
from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:torch.FloatTensor

class  lowercase__(				UpperCAmelCase ,						UpperCAmelCase							):



	"""simple docstring"""




	@register_to_config
	def __init__(       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			int = 3_2       ,							SCREAMING_SNAKE_CASE_  :			int = 6_4       ,							SCREAMING_SNAKE_CASE_  :			int = 2_0       ,							SCREAMING_SNAKE_CASE_  :			int = 7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Tuple=7_7       ,							SCREAMING_SNAKE_CASE_  :			Tuple=4       ,							SCREAMING_SNAKE_CASE_  :			float = 0.0       ,							SCREAMING_SNAKE_CASE_  :			str = "silu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = "linear"       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = "prd"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							)       ->     Tuple:
					super().__init__()
					lowercase_       =					num_attention_heads
					lowercase_       =					attention_head_dim
					lowercase_       =					num_attention_heads * attention_head_dim
					lowercase_       =					additional_embeddings

					lowercase_       =					time_embed_dim or inner_dim
					lowercase_       =					embedding_proj_dim or embedding_dim
					lowercase_       =					clip_embed_dim or embedding_dim

					lowercase_       =					Timesteps(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							0       )
					lowercase_       =					TimestepEmbedding(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							out_dim=SCREAMING_SNAKE_CASE_       ,							act_fn=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					nn.Linear(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					if embedding_proj_norm_type is None:
									lowercase_       =					None
					elif embedding_proj_norm_type == "layer":
									lowercase_       =					nn.LayerNorm(SCREAMING_SNAKE_CASE_       )
					else:
									raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''       )

					lowercase_       =					nn.Linear(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					if encoder_hid_proj_type is None:
									lowercase_       =					None
					elif encoder_hid_proj_type == "linear":
									lowercase_       =					nn.Linear(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					else:
									raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''       )

					lowercase_       =					nn.Parameter(torch.zeros(1       ,							num_embeddings + additional_embeddings       ,							SCREAMING_SNAKE_CASE_       )       )

					if added_emb_type == "prd":
									lowercase_       =					nn.Parameter(torch.zeros(1       ,							1       ,							SCREAMING_SNAKE_CASE_       )       )
					elif added_emb_type is None:
									lowercase_       =					None
					else:
									raise ValueError(
									    f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'''       )

					lowercase_       =					nn.ModuleList(
					    [
					        BasicTransformerBlock(
					            SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							dropout=SCREAMING_SNAKE_CASE_       ,							activation_fn='''gelu'''       ,							attention_bias=SCREAMING_SNAKE_CASE_       ,							)
					        for d in range(SCREAMING_SNAKE_CASE_       )
					    ]       )

					if norm_in_type == "layer":
									lowercase_       =					nn.LayerNorm(SCREAMING_SNAKE_CASE_       )
					elif norm_in_type is None:
									lowercase_       =					None
					else:
									raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''       )

					lowercase_       =					nn.LayerNorm(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					nn.Linear(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.full(
					    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]       ,							-1_00_00.0       )
					causal_attention_mask.triu_(1       )
					lowercase_       =					causal_attention_mask[None, ...]
					self.register_buffer('''causal_attention_mask'''       ,							SCREAMING_SNAKE_CASE_       ,							persistent=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					nn.Parameter(torch.zeros(1       ,							SCREAMING_SNAKE_CASE_       )       )
					lowercase_       =					nn.Parameter(torch.zeros(1       ,							SCREAMING_SNAKE_CASE_       )       )
	@property
	# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
	def 						_lowercase  (       self  :			Dict       )       ->     Dict[str, AttentionProcessor]:
					lowercase_       =					{}

					def fn_recursive_add_processors(SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			torch.nn.Module       ,							SCREAMING_SNAKE_CASE_  :			Dict[str, AttentionProcessor]       ):
									if hasattr(SCREAMING_SNAKE_CASE_       ,							'''set_processor'''       ):
													lowercase_       =					module.processor

