
"""simple docstring"""







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

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin




@dataclass
class 					A__			(      _lowerCamelCase):
		A_		:			torch.FloatTensor




class 					A__			(      _lowerCamelCase			,	_lowerCamelCase):

		@register_to_config
		def __init__( self			,						_SCREAMING_SNAKE_CASE = 16			,						_SCREAMING_SNAKE_CASE = 88			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 0.0			,						_SCREAMING_SNAKE_CASE = 32			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = False			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = "geglu"			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = False			,						_SCREAMING_SNAKE_CASE = False			,						_SCREAMING_SNAKE_CASE = False			,						_SCREAMING_SNAKE_CASE = "layer_norm"			,						_SCREAMING_SNAKE_CASE = True			,						):
			super().__init__()
			__lowerCAmelCase     :		Optional[Any]		     = use_linear_projection
			__lowerCAmelCase     :		Dict		     = num_attention_heads
			__lowerCAmelCase     :		Optional[Any]		     = attention_head_dim
			__lowerCAmelCase     :		Any		     = num_attention_heads * attention_head_dim

			# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
			# Define whether input is continuous or discrete depending on configuration
			__lowerCAmelCase     :		Tuple		     = (in_channels is not None) and (patch_size is None)
			__lowerCAmelCase     :		Tuple		     = num_vector_embeds is not None
			__lowerCAmelCase     :		List[str]		     = in_channels is not None and patch_size is not None

			if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
				__lowerCAmelCase     :		Dict		     = (
				    f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
				    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
				    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
				    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
				    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
				)
				deprecate('norm_type!=num_embeds_ada_norm'			,						'1.0.0'			,						_SCREAMING_SNAKE_CASE			,						standard_warn=_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		List[str]		     = 'ada_norm'

			if self.is_input_continuous and self.is_input_vectorized:
				raise ValueError(
				    f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
				    ' sure that either `in_channels` or `num_vector_embeds` is None.'					)
			elif self.is_input_vectorized and self.is_input_patches:
				raise ValueError(
				    f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
				    ' sure that either `num_vector_embeds` or `num_patches` is None.'					)
			elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
				raise ValueError(
				    f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
				    f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."					)

			# 2. Define input layers
			if self.is_input_continuous:
				__lowerCAmelCase     :		Dict		     = in_channels

				__lowerCAmelCase     :		Any		     = torch.nn.GroupNorm(num_groups=_SCREAMING_SNAKE_CASE			,						num_channels=_SCREAMING_SNAKE_CASE			,						eps=1E-6			,						affine=_SCREAMING_SNAKE_CASE					)
				if use_linear_projection:
					__lowerCAmelCase     :		Union[str, Any]		     = nn.Linear(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
				else:
					__lowerCAmelCase     :		Tuple		     = nn.Convad(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						kernel_size=1			,						stride=1			,						padding=0					)
			elif self.is_input_vectorized:
				assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
				assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

				__lowerCAmelCase     :		List[Any]		     = sample_size
				__lowerCAmelCase     :		Optional[int]		     = sample_size
				__lowerCAmelCase     :		List[str]		     = num_vector_embeds
				__lowerCAmelCase     :		Tuple		     = self.height * self.width

				__lowerCAmelCase     :		List[Any]		     = ImagePositionalEmbeddings(
				    num_embed=_SCREAMING_SNAKE_CASE			,						embed_dim=_SCREAMING_SNAKE_CASE			,						height=self.height			,						width=self.width					)
			elif self.is_input_patches:
				assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

				__lowerCAmelCase     :		Union[str, Any]		     = sample_size
				__lowerCAmelCase     :		Union[str, Any]		     = sample_size

				__lowerCAmelCase     :		List[Any]		     = patch_size
				__lowerCAmelCase     :		Optional[Any]		     = PatchEmbed(
				    height=_SCREAMING_SNAKE_CASE			,						width=_SCREAMING_SNAKE_CASE			,						patch_size=_SCREAMING_SNAKE_CASE			,						in_channels=_SCREAMING_SNAKE_CASE			,						embed_dim=_SCREAMING_SNAKE_CASE			,						)

