

import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCamelCase_    :		Optional[int]							=	"""\
    Text data.
    Second line of data."""

lowerCamelCase_    :		int							=	"""file"""
@pytest.fixture(scope='session'						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       tmp_path_factory.mktemp('data'						) / (FILE_PATH + '.zstd')
					__a		 =       bytes(__lowerCamelCase				,    'utf-8'						)
					with zstd.open(__lowerCamelCase				,    'wb'						) as f:
										f.write(__lowerCamelCase						)
					return path
@pytest.fixture
def 							lowerCAmelCase(     __lowerCamelCase						):
					with open(os.path.join(tmpfs.local_root_dir				,    __lowerCamelCase						)				,    'w'						) as f:
										f.write(__lowerCamelCase						)
					return FILE_PATH
@pytest.mark.parametrize('compression_format'				,    ['gzip', 'xz', 'zstd']						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
					__a		 =       input_paths[compression_format]
					__a		 =       tmp_path / 'cache'
					__a		 =       DownloadConfig(cache_dir=__lowerCamelCase				,    extract_compressed_file=__lowerCamelCase						)
					__a		 =       cached_path(__lowerCamelCase				,    download_config=__lowerCamelCase						)
					with open(__lowerCamelCase						) as f:
										__a		 =       f.read()
					with open(__lowerCamelCase						) as f:
										__a		 =       f.read()
					assert extracted_file_content == expected_file_content
@pytest.mark.parametrize('default_extracted'				,    [True, False]						)
@pytest.mark.parametrize('default_cache_dir'				,    [True, False]						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       'custom_cache'
					__a		 =       'custom_extracted_dir'
					__a		 =       tmp_path / 'custom_extracted_path'
					if default_extracted:
										__a		 =       ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
					else:
										monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'				,    __lowerCamelCase						)
										monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'				,    str(__lowerCamelCase						)						)
										__a		 =       custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

					__a		 =       xz_file
					__a		 =       (
					    DownloadConfig(extract_compressed_file=__lowerCamelCase						)
					    if default_cache_dir
					    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir				,    extract_compressed_file=__lowerCamelCase						)
					)
					__a		 =       cached_path(__lowerCamelCase				,    download_config=__lowerCamelCase						)
					assert Path(__lowerCamelCase						).parent.parts[-2:] == expected
def 							lowerCAmelCase(     __lowerCamelCase						):
					# absolute path
					__a		 =       str(Path(__lowerCamelCase						).resolve()						)
					assert cached_path(__lowerCamelCase						) == text_file
					# relative path
					__a		 =       str(Path(__lowerCamelCase						).resolve().relative_to(Path(os.getcwd()						)						)						)
					assert cached_path(__lowerCamelCase						) == text_file
def 							lowerCAmelCase(     __lowerCamelCase						):
					# absolute path
					__a		 =       str(tmp_path.resolve() / '__missing_file__.txt'						)
					with pytest.raises(__lowerCamelCase						):
										cached_path(__lowerCamelCase						)
					# relative path
					__a		 =       './__missing_file__.txt'
					with pytest.raises(__lowerCamelCase						):
										cached_path(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       get_from_cache(f'''tmp://{tmpfs_file}'''						)
					with open(__lowerCamelCase						) as f:
										__a		 =       f.read()
					assert output_file_content == FILE_CONTENT
@patch('datasets.config.HF_DATASETS_OFFLINE'				,    __lowerCamelCase						)
def 							lowerCAmelCase(     ):
					with pytest.raises(__lowerCamelCase						):
										cached_path('https://huggingface.co'						)
@patch('datasets.config.HF_DATASETS_OFFLINE'				,    __lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       tmp_path_factory.mktemp('data'						) / 'file.html'
					with pytest.raises(__lowerCamelCase						):
										http_get('https://huggingface.co'				,    temp_file=__lowerCamelCase						)
					with pytest.raises(__lowerCamelCase						):
										http_head('https://huggingface.co'						)
@patch('datasets.config.HF_DATASETS_OFFLINE'				,    __lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       tmp_path_factory.mktemp('data'						) / 'file.html'
					with pytest.raises(__lowerCamelCase						):
										ftp_get('ftp://huggingface.co'				,    temp_file=__lowerCamelCase						)
					with pytest.raises(__lowerCamelCase						):
										ftp_head('ftp://huggingface.co'						)