									for sub_name, child in module.named_children():
													fn_recursive_add_processors(f'''{name}.{sub_name}'''       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

									return processors

					for name, module in self.named_children():
									fn_recursive_add_processors(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					return processors
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[AttentionProcessor, Dict[str, AttentionProcessor]]       )       ->     int:
					lowercase_       =					len(self.attn_processors.keys()       )

					if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ) and len(SCREAMING_SNAKE_CASE_       ) != count:
									raise ValueError(
									    f'''A dict of processors was passed, but the number of processors {len(SCREAMING_SNAKE_CASE_       )} does not match the'''
									    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''       )

					def fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			torch.nn.Module       ,							SCREAMING_SNAKE_CASE_  :			int       ):
									if hasattr(SCREAMING_SNAKE_CASE_       ,							'''set_processor'''       ):
													if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
																	module.set_processor(SCREAMING_SNAKE_CASE_       )
													else:
																	module.set_processor(processor.pop(f'''{name}.processor'''       )       )

									for sub_name, child in module.named_children():
													fn_recursive_attn_processor(f'''{name}.{sub_name}'''       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					for name, module in self.named_children():
									fn_recursive_attn_processor(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       )       ->     Optional[int]:
					self.set_attn_processor(AttnProcessor()       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[torch.Tensor, float, int]       ,							SCREAMING_SNAKE_CASE_  :			torch.FloatTensor       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.FloatTensor] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[torch.BoolTensor] = None       ,							SCREAMING_SNAKE_CASE_  :			bool = True       ,							)       ->     List[Any]:
					lowercase_       =					hidden_states.shape[0]

					lowercase_       =					timestep
					if not torch.is_tensor(SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					torch.tensor([timesteps]       ,							dtype=torch.long       ,							device=hidden_states.device       )
					elif torch.is_tensor(SCREAMING_SNAKE_CASE_       ) and len(timesteps.shape       ) == 0:
									lowercase_       =					timesteps[None].to(hidden_states.device       )

					# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
					lowercase_       =					timesteps * torch.ones(SCREAMING_SNAKE_CASE_       ,							dtype=timesteps.dtype       ,							device=timesteps.device       )

					lowercase_       =					self.time_proj(SCREAMING_SNAKE_CASE_       )

					# timesteps does not contain any weights and will always return f32 tensors
					# but time_embedding might be fp16, so we need to cast here.
					lowercase_       =					timesteps_projected.to(dtype=self.dtype       )
					lowercase_       =					self.time_embedding(SCREAMING_SNAKE_CASE_       )

					if self.embedding_proj_norm is not None:
									lowercase_       =					self.embedding_proj_norm(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.embedding_proj(SCREAMING_SNAKE_CASE_       )
					if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
									lowercase_       =					self.encoder_hidden_states_proj(SCREAMING_SNAKE_CASE_       )
					elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
									raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'''       )

					lowercase_       =					self.proj_in(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.positional_embedding.to(hidden_states.dtype       )

					lowercase_       =					[]
					lowercase_       =					0

					if encoder_hidden_states is not None:
									additional_embeds.append(SCREAMING_SNAKE_CASE_       )
									additional_embeddings_len += encoder_hidden_states.shape[1]

					if len(proj_embeddings.shape       ) == 2:
									lowercase_       =					proj_embeddings[:, None, :]

					if len(hidden_states.shape       ) == 2:
									lowercase_       =					hidden_states[:, None, :]

					lowercase_       =					additional_embeds + [
					    proj_embeddings,
					    time_embeddings[:, None, :],
					    hidden_states,
					]

					if self.prd_embedding is not None:
									lowercase_       =					self.prd_embedding.to(hidden_states.dtype       ).expand(SCREAMING_SNAKE_CASE_       ,							-1       ,							-1       )
									additional_embeds.append(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					torch.cat(
					    SCREAMING_SNAKE_CASE_       ,							dim=1       ,							)

					# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
					lowercase_       =					additional_embeddings_len + proj_embeddings.shape[1] + 1
					if positional_embeddings.shape[1] < hidden_states.shape[1]:
									lowercase_       =					F.pad(
									    SCREAMING_SNAKE_CASE_       ,							(
									        0,
									        0,
									        additional_embeddings_len,
									        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
									    )       ,							value=0.0       ,							)

					lowercase_       =					hidden_states + positional_embeddings

					if attention_mask is not None:
									lowercase_       =					(1 - attention_mask.to(hidden_states.dtype       )) * -1_00_00.0
									lowercase_       =					F.pad(SCREAMING_SNAKE_CASE_       ,							(0, self.additional_embeddings)       ,							value=0.0       )
									lowercase_       =					(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype       )
									lowercase_       =					attention_mask.repeat_interleave(self.config.num_attention_heads       ,							dim=0       )

					if self.norm_in is not None:
									lowercase_       =					self.norm_in(SCREAMING_SNAKE_CASE_       )

					for block in self.transformer_blocks:
									lowercase_       =					block(SCREAMING_SNAKE_CASE_       ,							attention_mask=SCREAMING_SNAKE_CASE_       )

					lowercase_       =					self.norm_out(SCREAMING_SNAKE_CASE_       )

					if self.prd_embedding is not None:
									lowercase_       =					hidden_states[:, -1]
					else:
									lowercase_       =					hidden_states[:, additional_embeddings_len:]

					lowercase_       =					self.proj_to_clip_embeddings(SCREAMING_SNAKE_CASE_       )

					if not return_dict:
									return (predicted_image_embedding,)

					return PriorTransformerOutput(predicted_image_embedding=SCREAMING_SNAKE_CASE_       )




	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Dict:
					lowercase_       =					(prior_latents * self.clip_std) + self.clip_mean
					return prior_latents






import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard




@pytest.mark.parametrize(
    '''compression_format, is_archive'''						,   [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]						,   )
def 				a						(							snake_case__:					int						,   snake_case__:					Tuple						,   snake_case__:					Dict						,   snake_case__:					Dict						,   snake_case__:					List[Any]						,   snake_case__:					int						,   snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					str						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[str]						,   snake_case__:					int						,   ):


				'''simple docstring'''
				lowercase_       =					{
				    '''7z''': (seven_zip_file, SevenZipExtractor),
				    '''bz2''': (bza_file, BzipaExtractor),
				    '''gzip''': (gz_file, GzipExtractor),
				    '''lz4''': (lza_file, LzaExtractor),
				    '''tar''': (tar_file, TarExtractor),
				    '''xz''': (xz_file, XzExtractor),
				    '''zip''': (zip_file, ZipExtractor),
				    '''zstd''': (zstd_file, ZstdExtractor),
				}
				lowercase_  ,	lowercase_       =					input_paths_and_base_extractors[compression_format]
				if input_path is None:
								lowercase_       =					F'''for \'{compression_format}\' compression_format, '''
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(snake_case__					)
				assert base_extractor.is_extractable(snake_case__					)
				lowercase_       =					tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
				base_extractor.extract(snake_case__						,   snake_case__					)
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												lowercase_       =					file_path.read_text(encoding='''utf-8'''					)
				else:
								lowercase_       =					output_path.read_text(encoding='''utf-8'''					)
				lowercase_       =					text_file.read_text(encoding='''utf-8'''					)
				assert extracted_file_content == expected_file_content




@pytest.mark.parametrize(
    '''compression_format, is_archive'''						,   [
        ('''7z''', True),
        ('''bz2''', False),
        ('''gzip''', False),
        ('''lz4''', False),
        ('''tar''', True),
        ('''xz''', False),
        ('''zip''', True),
        ('''zstd''', False),
    ]						,   )
def 				a						(							snake_case__:					List[Any]						,   snake_case__:					int						,   snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]						,   snake_case__:					Tuple						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					Union[str, Any]						,   snake_case__:					Tuple						,   snake_case__:					int						,   snake_case__:					Optional[int]						,   ):