			# 3. Define transformers blocks
			__lowerCAmelCase     :		List[str]		     = nn.ModuleList(
			    [
			        BasicTransformerBlock(
			            _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						dropout=_SCREAMING_SNAKE_CASE			,						cross_attention_dim=_SCREAMING_SNAKE_CASE			,						activation_fn=_SCREAMING_SNAKE_CASE			,						num_embeds_ada_norm=_SCREAMING_SNAKE_CASE			,						attention_bias=_SCREAMING_SNAKE_CASE			,						only_cross_attention=_SCREAMING_SNAKE_CASE			,						upcast_attention=_SCREAMING_SNAKE_CASE			,						norm_type=_SCREAMING_SNAKE_CASE			,						norm_elementwise_affine=_SCREAMING_SNAKE_CASE			,						)
			        for d in range(_SCREAMING_SNAKE_CASE					)
			    ]					)

			# 4. Define output layers
			__lowerCAmelCase     :		int		     = in_channels if out_channels is None else out_channels
			if self.is_input_continuous:
				# TODO: should use out_channels for continuous projections
				if use_linear_projection:
					__lowerCAmelCase     :		Any		     = nn.Linear(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
				else:
					__lowerCAmelCase     :		Union[str, Any]		     = nn.Convad(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						kernel_size=1			,						stride=1			,						padding=0					)
			elif self.is_input_vectorized:
				__lowerCAmelCase     :		List[str]		     = nn.LayerNorm(_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		str		     = nn.Linear(_SCREAMING_SNAKE_CASE			,						self.num_vector_embeds - 1					)
			elif self.is_input_patches:
				__lowerCAmelCase     :		Optional[Any]		     = nn.LayerNorm(_SCREAMING_SNAKE_CASE			,						elementwise_affine=_SCREAMING_SNAKE_CASE			,						eps=1E-6					)
				__lowerCAmelCase     :		Optional[Any]		     = nn.Linear(_SCREAMING_SNAKE_CASE			,						2 * inner_dim					)
				__lowerCAmelCase     :		Union[str, Any]		     = nn.Linear(_SCREAMING_SNAKE_CASE			,						patch_size * patch_size * self.out_channels					)







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = True			,						):
			# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
			#   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
			#   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
			# expects mask of shape:
			#   [batch, key_tokens]
			# adds singleton query_tokens dimension:
			#   [batch,                    1, key_tokens]
			# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
			#   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
			#   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
			if attention_mask is not None and attention_mask.ndim == 2:
				# assume that mask is expressed as:
				#   (1 = keep,      0 = discard)
				# convert mask into a bias that can be added to attention scores:
				#       (keep = +0,     discard = -10000.0)
				__lowerCAmelCase     :		Union[str, Any]		     = (1 - attention_mask.to(hidden_states.dtype					)) * -1_0000.0
				__lowerCAmelCase     :		Union[str, Any]		     = attention_mask.unsqueeze(1					)

			# convert encoder_attention_mask to a bias the same way we do for attention_mask
			if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
				__lowerCAmelCase     :		Optional[Any]		     = (1 - encoder_attention_mask.to(hidden_states.dtype					)) * -1_0000.0
				__lowerCAmelCase     :		Tuple		     = encoder_attention_mask.unsqueeze(1					)

			# 1. Input
			if self.is_input_continuous:
				__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		Tuple		     = hidden_states.shape
				__lowerCAmelCase     :		Tuple		     = hidden_states

