





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch


def       _A  (      lowerCamelCase   ):  # picklable for multiprocessing
   return x.sum()


def       _A  (      lowerCamelCase   ):  # picklable for multiprocessing
   return i + 1







@dataclass
class   __lowerCAmelCase			:
   _UpperCamelCase :						int
   _UpperCamelCase :						str







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Tuple      		=  {}
      a__	:						Any      		=  []
      a__	:						Optional[int]      		=  1
      a__	:						Any      		=  [1, 2]
      a__	:						str      		=  {"a": 1, "b": 2}
      a__	:						List[Any]      		=  {"a": [1, 2], "b": [3, 4]}
      a__	:						List[str]      		=  {"a": {"1": 1}, "b": 2}
      a__	:						Union[str, Any]      		=  {"a": 1, "b": 2, "c": 3, "d": 4}
      a__	:						Tuple      		=  {}
      a__	:						str      		=  []
      a__	:						str      		=  2
      a__	:						List[str]      		=  [2, 3]
      a__	:						Union[str, Any]      		=  {"a": 2, "b": 3}
      a__	:						Tuple      		=  {"a": [2, 3], "b": [4, 5]}
      a__	:						Optional[int]      		=  {"a": {"1": 2}, "b": 3}
      a__	:						Dict      		=  {"a": 2, "b": 3, "c": 4, "d": 5}
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case      )					,			snake_case      )

      a__	:						Dict      		=  2
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(map_nested(snake_case					,			snake_case					,			num_proc=snake_case      )					,			snake_case      )

      a__	:						Any      		=  {"a": np.eye(2      ), "b": np.zeros(3      ), "c": np.ones(2      )}
      a__	:						Any      		=  {"a": 2, "b": 0, "c": 2}
      a__	:						Optional[int]      		=  {
          "a": np.eye(2      ).astype(snake_case      ),
          "b": np.zeros(3      ).astype(snake_case      ),
          "c": np.ones(2      ).astype(snake_case      ),
      }
      self.assertEqual(map_nested(snake_case					,			snake_case					,			map_numpy=snake_case      )					,			snake_case      )
      self.assertEqual(
          {k: v.tolist() for k, v in map_nested(snake_case					,			snake_case					,			map_numpy=snake_case      ).items()}					,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,			)
      self.assertEqual(map_nested(snake_case					,			snake_case					,			map_numpy=snake_case					,			num_proc=snake_case      )					,			snake_case      )
      self.assertEqual(
          {k: v.tolist() for k, v in map_nested(snake_case					,			snake_case					,			map_numpy=snake_case					,			num_proc=snake_case      ).items()}					,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,			)
      with self.assertRaises(snake_case      ):  # can't pickle a local lambda
         map_nested(lambda snake_case      : x + 1					,			snake_case					,			num_proc=snake_case      )





   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						str      		=  {"a": 1, "b": 2}
      a__	:						Optional[Any]      		=  {"a": 3, "b": 4}
      a__	:						Dict      		=  {"a": 5, "b": 6}
      a__	:						int      		=  sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]      )
      self.assertEqual(sorted(zip_dict(snake_case					,			snake_case					,			snake_case      )      )					,			snake_case      )





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      class   __lowerCAmelCase			:
         _UpperCamelCase :						Union[str, Any]     		=				"""bar"""







      a__	:						int      		=  Foo()
      self.assertEqual(foo.my_attr					,			"bar"      )
      with temporary_assignment(snake_case					,			"my_attr"					,			"BAR"      ):
         self.assertEqual(foo.my_attr					,			"BAR"      )
      self.assertEqual(foo.my_attr					,			"bar"      )







@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"							, [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]							, )
def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   with patch("datasets.utils.py_utils._single_map_nested"   ) as mock_single_map_nested, patch(
       "datasets.parallel.parallel.Pool"   ) as mock_multiprocessing_pool:
      a__	:						Union[str, Any]      		=  {F"""{i}""": i for i in range(lowerCamelCase   )}
      a__	:						Any      		=  map_nested(lambda lowerCamelCase   : x + 10							, lowerCamelCase							, num_proc=lowerCamelCase							, parallel_min_length=16   )
      if expected_num_proc == 1:
         assert mock_single_map_nested.called
         assert not mock_multiprocessing_pool.called
      else:
         assert not mock_single_map_nested.called
         assert mock_multiprocessing_pool.called
         assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   @require_tf
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      import tensorflow as tf
      from tensorflow.keras import layers

