
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   UpperCAmelCase		(						A_       ,unittest.TestCase ):
   A__					:		Optional[int]						     =     DiTPipeline
   A__					:		Any						     =     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
   A__					:		Optional[Any]						     =     PipelineTesterMixin.required_optional_params - {
       "latents",
       "num_images_per_prompt",
       "callback",
       "callback_steps",
   }
   A__					:		List[Any]						     =     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
   A__					:		Union[str, Any]						     =     False



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[int]:


          '''simple docstring'''


          torch.manual_seed(0 )
          snake_case       :   Optional[int]							=      TransformeraDModel(
              sample_size=16    ,						num_layers=2    ,						patch_size=4    ,						attention_head_dim=8    ,						num_attention_heads=2    ,						in_channels=4    ,						out_channels=8    ,						attention_bias=snake_case__    ,						activation_fn="gelu-approximate"    ,						num_embeds_ada_norm=10_00    ,						norm_type="ada_norm_zero"    ,						norm_elementwise_affine=snake_case__    ,						)
          snake_case       :   List[Any]							=      AutoencoderKL()
          snake_case       :   Dict							=      DDIMScheduler()
          snake_case       :   Optional[Any]							=      {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
          return components



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					Dict    ,						snake_case__		:					Dict=0 )		->	int:


          '''simple docstring'''


          if str(snake_case__ ).startswith("mps" ):
                 snake_case       :   str							=      torch.manual_seed(snake_case__ )
          else:
                 snake_case       :   Tuple							=      torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
          snake_case       :   int							=      {
              "class_labels": [1],
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }
          return inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      "cpu"

          snake_case       :   str							=      self.get_dummy_components()
          snake_case       :   Any							=      self.pipeline_class(**snake_case__ )
          pipe.to(snake_case__ )
          pipe.set_progress_bar_config(disable=snake_case__ )

          snake_case       :   Union[str, Any]							=      self.get_dummy_inputs(snake_case__ )
          snake_case       :   Optional[int]							=      pipe(**snake_case__ ).images
          snake_case       :   List[str]							=      image[0, -3:, -3:, -1]

          self.assertEqual(image.shape    ,						(1, 16, 16, 3) )
          snake_case       :   Union[str, Any]							=      np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
          snake_case       :   Tuple							=      np.abs(image_slice.flatten() - expected_slice ).max()
          self.assertLessEqual(snake_case__    ,						1e-3 )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	int:


          '''simple docstring'''


          self._test_inference_batch_single_identical(relax_max_difference=snake_case__    ,						expected_max_diff=1e-3 )



   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()    ,						reason="XFormers attention is only available with CUDA and `xformers` installed"    ,						)
   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )






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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	str:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Dict:


          '''simple docstring'''


          snake_case       :   str							=      torch.manual_seed(0 )

          snake_case       :   Dict							=      DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
          pipe.to("cuda" )

          snake_case       :   List[Any]							=      ["vase", "umbrella", "white shark", "white wolf"]
          snake_case       :   Dict							=      pipe.get_label_ids(snake_case__ )

          snake_case       :   Optional[Any]							=      pipe(snake_case__    ,						generator=snake_case__    ,						num_inference_steps=40    ,						output_type="np" ).images

          for word, image in zip(snake_case__    ,						snake_case__ ):
                 snake_case       :   Union[str, Any]							=      load_numpy(
                     f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
                 assert np.abs((expected_image - image).max() ) < 1e-2



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Dict							=      DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
          snake_case       :   List[str]							=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
          pipe.to("cuda" )

          snake_case       :   Optional[Any]							=      ["vase", "umbrella"]
          snake_case       :   Optional[Any]							=      pipe.get_label_ids(snake_case__ )

          snake_case       :   List[str]							=      torch.manual_seed(0 )
          snake_case       :   int							=      pipe(snake_case__    ,						generator=snake_case__    ,						num_inference_steps=25    ,						output_type="np" ).images

          for word, image in zip(snake_case__    ,						snake_case__ ):
                 snake_case       :   Any							=      load_numpy(
                     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                     f"""/dit/{word}_512.npy""" )

                 assert np.abs((expected_image - image).max() ) < 1e-1







"""simple docstring"""



import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


UpperCAmelCase__          = 'http://www.mocksite.com/file1.txt'
UpperCAmelCase__          = '"text": ["foo", "foo"]'
UpperCAmelCase__          = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'

class       lowerCAmelCase__				:
     __a						=					200
     __a						=					{"""Content-Length""": """100"""}
     __a						=					{}







     def      lowercase	(							self			:			List[str]    ,      **_lowerCamelCase			:			List[str]			):
       return [bytes(_lowerCamelCase    ,      '''utf-8'''			)]






def 					_UpperCAmelCase      ( *__lowerCamelCase   :       List[str] ,					**__lowerCamelCase   :       Dict		)     ->					Dict:
  return MockResponse()
@pytest.mark.parametrize('''urls_type''' ,					[str, list, dict]		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  import requests

  monkeypatch.setattr(__lowerCamelCase ,					'''request''' ,					__lowerCamelCase		)