				__lowerCAmelCase     :		Tuple		     = self.norm(_SCREAMING_SNAKE_CASE					)
				if not self.use_linear_projection:
					__lowerCAmelCase     :		Union[str, Any]		     = self.proj_in(_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		str		     = hidden_states.shape[1]
					__lowerCAmelCase     :		List[Any]		     = hidden_states.permute(0			,						2			,						3			,						1					).reshape(_SCREAMING_SNAKE_CASE			,						height * width			,						_SCREAMING_SNAKE_CASE					)
				else:
					__lowerCAmelCase     :		Dict		     = hidden_states.shape[1]
					__lowerCAmelCase     :		str		     = hidden_states.permute(0			,						2			,						3			,						1					).reshape(_SCREAMING_SNAKE_CASE			,						height * width			,						_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		Optional[Any]		     = self.proj_in(_SCREAMING_SNAKE_CASE					)
			elif self.is_input_vectorized:
				__lowerCAmelCase     :		Tuple		     = self.latent_image_embedding(_SCREAMING_SNAKE_CASE					)
			elif self.is_input_patches:
				__lowerCAmelCase     :		List[Any]		     = self.pos_embed(_SCREAMING_SNAKE_CASE					)

			# 2. Blocks
			for block in self.transformer_blocks:
				__lowerCAmelCase     :		Dict		     = block(
				    _SCREAMING_SNAKE_CASE			,						attention_mask=_SCREAMING_SNAKE_CASE			,						encoder_hidden_states=_SCREAMING_SNAKE_CASE			,						encoder_attention_mask=_SCREAMING_SNAKE_CASE			,						timestep=_SCREAMING_SNAKE_CASE			,						cross_attention_kwargs=_SCREAMING_SNAKE_CASE			,						class_labels=_SCREAMING_SNAKE_CASE			,						)

			# 3. Output
			if self.is_input_continuous:
				if not self.use_linear_projection:
					__lowerCAmelCase     :		Optional[Any]		     = hidden_states.reshape(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					).permute(0			,						3			,						1			,						2					).contiguous()
					__lowerCAmelCase     :		Tuple		     = self.proj_out(_SCREAMING_SNAKE_CASE					)
				else:
					__lowerCAmelCase     :		Optional[Any]		     = self.proj_out(_SCREAMING_SNAKE_CASE					)
					__lowerCAmelCase     :		Optional[Any]		     = hidden_states.reshape(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					).permute(0			,						3			,						1			,						2					).contiguous()

				__lowerCAmelCase     :		Optional[int]		     = hidden_states + residual
			elif self.is_input_vectorized:
				__lowerCAmelCase     :		Optional[int]		     = self.norm_out(_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		int		     = self.out(_SCREAMING_SNAKE_CASE					)
				# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
				__lowerCAmelCase     :		Tuple		     = logits.permute(0			,						2			,						1					)

				# log(p(x_0))
				__lowerCAmelCase     :		str		     = F.log_softmax(logits.double()			,						dim=1					).float()
			elif self.is_input_patches:
				# TODO: cleanup!
				__lowerCAmelCase     :		Optional[Any]		     = self.transformer_blocks[0].norma.emb(
				    _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						hidden_dtype=hidden_states.dtype					)
				__lowerCAmelCase     ,  __lowerCAmelCase     :		int		     = self.proj_out_a(F.silu(_SCREAMING_SNAKE_CASE					)					).chunk(2			,						dim=1					)
				__lowerCAmelCase     :		str		     = self.norm_out(_SCREAMING_SNAKE_CASE					) * (1 + scale[:, None]) + shift[:, None]
				__lowerCAmelCase     :		int		     = self.proj_out_a(_SCREAMING_SNAKE_CASE					)

				# unpatchify
				__lowerCAmelCase     :		int		     = int(hidden_states.shape[1] ** 0.5					)
				__lowerCAmelCase     :		int		     = hidden_states.reshape(
				    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)					)
				__lowerCAmelCase     :		str		     = torch.einsum('nhwpqc->nchpwq'			,						_SCREAMING_SNAKE_CASE					)
				__lowerCAmelCase     :		Optional[int]		     = hidden_states.reshape(
				    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)					)

			if not return_dict:
				return (output,)

			return TransformeraDModelOutput(sample=_SCREAMING_SNAKE_CASE					)

