self.dummy_unet
          UpperCAmelCase          =       self.dummy_movq

          UpperCAmelCase          =       DDIMScheduler(
              num_train_timesteps=1_0_0_0 ,							beta_schedule='linear' ,							beta_start=0.0_0_0_8_5 ,							beta_end=0.0_1_2 ,							clip_sample=lowercase_ ,							set_alpha_to_one=lowercase_ ,							steps_offset=1 ,							prediction_type='epsilon' ,							thresholding=lowercase_ ,							)

          UpperCAmelCase          =       {
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components




      def      a_   (				self ,							lowercase_ ,							lowercase_=0				)    ->       int:
          UpperCAmelCase          =       floats_tensor((1, self.cross_attention_dim) ,							rng=random.Random(lowercase_				)				).to(lowercase_				)
          UpperCAmelCase          =       floats_tensor((1, self.cross_attention_dim) ,							rng=random.Random(seed + 1				)				).to(lowercase_				)
          # create init_image
          UpperCAmelCase          =       floats_tensor((1, 3, 6_4, 6_4) ,							rng=random.Random(lowercase_				)				).to(lowercase_				)
          UpperCAmelCase          =       image.cpu().permute(0 ,							2 ,							3 ,							1				)[0]
          UpperCAmelCase          =       Image.fromarray(np.uinta(lowercase_				)				).convert('RGB'				).resize((2_5_6, 2_5_6)				)
          # create mask
          UpperCAmelCase          =       np.ones((6_4, 6_4) ,							dtype=np.floataa				)
          UpperCAmelCase          =       0

          if str(lowercase_				).startswith('mps'				):
              UpperCAmelCase          =       torch.manual_seed(lowercase_				)
          else:
              UpperCAmelCase          =       torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
          UpperCAmelCase          =       {
              'prompt': 'horse',
              'image': init_image,
              'mask_image': mask,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 6_4,
              'width': 6_4,
              'num_inference_steps': 2,
              'guidance_scale': 4.0,
              'output_type': 'np',
          }
          return inputs




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       'cpu'

          UpperCAmelCase          =       self.get_dummy_components()

          UpperCAmelCase          =       self.pipeline_class(**lowercase_				)
          UpperCAmelCase          =       pipe.to(lowercase_				)

          pipe.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       pipe(**self.get_dummy_inputs(lowercase_				)				)
          UpperCAmelCase          =       output.images

          UpperCAmelCase          =       pipe(
              **self.get_dummy_inputs(lowercase_				) ,							return_dict=lowercase_ ,							)[0]

          UpperCAmelCase          =       image[0, -3:, -3:, -1]
          UpperCAmelCase          =       image_from_tuple[0, -3:, -3:, -1]

          print(F"image.shape {image.shape}"				)

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

          UpperCAmelCase          =       np.array(
              [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]				)

          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()}"




      def      a_   (				self				)    ->       Union[str, Any]:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3				)

@slow
@require_torch_gpu
class   _UpperCAmelCase				(		unittest.TestCase				):




      def      a_   (				self				)    ->       int:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'				)

          UpperCAmelCase          =       load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
          UpperCAmelCase          =       np.ones((7_6_8, 7_6_8) ,							dtype=np.floataa				)
          UpperCAmelCase          =       0

          UpperCAmelCase          =       'a hat'

          UpperCAmelCase          =       KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior' ,							torch_dtype=torch.floataa				)
          pipe_prior.to(lowercase_				)

          UpperCAmelCase          =       KandinskyInpaintPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-inpaint' ,							torch_dtype=torch.floataa				)
          UpperCAmelCase          =       pipeline.to(lowercase_				)
          pipeline.set_progress_bar_config(disable=lowercase_				)

          UpperCAmelCase          =       torch.Generator(device='cpu'				).manual_seed(0				)
          UpperCAmelCase      ,				UpperCAmelCase          =       pipe_prior(
              lowercase_ ,							generator=lowercase_ ,							num_inference_steps=5 ,							negative_prompt='' ,							).to_tuple()

          UpperCAmelCase          =       pipeline(
              lowercase_ ,							image=lowercase_ ,							mask_image=lowercase_ ,							image_embeds=lowercase_ ,							negative_image_embeds=lowercase_ ,							generator=lowercase_ ,							num_inference_steps=1_0_0 ,							height=7_6_8 ,							width=7_6_8 ,							output_type='np' ,							)

          UpperCAmelCase          =       output.images[0]

          assert image.shape == (7_6_8, 7_6_8, 3)

          assert_mean_pixel_difference(lowercase_ ,							lowercase_				)





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
    '''tiiuae/falcon-7b''': '''https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json''',
}
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				Optional[Any]				   =						"falcon"
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["past_key_values"]




      def __init__(				self ,							lowercase_=6_5_0_2_4 ,							lowercase_=4_5_4_4 ,							lowercase_=3_2 ,							lowercase_=7_1 ,							lowercase_=1E-5 ,							lowercase_=0.0_2 ,							lowercase_=True ,							lowercase_=0.0 ,							lowercase_=0.0 ,							lowercase_=None ,							lowercase_=False ,							lowercase_=False ,							lowercase_=True ,							lowercase_=True ,							lowercase_=False ,							lowercase_=1_1 ,							lowercase_=1_1 ,							**lowercase_ ,							)    ->       Any:
          UpperCAmelCase          =       vocab_size
          # Backward compatibility with n_embed kwarg
          UpperCAmelCase          =       kwargs.pop('n_embed' ,							lowercase_				)
          UpperCAmelCase          =       hidden_size if n_embed is None else n_embed
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       layer_norm_epsilon
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       use_cache
          UpperCAmelCase          =       hidden_dropout
          UpperCAmelCase          =       attention_dropout

          UpperCAmelCase          =       bos_token_id
          UpperCAmelCase          =       eos_token_id
          UpperCAmelCase          =       num_attention_heads if num_kv_heads is None else num_kv_heads
          UpperCAmelCase          =       alibi
          UpperCAmelCase          =       new_decoder_architecture
          UpperCAmelCase          =       multi_query  # Ignored when new_decoder_architecture is True
          UpperCAmelCase          =       parallel_attn
          UpperCAmelCase          =       bias

          super().__init__(bos_token_id=lowercase_ ,							eos_token_id=lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       List[Any]:
          return self.hidden_size // self.num_attention_heads




      @property
      def      a_   (				self				)    ->       List[str]:
          return not self.alibi



