




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 		__A   (      __lowerCamelCase       ,    __lowerCamelCase      ):

						@register_to_config
						def __init__(		self :Tuple     ,			*,
						    __snake_case :int = 4     ,			__snake_case :int = 7_68     ,			__snake_case :int     ,			__snake_case :Any     ,			):
										'''simple docstring'''

										super().__init__()

										__magic_name__						:						Optional[int]										=nn.Parameter(torch.zeros(__snake_case			)			)

										# parameters for additional clip time embeddings
										__magic_name__						:						Tuple										=nn.Linear(__snake_case     ,			__snake_case			)
										__magic_name__						:						List[str]										=nn.Linear(__snake_case     ,			__snake_case			)

										# parameters for encoder hidden states
										__magic_name__						:						Optional[Any]										=clip_extra_context_tokens
										__magic_name__						:						str										=nn.Linear(
										    __snake_case     ,			self.clip_extra_context_tokens * cross_attention_dim			)
										__magic_name__						:						List[str]										=nn.Linear(__snake_case     ,			__snake_case			)
										__magic_name__						:						Tuple										=nn.LayerNorm(__snake_case			)


						def      A__    (		self :Union[str, Any]     ,			*, __snake_case :Optional[int]     ,			__snake_case :Union[str, Any]     ,			__snake_case :Union[str, Any]     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										if do_classifier_free_guidance:
														# Add the classifier free guidance embeddings to the image embeddings
														__magic_name__						:						Tuple										=image_embeddings.shape[0]
														__magic_name__						:						Dict										=self.learned_classifier_free_guidance_embeddings.unsqueeze(0			)
														__magic_name__						:						Dict										=classifier_free_guidance_embeddings.expand(
														    __snake_case     ,			-1			)
														__magic_name__						:						Dict										=torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,			dim=0			)

										# The image embeddings batch size and the text embeddings batch size are equal
										assert image_embeddings.shape[0] == prompt_embeds.shape[0]

										__magic_name__						:						Union[str, Any]										=prompt_embeds.shape[0]

										# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
										# adding CLIP embeddings to the existing timestep embedding, ...
										__magic_name__						:						Union[str, Any]										=self.embedding_proj(__snake_case			)
										__magic_name__						:						Any										=self.clip_image_embeddings_project_to_time_embeddings(__snake_case			)
										__magic_name__						:						List[Any]										=time_projected_image_embeddings + time_projected_prompt_embeds

										# ... and by projecting CLIP embeddings into four
										# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
										__magic_name__						:						List[str]										=self.clip_extra_context_tokens_proj(__snake_case			)
										__magic_name__						:						Optional[int]										=clip_extra_context_tokens.reshape(__snake_case     ,			-1     ,			self.clip_extra_context_tokens			)
										__magic_name__						:						List[Any]										=clip_extra_context_tokens.permute(0     ,			2     ,			1			)

										__magic_name__						:						Union[str, Any]										=self.encoder_hidden_states_proj(__snake_case			)
										__magic_name__						:						Optional[int]										=self.text_encoder_hidden_states_norm(__snake_case			)
										__magic_name__						:						Optional[Any]										=torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,			dim=1			)

										return text_encoder_hidden_states, additive_clip_time_embeddings






import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

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


__lowerCAmelCase					   =       4
__lowerCAmelCase					   =       3





class     lowerCamelCase      (       __lowerCamelCase					):
	pass



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




			'''simple docstring'''


			for shard in shards:
						for i in range(a	):
									yield {"i": i, "shard": shard}


def    a      ( )     ->List[str]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= int(os.environ['''RANK''']	)
			SCREAMING_SNAKE_CASE     						= int(os.environ['''WORLD_SIZE''']	)

			SCREAMING_SNAKE_CASE     						= ArgumentParser()
			parser.add_argument('''--streaming'''		,						type=a	)
			parser.add_argument('''--local_rank'''		,						type=a	)
			parser.add_argument('''--num_workers'''		,						type=a		,						default=0	)
			SCREAMING_SNAKE_CASE     						= parser.parse_args()
			SCREAMING_SNAKE_CASE     						= args.streaming
			SCREAMING_SNAKE_CASE     						= args.num_workers

			SCREAMING_SNAKE_CASE     						= {'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(a	)]}
			SCREAMING_SNAKE_CASE     						= IterableDataset.from_generator(a		,						gen_kwargs=a	)
			if not streaming:
						SCREAMING_SNAKE_CASE     						= Dataset.from_list(list(a	)	)

			SCREAMING_SNAKE_CASE     						= split_dataset_by_node(a		,						rank=a		,						world_size=a	)
			SCREAMING_SNAKE_CASE     						= torch.utils.data.DataLoader(a		,						num_workers=a	)

			SCREAMING_SNAKE_CASE     						= NUM_SHARDS * NUM_ITEMS_PER_SHARD
			SCREAMING_SNAKE_CASE     						= full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)	)

			SCREAMING_SNAKE_CASE     						= sum(1 for _ in dataloader	)
			if local_size != expected_local_size:
						raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""	)


if __name__ == "__main__":
							main()