"""simple docstring"""







import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def        __lowerCAmelCase							():
	__lowerCAmelCase     :		Optional[int]		     = Dataset.from_dict({'i': range(17						)}						)
	__lowerCAmelCase     :		List[Any]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Tuple		     = 3
	__lowerCAmelCase     :		List[Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(_UpperCamelCase						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[Any]		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		List[Any]		     = len(list(_UpperCamelCase						)						)
	__lowerCAmelCase     :		str		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
@pytest.mark.parametrize('shards_per_node'			,    [1, 2, 3]						)
def        __lowerCAmelCase							(_UpperCamelCase						):
	def gen(_UpperCamelCase						):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(17						))

	__lowerCAmelCase     :		List[Any]		     = 3
	__lowerCAmelCase     :		Optional[int]		     = shards_per_node * world_size
	__lowerCAmelCase     :		Dict		     = {'shards': [F"shard_{shard_idx}.txt" for shard_idx in range(_UpperCamelCase						)]}
	__lowerCAmelCase     :		Dict		     = IterableDataset.from_generator(_UpperCamelCase			,    gen_kwargs=_UpperCamelCase						)
	__lowerCAmelCase     :		str		     = len(list(_UpperCamelCase						)						)
	assert full_ds.n_shards == world_size * shards_per_node
	__lowerCAmelCase     :		Union[str, Any]		     = [
	    split_dataset_by_node(_UpperCamelCase			,    rank=_UpperCamelCase			,    world_size=_UpperCamelCase						) for rank in range(_UpperCamelCase						)
	]
	assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
	assert sum(len(list(_UpperCamelCase						)						) for ds in datasets_per_rank						) == full_size
	assert len({tuple(x.values()						) for ds in datasets_per_rank for x in ds}						) == full_size
def        __lowerCAmelCase							():
	def gen():
		return ({"i": i} for i in range(17						))

	__lowerCAmelCase     :		str		     = 2
	__lowerCAmelCase     :		Any		     = IterableDataset.from_generator(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = len(list(_UpperCamelCase						)						)

	__lowerCAmelCase     :		Dict		     = split_dataset_by_node(_UpperCamelCase			,    rank=0			,    world_size=_UpperCamelCase						).shuffle(seed=42						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(_UpperCamelCase			,    rank=0			,    world_size=_UpperCamelCase						).shuffle()

	__lowerCAmelCase     :		List[str]		     = split_dataset_by_node(full_ds.shuffle(seed=42						)			,    rank=0			,    world_size=_UpperCamelCase						)
	assert len(list(_UpperCamelCase						)						) == 1 + full_size // world_size
	with pytest.raises(_UpperCamelCase						):
		split_dataset_by_node(full_ds.shuffle()			,    rank=0			,    world_size=_UpperCamelCase						)
@pytest.mark.parametrize('streaming'			,    [False, True]						)
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = 2
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		List[Any]		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		int		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		Tuple		     = F"\n        --streaming={streaming}\n    ".split()
	__lowerCAmelCase     :		Optional[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)
@pytest.mark.parametrize(
    'nproc_per_node, num_workers'			,    [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]			,    )
@require_torch
@pytest.mark.skipif(os.name == 'nt'			,    reason='execute_subprocess_async doesn\'t support windows'						)
@pytest.mark.integration
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[int]		     = True
	__lowerCAmelCase     :		Optional[Any]		     = get_torch_dist_unique_port()
	__lowerCAmelCase     :		Tuple		     = Path(_UpperCamelCase						).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	__lowerCAmelCase     :		Optional[Any]		     = F"\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = F"\n        --streaming={streaming}\n        --num_workers={num_workers}\n    ".split()
	__lowerCAmelCase     :		List[Any]		     = [sys.executable] + distributed_args + args
	execute_subprocess_async(_UpperCamelCase			,    env=os.environ.copy()						)