

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   int       				=  0
	_lowercase  :   bool       				=  False
	_lowercase  :   float       				=  3.0




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						self.assertDictEqual(MockClass().to_kwargs()  , {}      )
						self.assertDictEqual(MockClass(a=2      ).to_kwargs()  , {'a': 2}      )
						self.assertDictEqual(MockClass(a=2  , b=__UpperCamelCase      ).to_kwargs()  , {'a': 2, 'b': True}      )
						self.assertDictEqual(MockClass(a=2  , c=2.25      ).to_kwargs()  , {'a': 2, 'c': 2.25}      )







	@require_cuda
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    GradScalerKwargs(init_scale=10_24  , growth_factor=2      )
						AcceleratorState._reset_state()
						__magic_name__							=    Accelerator(mixed_precision='fp16'  , kwargs_handlers=[scaler_handler]      )
						print(accelerator.use_fpaa      )
						__magic_name__							=    accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale  , 1024.0      )
						self.assertEqual(scaler._growth_factor  , 2.0      )

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor  , 0.5      )
						self.assertEqual(scaler._growth_interval  , 20_00      )
						self.assertEqual(scaler._enabled  , __UpperCamelCase      )







	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
						execute_subprocess_async(__UpperCamelCase  , env=os.environ.copy()      )


if __name__ == "__main__":
						A__						=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						A__						=							Accelerator(kwargs_handlers=[ddp_scaler])
						A__						=							torch.nn.Linear(100, 200)
						A__						=							accelerator.prepare(model)

						# Check the values changed in kwargs
						A__						=							""
						A__						=							model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)







import torch
from torch import nn

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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

						__magic_name__							=    nn.Parameter(torch.zeros(__UpperCamelCase      )      )

						# parameters for additional clip time embeddings
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )

						# parameters for encoder hidden states
						__magic_name__							=    clip_extra_context_tokens
						__magic_name__							=    nn.Linear(
						    __UpperCamelCase  , self.clip_extra_context_tokens * cross_attention_dim      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.LayerNorm(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						if do_classifier_free_guidance:
											# Add the classifier free guidance embeddings to the image embeddings
											__magic_name__							=    image_embeddings.shape[0]
											__magic_name__							=    self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
											__magic_name__							=    classifier_free_guidance_embeddings.expand(
											    __UpperCamelCase  , -1      )
											__magic_name__							=    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__							=    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__							=    self.embedding_proj(__UpperCamelCase      )
						__magic_name__							=    self.clip_image_embeddings_project_to_time_embeddings(__UpperCamelCase      )
						__magic_name__							=    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__							=    self.clip_extra_context_tokens_proj(__UpperCamelCase      )
						__magic_name__							=    clip_extra_context_tokens.reshape(__UpperCamelCase  , -1  , self.clip_extra_context_tokens      )
						__magic_name__							=    clip_extra_context_tokens.permute(0  , 2  , 1      )

						__magic_name__							=    self.encoder_hidden_states_proj(__UpperCamelCase      )
						__magic_name__							=    self.text_encoder_hidden_states_norm(__UpperCamelCase      )
						__magic_name__							=    torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]  , dim=1      )

						return text_encoder_hidden_states, additive_clip_time_embeddings