      a__	:						Any      		=  layers.Dense(2      )

      def gen_random_output():
         a__	:						List[Any]      		=  tf.random.uniform((1, 3)      )
         return model(snake_case      ).numpy()

      with temp_seed(42					,			set_tensorflow=snake_case      ):
         a__	:						List[Any]      		=  gen_random_output()
      with temp_seed(42					,			set_tensorflow=snake_case      ):
         a__	:						List[Any]      		=  gen_random_output()
      a__	:						Optional[Any]      		=  gen_random_output()

      np.testing.assert_equal(snake_case					,			snake_case      )
      self.assertGreater(np.abs(outa - outa      ).sum()					,			0      )





   @require_torch
   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      import torch

      def gen_random_output():
         a__	:						int      		=  torch.nn.Linear(3					,			2      )
         a__	:						Optional[Any]      		=  torch.rand(1					,			3      )
         return model(snake_case      ).detach().numpy()

      with temp_seed(42					,			set_pytorch=snake_case      ):
         a__	:						Tuple      		=  gen_random_output()
      with temp_seed(42					,			set_pytorch=snake_case      ):
         a__	:						Dict      		=  gen_random_output()
      a__	:						Optional[Any]      		=  gen_random_output()

      np.testing.assert_equal(snake_case					,			snake_case      )
      self.assertGreater(np.abs(outa - outa      ).sum()					,			0      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      def gen_random_output():
         return np.random.rand(1					,			3      )

      with temp_seed(42      ):
         a__	:						Union[str, Any]      		=  gen_random_output()
      with temp_seed(42      ):
         a__	:						Optional[int]      		=  gen_random_output()
      a__	:						List[Any]      		=  gen_random_output()

      np.testing.assert_equal(snake_case					,			snake_case      )
      self.assertGreater(np.abs(outa - outa      ).sum()					,			0      )







@pytest.mark.parametrize("input_data"							, [{}]   )
def       _A  (      lowerCamelCase   ):
   a__	:						int      		=  NestedDataStructure(lowerCamelCase   ).data
   assert output_data == input_data


@pytest.mark.parametrize(
    "data, expected_output"							, [
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]							, )
def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  NestedDataStructure(lowerCamelCase   ).flatten()
   assert output == expected_output


def       _A  (      ):
   a__	:						List[str]      		=  A(x=1							, y="foobar"   )
   a__	:						Dict      		=  {"x": 1, "y": "foobar"}
   assert asdict(lowerCamelCase   ) == expected_output

   a__	:						int      		=  {"a": {"b": A(x=10							, y="foo"   )}, "c": [A(x=20							, y="bar"   )]}
   a__	:						int      		=  {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
   assert asdict(lowerCamelCase   ) == expected_output

   with pytest.raises(lowerCamelCase   ):
      asdict([1, A(x=10							, y="foo"   )]   )


def       _A  (      lowerCamelCase   ):
   return text.split()


def       _A  (      lowerCamelCase   ):
   yield (time.time(), content)
   time.sleep(2   )
   yield (time.time(), content)


def       _A  (      ):
   with Pool(2   ) as pool:
      a__	:						str      		=  list(iflatmap_unordered(lowerCamelCase							, _split_text							, kwargs_iterable=[{"text": "hello there"}] * 10   )   )
      assert out.count("hello"   ) == 10
      assert out.count("there"   ) == 10
      assert len(lowerCamelCase   ) == 20

   # check multiprocess from pathos (uses dill for pickling)
   with multiprocess.Pool(2   ) as pool:
      a__	:						Tuple      		=  list(iflatmap_unordered(lowerCamelCase							, _split_text							, kwargs_iterable=[{"text": "hello there"}] * 10   )   )
      assert out.count("hello"   ) == 10
      assert out.count("there"   ) == 10
      assert len(lowerCamelCase   ) == 20

   # check that we get items as fast as possible
   with Pool(2   ) as pool:
      a__	:						Tuple      		=  []
      for yield_time, content in iflatmap_unordered(
          lowerCamelCase							, _aseconds_generator_of_aitems_with_timing							, kwargs_iterable=[{"content": "a"}, {"content": "b"}]   ):
         assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
         out.append(lowerCamelCase   )
      assert out.count("a"   ) == 2
      assert out.count("b"   ) == 2
      assert len(lowerCamelCase   ) == 4








import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()







class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase		,_UpperCamelCase		,unittest.TestCase  ):
   _UpperCamelCase :						Optional[int]     		=				StableUnCLIPPipeline
   _UpperCamelCase :						Optional[int]     		=				TEXT_TO_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase :						List[Any]     		=				TEXT_TO_IMAGE_IMAGE_PARAMS
   _UpperCamelCase :						str     		=				TEXT_TO_IMAGE_IMAGE_PARAMS

   # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
   _UpperCamelCase :						Any     		=				False





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Any      		=  32
      a__	:						int      		=  embedder_hidden_size

      # prior components

      torch.manual_seed(0      )
      a__	:						Any      		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=snake_case					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						int      		=  PriorTransformer(
          num_attention_heads=2					,			attention_head_dim=12					,			embedding_dim=snake_case					,			num_layers=1					,			)

      torch.manual_seed(0      )
      a__	:						str      		=  DDPMScheduler(
          variance_type="fixed_small_log"					,			prediction_type="sample"					,			num_train_timesteps=1_000					,			clip_sample=snake_case					,			clip_sample_range=5.0					,			beta_schedule="squaredcos_cap_v2"					,			)

      # regular denoising components

      torch.manual_seed(0      )
      a__	:						Any      		=  StableUnCLIPImageNormalizer(embedding_dim=snake_case      )
      a__	:						int      		=  DDPMScheduler(beta_schedule="squaredcos_cap_v2"      )

      torch.manual_seed(0      )
      a__	:						Optional[Any]      		=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

      torch.manual_seed(0      )
      a__	:						Union[str, Any]      		=  CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0					,			eos_token_id=2					,			hidden_size=snake_case					,			projection_dim=32					,			intermediate_size=37					,			layer_norm_eps=1E-05					,			num_attention_heads=4					,			num_hidden_layers=5					,			pad_token_id=1					,			vocab_size=1_000					,			)      )

      torch.manual_seed(0      )
      a__	:						Any      		=  UNetaDConditionModel(
          sample_size=32					,			in_channels=4					,			out_channels=4					,			down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")					,			up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")					,			block_out_channels=(32, 64)					,			attention_head_dim=(2, 4)					,			class_embed_type="projection"					,			projection_class_embeddings_input_dim=embedder_projection_dim * 2					,			cross_attention_dim=snake_case					,			layers_per_block=1					,			upcast_attention=snake_case					,			use_linear_projection=snake_case					,			)

      torch.manual_seed(0      )
      a__	:						Tuple      		=  DDIMScheduler(
          beta_schedule="scaled_linear"					,			beta_start=0.00_085					,			beta_end=0.012					,			prediction_type="v_prediction"					,			set_alpha_to_one=snake_case					,			steps_offset=1					,			)

      torch.manual_seed(0      )
      a__	:						Optional[int]      		=  AutoencoderKL()

      a__	:						Any      		=  {
          # prior components
          "prior_tokenizer": prior_tokenizer,
          "prior_text_encoder": prior_text_encoder,
          "prior": prior,
          "prior_scheduler": prior_scheduler,
          # image noising components
          "image_normalizer": image_normalizer,
          "image_noising_scheduler": image_noising_scheduler,
          # regular denoising components
          "tokenizer": tokenizer,
          "text_encoder": text_encoder,
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
      }

      return components





   def 	_snake_case ( self					,			snake_case					,			snake_case=0      )				->  Dict:
      """simple docstring"""





      if str(snake_case      ).startswith("mps"      ):
         a__	:						Union[str, Any]      		=  torch.manual_seed(snake_case      )
      else:
         a__	:						List[str]      		=  torch.Generator(device=snake_case      ).manual_seed(snake_case      )
      a__	:						Any      		=  {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "prior_num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Dict      		=  torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=snake_case      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  torch_device in ["cpu", "mps"]

      self._test_inference_batch_single_identical(test_max_difference=snake_case      )







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





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						int      		=  load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"      )

      a__	:						int      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a__	:						Dict      		=  torch.Generator(device="cpu"      ).manual_seed(0      )
      a__	:						Dict      		=  pipe("anime turle"					,			generator=snake_case					,			output_type="np"      )

      a__	:						List[str]      		=  output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      a__	:						str      		=  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,			torch_dtype=torch.floataa      )
      a__	:						Union[str, Any]      		=  pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a__	:						Union[str, Any]      		=  pipe(
          "anime turtle"					,			prior_num_inference_steps=2					,			num_inference_steps=2					,			output_type="np"					,			)

      a__	:						List[Any]      		=  torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9