				'''simple docstring'''
				lowercase_       =					{
				    '''7z''': seven_zip_file,
				    '''bz2''': bza_file,
				    '''gzip''': gz_file,
				    '''lz4''': lza_file,
				    '''tar''': tar_file,
				    '''xz''': xz_file,
				    '''zip''': zip_file,
				    '''zstd''': zstd_file,
				}
				lowercase_       =					input_paths[compression_format]
				if input_path is None:
								lowercase_       =					F'''for \'{compression_format}\' compression_format, '''
								if compression_format == "7z":
												reason += require_pyazr.kwargs["reason"]
								elif compression_format == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_format == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(snake_case__					)
				lowercase_       =					Extractor.infer_extractor_format(snake_case__					)
				assert extractor_format is not None
				lowercase_       =					tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
				Extractor.extract(snake_case__						,   snake_case__						,   snake_case__					)
				if is_archive:
								assert output_path.is_dir()
								for file_path in output_path.iterdir():
												assert file_path.name == text_file.name
												lowercase_       =					file_path.read_text(encoding='''utf-8'''					)
				else:
								lowercase_       =					output_path.read_text(encoding='''utf-8'''					)
				lowercase_       =					text_file.read_text(encoding='''utf-8'''					)
				assert extracted_file_content == expected_file_content




@pytest.fixture
def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_dot_dot'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_dot_dot.tar'''
				with tarfile.TarFile(snake_case__						,   '''w'''					) as f:
								f.add(snake_case__						,   arcname=os.path.join('''..'''						,   text_file.name					)					)
				return path




@pytest.fixture
def 				a						(							snake_case__:					int					):


				'''simple docstring'''
				import tarfile

				lowercase_       =					tmp_path / '''data_sym_link'''
				directory.mkdir()
				lowercase_       =					directory / '''tar_file_with_sym_link.tar'''
				os.symlink('''..'''						,   directory / '''subdir'''						,   target_is_directory=snake_case__					)
				with tarfile.TarFile(snake_case__						,   '''w'''					) as f:
								f.add(str(directory / '''subdir'''					)						,   arcname='''subdir'''					)  # str required by os.readlink on Windows and Python < 3.8
				return path




@pytest.mark.parametrize(
    '''insecure_tar_file, error_log'''						,   [('''tar_file_with_dot_dot''', '''illegal path'''), ('''tar_file_with_sym_link''', '''Symlink''')]						,   )
def 				a						(							snake_case__:					List[Any]						,   snake_case__:					Optional[int]						,   snake_case__:					List[str]						,   snake_case__:					List[str]						,   snake_case__:					int						,   snake_case__:					Optional[Any]					):


				'''simple docstring'''
				lowercase_       =					{
				    '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
				    '''tar_file_with_sym_link''': tar_file_with_sym_link,
				}
				lowercase_       =					insecure_tar_files[insecure_tar_file]
				lowercase_       =					tmp_path / '''extracted'''
				TarExtractor.extract(snake_case__						,   snake_case__					)
				assert caplog.text
				for record in caplog.records:
								assert record.levelname == "ERROR"
								assert error_log in record.msg




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				# We should have less false positives than zipfile.is_zipfile
				# We do that by checking only the magic number
				lowercase_       =					tmpdir / '''not_a_zip_file'''
				# From: https://github.com/python/cpython/pull/5053
				lowercase_       =					(
				    B'''\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'''
				    B'''\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'''
				    B'''DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'''
				    B'''\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'''
				)
				with not_a_zip_file.open('''wb'''					) as f:
								f.write(snake_case__					)
				assert zipfile.is_zipfile(str(snake_case__					)					)  # is a false positive for `zipfile`
				assert not ZipExtractor.is_extractable(snake_case__					)  # but we're right




