
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       _snake_case					(     snake_case				,						snake_case				,						snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    StableUnCLIPPipeline
			UpperCamelCase__          =    TEXT_TO_IMAGE_PARAMS
			UpperCamelCase__          =    TEXT_TO_IMAGE_BATCH_PARAMS
			UpperCamelCase__          =    TEXT_TO_IMAGE_IMAGE_PARAMS
			UpperCamelCase__          =    TEXT_TO_IMAGE_IMAGE_PARAMS

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     32
				__magic_name__		:		Optional[Any]	   =     embedder_hidden_size

				# prior components

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

				torch.manual_seed(0		)
				__magic_name__		:		Any	   =     CLIPTextModelWithProjection(
				    CLIPTextConfig(
				        bos_token_id=0						,   eos_token_id=2						,   hidden_size=_a						,   projection_dim=_a						,   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		)
				__magic_name__		:		Optional[int]	   =     PriorTransformer(
				    num_attention_heads=2						,   attention_head_dim=12						,   embedding_dim=_a						,   num_layers=1						,   )

				torch.manual_seed(0		)
				__magic_name__		:		Dict	   =     DDPMScheduler(
				    variance_type="fixed_small_log"						,   prediction_type="sample"						,   num_train_timesteps=1_000						,   clip_sample=_a						,   clip_sample_range=5.0						,   beta_schedule="squaredcos_cap_v2"						,   )

				# regular denoising components

				torch.manual_seed(0		)
				__magic_name__		:		List[str]	   =     StableUnCLIPImageNormalizer(embedding_dim=_a		)
				__magic_name__		:		List[Any]	   =     DDPMScheduler(beta_schedule="squaredcos_cap_v2"		)

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

				torch.manual_seed(0		)
				__magic_name__		:		Dict	   =     CLIPTextModel(
				    CLIPTextConfig(
				        bos_token_id=0						,   eos_token_id=2						,   hidden_size=_a						,   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		)
				__magic_name__		:		Union[str, 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=_a						,   layers_per_block=1						,   upcast_attention=_a						,   use_linear_projection=_a						,   )

				torch.manual_seed(0		)
				__magic_name__		:		Optional[Any]	   =     DDIMScheduler(
				    beta_schedule="scaled_linear"						,   beta_start=0.0_00_85						,   beta_end=0.0_12						,   prediction_type="v_prediction"						,   set_alpha_to_one=_a						,   steps_offset=1						,   )

				torch.manual_seed(0		)
				__magic_name__		:		List[Any]	   =     AutoencoderKL()

				__magic_name__		:		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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				if str(_a		).startswith("mps"		):
					__magic_name__		:		Dict	   =     torch.manual_seed(_a		)
				else:
					__magic_name__		:		int	   =     torch.Generator(device=_a		).manual_seed(_a		)
				__magic_name__		:		int	   =     {
				    "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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     torch_device == "cpu"

				self._test_attention_slicing_forward_pass(test_max_difference=_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     torch_device in ["cpu", "mps"]

				self._test_inference_batch_single_identical(test_max_difference=_a		)







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







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"		)

				__magic_name__		:		int	   =     StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,   torch_dtype=torch.floataa		)
				pipe.to(_a		)
				pipe.set_progress_bar_config(disable=_a		)
				# 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()

				__magic_name__		:		Any	   =     torch.Generator(device="cpu"		).manual_seed(0		)
				__magic_name__		:		Optional[int]	   =     pipe("anime turle"						,   generator=_a						,   output_type="np"		)

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

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

				assert_mean_pixel_difference(_a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				__magic_name__		:		int	   =     StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,   torch_dtype=torch.floataa		)
				__magic_name__		:		List[str]	   =     pipe.to(_a		)
				pipe.set_progress_bar_config(disable=_a		)
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				__magic_name__		:		Any	   =     pipe(
				    "anime turtle"						,   prior_num_inference_steps=2						,   num_inference_steps=2						,   output_type="np"						,   )

				__magic_name__		:		int	   =     torch.cuda.max_memory_allocated()
				# make sure that less than 7 GB is allocated
				assert mem_bytes < 7 * 10**9


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   lowerCAmelCase_						(					_snake_case      :  List[Any]   )				->		List[Any]:







	'''simple docstring'''


	assert "mock" in _fsspec_registry
	assert "bz2" in _fsspec_registry






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	assert "mock" not in _fsspec_registry
	assert "bz2" in _fsspec_registry






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







	'''simple docstring'''


	__magic_name__		:		Dict	   =     "mock-s3-bucket"
	__magic_name__		:		Any	   =     F'''s3://{mock_bucket}'''
	__magic_name__		:		str	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path.startswith("s3://"   ) is False

	__magic_name__		:		Tuple	   =     "./local/path"
	__magic_name__		:		Optional[Any]	   =     extract_path_from_uri(_snake_case   )
	assert dataset_path == new_dataset_path






def   lowerCAmelCase_						(					_snake_case      :  List[str]   )				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is True

	__magic_name__		:		Optional[int]	   =     fsspec.filesystem("file"   )

	__magic_name__		:		int	   =     is_remote_filesystem(_snake_case   )
	assert is_remote is False






@pytest.mark.parametrize("compression_fs_class"      ,   _snake_case   )
def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  int      ,   _snake_case      :  Tuple      ,   _snake_case      :  Any      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Any   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     {"gzip": gz_file, "xz": xz_file, "zstd": zstd_file, "bz2": bza_file, "lz4": lza_file}
	__magic_name__		:		str	   =     input_paths[compression_fs_class.protocol]
	if input_path is None:
		__magic_name__		:		Dict	   =     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(_snake_case   )
	__magic_name__		:		str	   =     fsspec.filesystem(compression_fs_class.protocol      ,   fo=_snake_case   )
	assert isinstance(_snake_case      ,   _snake_case   )
	__magic_name__		:		int	   =     os.path.basename(_snake_case   )
	__magic_name__		:		Optional[int]	   =     expected_filename[: expected_filename.rindex("."   )]
	assert fs.glob("*"   ) == [expected_filename]
	with fs.open(_snake_case      ,   "r"      ,   encoding="utf-8"   ) as f, open(_snake_case      ,   encoding="utf-8"   ) as expected_file:
		assert f.read() == expected_file.read()






@pytest.mark.parametrize("protocol"      ,   ["zip", "gzip"]   )
def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     {"zip": zip_jsonl_path, "gzip": jsonl_gz_path}
	__magic_name__		:		int	   =     compressed_file_paths[protocol]
	__magic_name__		:		Tuple	   =     "dataset.jsonl"
	__magic_name__		:		List[str]	   =     F'''{protocol}://{member_file_path}::{compressed_file_path}'''
	__magic_name__   ,    *__magic_name__		:		Optional[Any]	   =     fsspec.get_fs_token_paths(_snake_case   )
	assert fs.isfile(_snake_case   )
	assert not fs.isfile("non_existing_" + member_file_path   )






@pytest.mark.integration
def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple   )				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     hf_api.dataset_info(_snake_case      ,   token=_snake_case   )
	__magic_name__		:		Optional[Any]	   =     HfFileSystem(repo_info=_snake_case      ,   token=_snake_case   )
	assert sorted(hffs.glob("*"   )   ) == [".gitattributes", "data"]
	assert hffs.isdir("data"   )
	assert hffs.isfile(".gitattributes"   ) and hffs.isfile("data/text_data.txt"   )
	with open(_snake_case   ) as f:
		assert hffs.open("data/text_data.txt"      ,   "r"   ).read() == f.read()






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     "bz2"

	# Import module
	import datasets.filesystems

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

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