  _snake_case					     =       URL
  if issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       url
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       [url]
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       {'''train''': url}
  _snake_case					     =       '''dummy'''
  _snake_case					     =       '''downloads'''
  _snake_case					     =       tmp_path
  _snake_case					     =       DownloadConfig(
      cache_dir=os.path.join(__lowerCamelCase ,					__lowerCamelCase		) ,					use_etag=__lowerCamelCase ,					)
  _snake_case					     =       DownloadManager(dataset_name=__lowerCamelCase ,					download_config=__lowerCamelCase		)
  _snake_case					     =       dl_manager.download(__lowerCamelCase		)
  _snake_case					     =       urls
  for downloaded_paths in [downloaded_paths]:
    if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       [downloaded_paths]
      _snake_case					     =       [urls]
    elif isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      assert "train" in downloaded_paths.keys()
      _snake_case					     =       downloaded_paths.values()
      _snake_case					     =       urls.values()
    assert downloaded_paths
    for downloaded_path, input_url in zip(__lowerCamelCase ,					__lowerCamelCase		):
      assert downloaded_path == dl_manager.downloaded_paths[input_url]
      _snake_case					     =       Path(__lowerCamelCase		)
      _snake_case					     =       downloaded_path.parts
      assert parts[-1] == HASH
      assert parts[-2] == cache_subdir
      assert downloaded_path.exists()
      _snake_case					     =       downloaded_path.read_text()
      assert content == CONTENT
      _snake_case					     =       downloaded_path.with_suffix('''.json'''		)
      assert metadata_downloaded_path.exists()
      _snake_case					     =       json.loads(metadata_downloaded_path.read_text()		)
      assert metadata_content == {"url": URL, "etag": None}
@pytest.mark.parametrize('''paths_type''' ,					[str, list, dict]		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       Optional[int]		)     ->					int:
  _snake_case					     =       str(__lowerCamelCase		)
  if issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       filename
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       [filename]
  elif issubclass(__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       {'''train''': filename}
  _snake_case					     =       '''dummy'''
  _snake_case					     =       xz_file.parent
  _snake_case					     =       '''extracted'''
  _snake_case					     =       DownloadConfig(
      cache_dir=__lowerCamelCase ,					use_etag=__lowerCamelCase ,					)
  _snake_case					     =       DownloadManager(dataset_name=__lowerCamelCase ,					download_config=__lowerCamelCase		)
  _snake_case					     =       dl_manager.extract(__lowerCamelCase		)
  _snake_case					     =       paths
  for extracted_paths in [extracted_paths]:
    if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       [extracted_paths]
      _snake_case					     =       [paths]
    elif isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      assert "train" in extracted_paths.keys()
      _snake_case					     =       extracted_paths.values()
      _snake_case					     =       paths.values()
    assert extracted_paths
    for extracted_path, input_path in zip(__lowerCamelCase ,					__lowerCamelCase		):
      assert extracted_path == dl_manager.extracted_paths[input_path]
      _snake_case					     =       Path(__lowerCamelCase		)
      _snake_case					     =       extracted_path.parts
      assert parts[-1] == hash_url_to_filename(__lowerCamelCase ,					etag=__lowerCamelCase		)
      assert parts[-2] == extracted_subdir
      assert extracted_path.exists()
      _snake_case					     =       extracted_path.read_text()
      _snake_case					     =       text_file.read_text()
      assert extracted_file_content == expected_file_content
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[Any]		)     ->					Dict:
  assert path.endswith('''.jsonl'''		)
  for num_items, line in enumerate(__lowerCamelCase ,					start=1		):
    _snake_case					     =       json.loads(line.decode('''utf-8'''		)		)
    assert item.keys() == {"col_1", "col_2", "col_3"}
  assert num_items == 4
@pytest.mark.parametrize('''archive_jsonl''' ,					['''tar_jsonl_path''', '''zip_jsonl_path''']		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       str		)     ->					Dict:
  _snake_case					     =       request.getfixturevalue(__lowerCamelCase		)
  _snake_case					     =       DownloadManager()
  for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__lowerCamelCase		) ,					start=1		):
    _test_jsonl(__lowerCamelCase ,					__lowerCamelCase		)
  assert num_jsonl == 2
@pytest.mark.parametrize('''archive_nested_jsonl''' ,					['''tar_nested_jsonl_path''', '''zip_nested_jsonl_path''']		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[Any]		)     ->					Tuple:
  _snake_case					     =       request.getfixturevalue(__lowerCamelCase		)
  _snake_case					     =       DownloadManager()
  for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__lowerCamelCase		) ,					start=1		):
    for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__lowerCamelCase		) ,					start=1		):
      _test_jsonl(__lowerCamelCase ,					__lowerCamelCase		)
  assert num_tar == 1
  assert num_jsonl == 2

def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					List[Any]:
  _snake_case					     =       DownloadManager()
  for num_file, file in enumerate(dl_manager.iter_files(__lowerCamelCase		) ,					start=1		):
    assert os.path.basename(__lowerCamelCase		) == ("test.txt" if num_file == 1 else "train.txt")
  assert num_file == 2



