


from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowercase							:   Optional[int]						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class    _UpperCamelCase     (       __snake_case     ,		__snake_case					):

				"""simple docstring"""







				@register_to_config
				def __init__(    self					, a__					, a__ = None					, a__ = None )						->					List[Any]:
					super().__init__()

					A             =					learnable

					if self.learnable:
						assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
						assert length is not None, "learnable=True requires `length` to be set"

						A             =					torch.zeros(a__					, a__ )
					else:
						A             =					None

					A             =					torch.nn.Parameter(a__ )






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			42
				lowerCAmelCase					=			42
				lowerCAmelCase					=			42
				lowerCAmelCase					=			42
				lowerCAmelCase					=			42
				lowerCAmelCase					=			42

				def __init__(    self					, a__					, a__					, a__					, a__					, a__					, a__					, )						->					int:
					super().__init__()

					self.register_modules(
					    vqvae=a__					, transformer=a__					, text_encoder=a__					, tokenizer=a__					, scheduler=a__					, learned_classifier_free_sampling_embeddings=a__					, )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					len(a__ ) if isinstance(a__					, a__ ) else 1

					# get prompt text embeddings
					A             =					self.tokenizer(
					    a__					, padding="""max_length"""					, max_length=self.tokenizer.model_max_length					, return_tensors="""pt"""					, )
					A             =					text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						A             =					self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
						logger.warning(
						    """The following part of your input was truncated because CLIP can only handle sequences up to"""
						    f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
						A             =					text_input_ids[:, : self.tokenizer.model_max_length]
					A             =					self.text_encoder(text_input_ids.to(self.device ) )[0]

					# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
					# While CLIP does normalize the pooled output of the text transformer when combining
					# the image and text embeddings, CLIP does not directly normalize the last hidden state.
					#
					# CLIP normalizing the pooled output.
					# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
					A             =					prompt_embeds / prompt_embeds.norm(dim=-1					, keepdim=a__ )

					# duplicate text embeddings for each generation per prompt
					A             =					prompt_embeds.repeat_interleave(a__					, dim=0 )

					if do_classifier_free_guidance:
						if self.learned_classifier_free_sampling_embeddings.learnable:
							A             =					self.learned_classifier_free_sampling_embeddings.embeddings
							A             =					negative_prompt_embeds.unsqueeze(0 ).repeat(a__					, 1					, 1 )
						else:
							A             =					[""""""] * batch_size

							A             =					text_input_ids.shape[-1]
							A             =					self.tokenizer(
							    a__					, padding="""max_length"""					, max_length=a__					, truncation=a__					, return_tensors="""pt"""					, )
							A             =					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
							# See comment for normalizing text embeddings
							A             =					negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1					, keepdim=a__ )

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						A             =					negative_prompt_embeds.shape[1]
						A             =					negative_prompt_embeds.repeat(1					, a__					, 1 )
						A             =					negative_prompt_embeds.view(batch_size * num_images_per_prompt					, a__					, -1 )

						# For classifier free guidance, we need to do two forward passes.
						# Here we concatenate the unconditional and text embeddings into a single batch
						# to avoid doing two forward passes
						A             =					torch.cat([negative_prompt_embeds, prompt_embeds] )

					return prompt_embeds

				@torch.no_grad()
				def __call__(    self					, a__					, a__ = 100					, a__ = 5.0					, a__ = 1.0					, a__ = 1					, a__ = None					, a__ = None					, a__ = "pil"					, a__ = True					, a__ = None					, a__ = 1					, )						->					Union[ImagePipelineOutput, Tuple]:
					if isinstance(a__					, a__ ):
						A             =					1
					elif isinstance(a__					, a__ ):
						A             =					len(a__ )
					else:
						raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(a__ )}' )

					A             =					batch_size * num_images_per_prompt

					A             =					guidance_scale > 1.0

					A             =					self._encode_prompt(a__					, a__					, a__ )

					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(a__					, a__ ) or callback_steps <= 0)
					):
						raise ValueError(
						    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
						    f' {type(a__ )}.' )

					# get the initial completely masked latents unless the user supplied it

					A             =					(batch_size, self.transformer.num_latent_pixels)
					if latents is None:
						A             =					self.transformer.num_vector_embeds - 1
						A             =					torch.full(a__					, a__ ).to(self.device )
					else:
						if latents.shape != latents_shape:
							raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
						if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
							raise ValueError(
							    """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
							    f' {self.transformer.num_vector_embeds - 1} (inclusive).' )
						A             =					latents.to(self.device )

