
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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      __A(    snake_case_    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      KandinskyVaaControlnetPipeline
  SCREAMING_SNAKE_CASE__         =      ["""image_embeds""", """negative_image_embeds""", """hint"""]
  SCREAMING_SNAKE_CASE__         =      ["""image_embeds""", """negative_image_embeds""", """hint"""]
  SCREAMING_SNAKE_CASE__         =      [
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  SCREAMING_SNAKE_CASE__         =      False







  @property
  def 	UpperCAmelCase_ (self						):
       return 32







  @property
  def 	UpperCAmelCase_ (self						):
       return 32







  @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_00







  @property
  def 	UpperCAmelCase_ (self						):
       torch.manual_seed(0						)

       UpperCamelCase__				=    {
           'in_channels': 8,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'image_hint',
           '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': '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(**SCREAMING_SNAKE_CASE_						)
       return model







  @property
  def 	UpperCAmelCase_ (self						):
       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "AttnDownEncoderBlock2D",
           ],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }







  @property
  def 	UpperCAmelCase_ (self						):
       torch.manual_seed(0						)
       UpperCamelCase__				=    VQModel(**self.dummy_movq_kwargs						)
       return model







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.dummy_unet
       UpperCamelCase__				=    self.dummy_movq

       UpperCamelCase__				=    DDIMScheduler(
           num_train_timesteps=10_00	,      beta_schedule="""linear"""	,      beta_start=0.0_0085	,      beta_end=0.012	,      clip_sample=SCREAMING_SNAKE_CASE_	,      set_alpha_to_one=SCREAMING_SNAKE_CASE_	,      steps_offset=1	,      prediction_type="""epsilon"""	,      thresholding=SCREAMING_SNAKE_CASE_	,      )

       UpperCamelCase__				=    {
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }
       return components







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=0						):
       UpperCamelCase__				=    floats_tensor((1, self.text_embedder_hidden_size)	,      rng=random.Random(SCREAMING_SNAKE_CASE_						)						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    floats_tensor((1, self.text_embedder_hidden_size)	,      rng=random.Random(seed + 1						)						).to(
           SCREAMING_SNAKE_CASE_						)

       # create hint
       UpperCamelCase__				=    floats_tensor((1, 3, 64, 64)	,      rng=random.Random(SCREAMING_SNAKE_CASE_						)						).to(SCREAMING_SNAKE_CASE_						)

       if str(SCREAMING_SNAKE_CASE_						).startswith("""mps"""						):
            UpperCamelCase__				=    torch.manual_seed(SCREAMING_SNAKE_CASE_						)
       else:
            UpperCamelCase__				=    torch.Generator(device=SCREAMING_SNAKE_CASE_						).manual_seed(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'hint': hint,
           'generator': generator,
           'height': 64,
           'width': 64,
           'guidance_scale': 4.0,
           'num_inference_steps': 2,
           'output_type': 'np',
       }
       return inputs







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    'cpu'

       UpperCamelCase__				=    self.get_dummy_components()

       UpperCamelCase__				=    self.pipeline_class(**SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    pipe.to(SCREAMING_SNAKE_CASE_						)

       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_						)						)
       UpperCamelCase__				=    output.images

       UpperCamelCase__				=    pipe(
           **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_						)	,      return_dict=SCREAMING_SNAKE_CASE_	,      )[0]

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

       assert image.shape == (1, 64, 64, 3)

       UpperCamelCase__				=    np.array(
           [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]						)

       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			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""						)

       UpperCamelCase__				=    load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinskyv22/hint_image_cat.png"""						)
       UpperCamelCase__				=    torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_						)						).float() / 255.0
       UpperCamelCase__				=    hint.permute(2	,      0	,      1						).unsqueeze(0						)

       UpperCamelCase__				=    KandinskyVaaPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-2-prior"""	,      torch_dtype=torch.floataa						)
       pipe_prior.to(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    KandinskyVaaControlnetPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-2-controlnet-depth"""	,      torch_dtype=torch.floataa						)
       UpperCamelCase__				=    pipeline.to(SCREAMING_SNAKE_CASE_						)
       pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    'A robot, 4k photo'

       UpperCamelCase__				=    torch.Generator(device="""cuda"""						).manual_seed(0						)
       UpperCamelCase__				=    pipe_prior(
           SCREAMING_SNAKE_CASE_	,      generator=SCREAMING_SNAKE_CASE_	,      num_inference_steps=5	,      negative_prompt=""""""	,      ).to_tuple()

       UpperCamelCase__				=    torch.Generator(device="""cuda"""						).manual_seed(0						)
       UpperCamelCase__				=    pipeline(
           image_embeds=SCREAMING_SNAKE_CASE_	,      negative_image_embeds=SCREAMING_SNAKE_CASE_	,      hint=SCREAMING_SNAKE_CASE_	,      generator=SCREAMING_SNAKE_CASE_	,      num_inference_steps=1_00	,      output_type="""np"""	,      )

       UpperCamelCase__				=    output.images[0]

       assert image.shape == (5_12, 5_12, 3)

       assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM




@require_torch
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.decode(greedy_ids[0]						)

       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()
       UpperCamelCase__				=    """"""
       for new_text in streamer:
            streamer_text += new_text

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    greedy_ids[:, input_ids.shape[1] :]
       UpperCamelCase__				=    tokenizer.decode(new_greedy_ids[0]						)

       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_prompt=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=10	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)
       # The greedy text should be printed to stdout, except for the final "\n" in the streamer
       UpperCamelCase__				=    cs.out[:-1]

       self.assertEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
       # with actual models -- the dummy models' tokenizers are not aligned with their models, and
       # `skip_special_tokens=True` has no effect on them
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""distilgpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""distilgpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    torch.ones((1, 5)	,      device=SCREAMING_SNAKE_CASE_						).long() * model.config.bos_token_id
       with CaptureStdout() as cs:
            UpperCamelCase__				=    TextStreamer(SCREAMING_SNAKE_CASE_	,      skip_special_tokens=SCREAMING_SNAKE_CASE_						)
            model.generate(SCREAMING_SNAKE_CASE_	,      max_new_tokens=1	,      do_sample=SCREAMING_SNAKE_CASE_	,      streamer=SCREAMING_SNAKE_CASE_						)

       # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
       # re-tokenized, must only contain one token
       UpperCamelCase__				=    cs.out[:-1]  # Remove the final "\n"
       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       self.assertEqual(streamer_text_tokenized.input_ids.shape	,      (1, 1)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						)
       UpperCamelCase__				=    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    -1

       UpperCamelCase__				=    ids_tensor((1, 5)	,      vocab_size=model.config.vocab_size						).to(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    TextIteratorStreamer(SCREAMING_SNAKE_CASE_	,      timeout=0.001						)
       UpperCamelCase__				=    {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
       UpperCamelCase__				=    Thread(target=model.generate	,      kwargs=SCREAMING_SNAKE_CASE_						)
       thread.start()

       # The streamer will timeout after 0.001 seconds, so an exception will be raised
       with self.assertRaises(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    """"""
            for new_text in streamer:
                 streamer_text += new_text





