

import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard





def      A__					(			__A  :						Optional[int]			)					->Optional[Any]:
      assert "mock" in _fsspec_registry
      assert "bz2" in _fsspec_registry





def      A__					(			)					->int:
      assert "mock" not in _fsspec_registry
      assert "bz2" in _fsspec_registry





def      A__					(			)					->Union[str, Any]:
      __A    ='''mock-s3-bucket'''
      __A    =F'''s3://{mock_bucket}'''
      __A    =extract_path_from_uri(__A			)
      assert dataset_path.startswith('''s3://'''			) is False

      __A    ='''./local/path'''
      __A    =extract_path_from_uri(__A			)
      assert dataset_path == new_dataset_path





def      A__					(			__A  :						Tuple			)					->Any:
      __A    =is_remote_filesystem(__A			)
      assert is_remote is True

      __A    =fsspec.filesystem('''file'''			)

      __A    =is_remote_filesystem(__A			)
      assert is_remote is False





@pytest.mark.parametrize('''compression_fs_class'''     ,				__A			)
def      A__					(			__A  :						Optional[Any]     ,				__A  :						Any     ,				__A  :						Optional[int]     ,				__A  :						Dict     ,				__A  :						Dict     ,				__A  :						int     ,				__A  :						str			)					->Optional[Any]:
      __A    ={'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
      __A    =input_paths[compression_fs_class.protocol]
      if input_path is None:
            __A    =F'''for \'{compression_fs_class.protocol}\' compression protocol, '''
            if compression_fs_class.protocol == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_fs_class.protocol == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(__A			)
      __A    =fsspec.filesystem(compression_fs_class.protocol     ,				fo=__A			)
      assert isinstance(__A     ,				__A			)
      __A    =os.path.basename(__A			)
      __A    =expected_filename[: expected_filename.rindex('''.'''			)]
      assert fs.glob('''*'''			) == [expected_filename]
      with fs.open(__A     ,				'''r'''     ,				encoding='''utf-8'''			) as f, open(__A     ,				encoding='''utf-8'''			) as expected_file:
            assert f.read() == expected_file.read()





@pytest.mark.parametrize('''protocol'''     ,				['''zip''', '''gzip''']			)
def      A__					(			__A  :						List[str]     ,				__A  :						Optional[int]     ,				__A  :						Dict			)					->List[Any]:
      __A    ={'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
      __A    =compressed_file_paths[protocol]
      __A    ='''dataset.jsonl'''
      __A    =F'''{protocol}://{member_file_path}::{compressed_file_path}'''
      __A    ,		*__A    =fsspec.get_fs_token_paths(__A			)
      assert fs.isfile(__A			)
      assert not fs.isfile('''non_existing_''' + member_file_path			)





@pytest.mark.integration
def      A__					(			__A  :						Dict     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						int			)					->Union[str, Any]:
      __A    =hf_api.dataset_info(__A     ,				token=__A			)
      __A    =HfFileSystem(repo_info=__A     ,				token=__A			)
      assert sorted(hffs.glob('''*'''			)			) == [".gitattributes", "data"]
      assert hffs.isdir('''data'''			)
      assert hffs.isfile('''.gitattributes'''			) and hffs.isfile('''data/text_data.txt'''			)
      with open(__A			) as f:
            assert hffs.open('''data/text_data.txt'''     ,				'''r'''			).read() == f.read()





def      A__					(			)					->List[str]:
      __A    ='''bz2'''

      # Import module
      import datasets.filesystems

      # Overwrite protocol and reload
      register_implementation(__A     ,				__A     ,				clobber=__A			)
      with pytest.warns(__A			) as warning_info:
            importlib.reload(datasets.filesystems			)

      assert len(__A			) == 1
      assert (
          str(warning_info[0].message			)
          == F'''A filesystem protocol was already set for {protocol} and will be overwritten.'''
      )





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    KandinskyVaaPipeline
       lowercase_						=    [
           """image_embeds""",
           """negative_image_embeds""",
       ]
       lowercase_						=    ["""image_embeds""", """negative_image_embeds"""]
       lowercase_						=    [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_						=    False

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim * 4

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1_0_0

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )

             __A    ={
                 '''in_channels''': 4,
                 # Out channels is double in channels because predicts mean and variance
                 '''out_channels''': 8,
                 '''addition_embed_type''': '''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''': '''image_proj''',
                 '''cross_attention_dim''': self.cross_attention_dim,
                 '''attention_head_dim''': 4,
                 '''resnet_time_scale_shift''': '''scale_shift''',
                 '''class_embed_type''': None,
             }

             __A    =UNetaDConditionModel(**lowercase__     )
             return model

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )
             __A    =VQModel(**self.dummy_movq_kwargs     )
             return model

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.dummy_unet
             __A    =self.dummy_movq

             __A    =DDIMScheduler(
                 num_train_timesteps=1_0_0_0 ,   beta_schedule='''linear''' ,   beta_start=0.0_0085 ,   beta_end=0.012 ,   clip_sample=lowercase__ ,   set_alpha_to_one=lowercase__ ,   steps_offset=1 ,   prediction_type='''epsilon''' ,   thresholding=lowercase__ ,   )

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

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(seed + 1     )     ).to(
                 lowercase__     )
             if str(lowercase__     ).startswith('''mps'''     ):
                   __A    =torch.manual_seed(lowercase__     )
             else:
                   __A    =torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
             __A    ={
                 '''image_embeds''': image_embeds,
                 '''negative_image_embeds''': negative_image_embeds,
                 '''generator''': generator,
                 '''height''': 6_4,
                 '''width''': 6_4,
                 '''guidance_scale''': 4.0,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''np''',
             }
             return inputs


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''

             __A    =self.get_dummy_components()

             __A    =self.pipeline_class(**lowercase__     )
             __A    =pipe.to(lowercase__     )

             pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =pipe(**self.get_dummy_inputs(lowercase__     )     )
             __A    =output.images

             __A    =pipe(
                 **self.get_dummy_inputs(lowercase__     ) ,   return_dict=lowercase__ ,   )[0]

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

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

             __A    =np.array(
                 [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]     )

             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        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''     )

             __A    =KandinskyVaaPriorPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-prior''' ,   torch_dtype=torch.floataa     )
             pipe_prior.to(lowercase__     )

             __A    =KandinskyVaaPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-decoder''' ,   torch_dtype=torch.floataa     )
             __A    =pipeline.to(lowercase__     )
             pipeline.set_progress_bar_config(disable=lowercase__     )

             __A    ='''red cat, 4k photo'''

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    ,		__A    =pipe_prior(
                 lowercase__ ,   generator=lowercase__ ,   num_inference_steps=5 ,   negative_prompt='''''' ,   ).to_tuple()

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    =pipeline(
                 image_embeds=lowercase__ ,   negative_image_embeds=lowercase__ ,   generator=lowercase__ ,   num_inference_steps=1_0_0 ,   output_type='''np''' ,   )

             __A    =output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)

             assert_mean_pixel_difference(lowercase__ ,   lowercase__     )