					# set timesteps
					self.scheduler.set_timesteps(a__					, device=self.device )

					A             =					self.scheduler.timesteps.to(self.device )

					A             =					latents

					for i, t in enumerate(self.progress_bar(a__ ) ):
						# expand the sample if we are doing classifier free guidance
						A             =					torch.cat([sample] * 2 ) if do_classifier_free_guidance else sample

						# predict the un-noised image
						# model_output == `log_p_x_0`
						A             =					self.transformer(a__					, encoder_hidden_states=a__					, timestep=a__ ).sample

						if do_classifier_free_guidance:
							A       ,      A             =					model_output.chunk(2 )
							A             =					model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
							model_output -= torch.logsumexp(a__					, dim=1					, keepdim=a__ )

						A             =					self.truncate(a__					, a__ )

						# remove `log(0)`'s (`-inf`s)
						A             =					model_output.clamp(-70 )

						# compute the previous noisy sample x_t -> x_t-1
						A             =					self.scheduler.step(a__					, timestep=a__					, sample=a__					, generator=a__ ).prev_sample

						# call the callback, if provided
						if callback is not None and i % callback_steps == 0:
							callback(a__					, a__					, a__ )

					A             =					self.vqvae.config.vq_embed_dim
					A             =					(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
					A             =					self.vqvae.quantize.get_codebook_entry(a__					, shape=a__ )
					A             =					self.vqvae.decode(a__					, force_not_quantize=a__ ).sample

					A             =					(image / 2 + 0.5).clamp(0					, 1 )
					A             =					image.cpu().permute(0					, 2					, 3					, 1 ).numpy()

					if output_type == "pil":
						A             =					self.numpy_to_pil(a__ )

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ )						->					torch.FloatTensor:
					A       ,      A             =					torch.sort(a__					, 1					, descending=a__ )
					A             =					torch.exp(a__ )
					A             =					sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

					# Ensure that at least the largest probability is not zeroed out
					A             =					torch.full_like(keep_mask[:, 0:1, :]					, a__ )
					A             =					torch.cat((all_true, keep_mask)					, dim=1 )
					A             =					keep_mask[:, :-1, :]

					A             =					keep_mask.gather(1					, indices.argsort(1 ) )

					A             =					log_p_x_0.clone()

					A             =					-torch.inf  # -inf = log(0)

					return rv







from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:   Any						=						logging.get_logger(__name__)

_lowercase							:   Union[str, Any]						=						{
    "snap-research/efficientformer-l1-300": (
        "https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
    ),
}






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			'efficientformer'

				def __init__(    self					, a__ = [3, 2, 6, 4]					, a__ = [48, 96, 224, 448]					, a__ = [True, True, True, True]					, a__ = 448					, a__ = 32					, a__ = 4					, a__ = 7					, a__ = 5					, a__ = 8					, a__ = 4					, a__ = 0.0					, a__ = 16					, a__ = 3					, a__ = 3					, a__ = 3					, a__ = 2					, a__ = 1					, a__ = 0.0					, a__ = 1					, a__ = True					, a__ = True					, a__ = 1e-5					, a__ = "gelu"					, a__ = 0.02					, a__ = 1e-12					, a__ = 224					, a__ = 1e-05					, **a__					, )						->					None:
					super().__init__(**a__ )

					A             =					hidden_act
					A             =					hidden_dropout_prob
					A             =					hidden_sizes
					A             =					num_hidden_layers
					A             =					num_attention_heads
					A             =					initializer_range
					A             =					layer_norm_eps
					A             =					patch_size
					A             =					num_channels
					A             =					depths
					A             =					mlp_expansion_ratio
					A             =					downsamples
					A             =					dim
					A             =					key_dim
					A             =					attention_ratio
					A             =					resolution
					A             =					pool_size
					A             =					downsample_patch_size
					A             =					downsample_stride
					A             =					downsample_pad
					A             =					drop_path_rate
					A             =					num_metaad_blocks
					A             =					distillation
					A             =					use_layer_scale
					A             =					layer_scale_init_value
					A             =					image_size
					A             =					batch_norm_eps