@patch('datasets.config.HF_DATASETS_OFFLINE'				,    __lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       tmp_path_factory.mktemp('data'						) / 'file.html'
					with pytest.raises(__lowerCamelCase						):
										fsspec_get('s3://huggingface.co'				,    temp_file=__lowerCamelCase						)
					with pytest.raises(__lowerCamelCase						):
										fsspec_head('s3://huggingface.co'						)





import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np


class 						a__							:



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=1				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=3				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								torch.manual_seed(0   )
								__a		 =       TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

								torch.manual_seed(0   )
								__a		 =       UNetaDConditionModel(
								    sample_size=3_2				,						layers_per_block=[1, 2]				,						block_out_channels=[3_2, 6_4]				,						down_block_types=[
								        'ResnetDownsampleBlock2D',
								        'SimpleCrossAttnDownBlock2D',
								    ]				,						mid_block_type='UNetMidBlock2DSimpleCrossAttn'				,						up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']				,						in_channels=6				,						out_channels=6				,						cross_attention_dim=3_2				,						encoder_hid_dim=3_2				,						attention_head_dim=8				,						addition_embed_type='text'				,						addition_embed_type_num_heads=2				,						cross_attention_norm='group_norm'				,						resnet_time_scale_shift='scale_shift'				,						act_fn='gelu'				,						class_embed_type='timestep'				,						mid_block_scale_factor=1.414				,						time_embedding_act_fn='gelu'				,						time_embedding_dim=3_2				,						)
								unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						thresholding=UpperCAmelCase				,						dynamic_thresholding_ratio=0.95				,						sample_max_value=1.0				,						prediction_type='epsilon'				,						variance_type='learned_range'				,						)

								torch.manual_seed(0   )
								__a		 =       DDPMScheduler(
								    num_train_timesteps=1_0_0_0				,						beta_schedule='squaredcos_cap_v2'				,						beta_start=0.0_001				,						beta_end=0.02				,						)

								torch.manual_seed(0   )
								__a		 =       IFWatermarker()

								return {
								    "text_encoder": text_encoder,
								    "tokenizer": tokenizer,
								    "unet": unet,
								    "scheduler": scheduler,
								    "image_noising_scheduler": image_noising_scheduler,
								    "watermarker": watermarker,
								    "safety_checker": None,
								    "feature_extractor": None,
								}



			def __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['prompt']
								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								if "image" in inputs:
													__a		 =       inputs['image']
								else:
													__a		 =       None

								if "mask_image" in inputs:
													__a		 =       inputs['mask_image']
								else:
													__a		 =       None

								if "original_image" in inputs:
													__a		 =       inputs['original_image']
								else:
													__a		 =       None

								__a ,				__a		 =       pipe.encode_prompt(UpperCAmelCase   )

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								# set all optional components to None
								for optional_component in pipe._optional_components:
													setattr(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(UpperCAmelCase				,						UpperCAmelCase   ) is None				,						f'''`{optional_component}` did not stay set to None after loading.'''				,						)

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )

								__a		 =       inputs['generator']
								__a		 =       inputs['num_inference_steps']
								__a		 =       inputs['output_type']

								# inputs with prompt converted to embeddings
								__a		 =       {
								    'prompt_embeds': prompt_embeds,
								    'negative_prompt_embeds': negative_prompt_embeds,
								    'generator': generator,
								    'num_inference_steps': num_inference_steps,
								    'output_type': output_type,
								}

								if image is not None:
													__a		 =       image

								if mask_image is not None:
													__a		 =       mask_image

								if original_image is not None:
													__a		 =       original_image

								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.get_dummy_components()
								__a		 =       self.pipeline_class(**UpperCAmelCase   )
								pipe.to(UpperCAmelCase   )
								pipe.set_progress_bar_config(disable=UpperCAmelCase   )

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe(**UpperCAmelCase   )[0]

								with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(UpperCAmelCase   )
													__a		 =       self.pipeline_class.from_pretrained(UpperCAmelCase   )
													pipe_loaded.to(UpperCAmelCase   )
													pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase   )

								pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

								__a		 =       self.get_dummy_inputs(UpperCAmelCase   )
								__a		 =       pipe_loaded(**UpperCAmelCase   )[0]

								__a		 =       np.abs(to_np(UpperCAmelCase   ) - to_np(UpperCAmelCase   )   ).max()
								self.assertLess(UpperCAmelCase				,						1e-4   )




