


'''simple docstring'''
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


__snake_case				 =	logging.get_logger(__name__)  # pylint: disable=invalid-name
class   lowercase      (				A__      ,						A__   ):

		"""simple docstring"""

		@register_to_config
		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None				):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__  :int				   =					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"

												UpperCamelCase__  :str				   =					torch.zeros(UpperCamelCase_       ,  UpperCamelCase_				)
							else:
												UpperCamelCase__  :str				   =					None

							UpperCamelCase__  :List[str]				   =					torch.nn.Parameter(UpperCamelCase_				)



class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							42
		_a       					=							42
		_a       					=							42
		_a       					=							42
		_a       					=							42
		_a       					=							42




		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  ):

							'''simple docstring'''


							super().__init__()

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




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					len(UpperCamelCase_				) if isinstance(UpperCamelCase_       ,  UpperCamelCase_				) else 1

							# get prompt text embeddings
							UpperCamelCase__  :str				   =					self.tokenizer(
							    UpperCamelCase_       ,  padding='''max_length'''       ,  max_length=self.tokenizer.model_max_length       ,  return_tensors='''pt'''       ,  )
							UpperCamelCase__  :Union[str, Any]				   =					text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												UpperCamelCase__  :int				   =					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}'''				)
												UpperCamelCase__  :Any				   =					text_input_ids[:, : self.tokenizer.model_max_length]
							UpperCamelCase__  :Optional[Any]				   =					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
							UpperCamelCase__  :List[str]				   =					prompt_embeds / prompt_embeds.norm(dim=-1       ,  keepdim=UpperCamelCase_				)

							# duplicate text embeddings for each generation per prompt
							UpperCamelCase__  :Optional[int]				   =					prompt_embeds.repeat_interleave(UpperCamelCase_       ,  dim=0				)

							if do_classifier_free_guidance:
												if self.learned_classifier_free_sampling_embeddings.learnable:
																	UpperCamelCase__  :str				   =					self.learned_classifier_free_sampling_embeddings.embeddings
																	UpperCamelCase__  :List[str]				   =					negative_prompt_embeds.unsqueeze(0				).repeat(UpperCamelCase_       ,  1       ,  1				)
												else:
																	UpperCamelCase__  :Any				   =					[''''''] * batch_size

																	UpperCamelCase__  :Tuple				   =					text_input_ids.shape[-1]
																	UpperCamelCase__  :Dict				   =					self.tokenizer(
																	    UpperCamelCase_       ,  padding='''max_length'''       ,  max_length=UpperCamelCase_       ,  truncation=UpperCamelCase_       ,  return_tensors='''pt'''       ,  )
																	UpperCamelCase__  :int				   =					self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
																	# See comment for normalizing text embeddings
																	UpperCamelCase__  :int				   =					negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1       ,  keepdim=UpperCamelCase_				)

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												UpperCamelCase__  :Any				   =					negative_prompt_embeds.shape[1]
												UpperCamelCase__  :Tuple				   =					negative_prompt_embeds.repeat(1       ,  UpperCamelCase_       ,  1				)
												UpperCamelCase__  :Any				   =					negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,  UpperCamelCase_       ,  -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
												UpperCamelCase__  :Any				   =					torch.cat([negative_prompt_embeds, prompt_embeds]				)

							return prompt_embeds




		@torch.no_grad()
		def __call__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_ = 100       ,  UpperCamelCase_ = 5.0       ,  UpperCamelCase_ = 1.0       ,  UpperCamelCase_ = 1       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = "pil"       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = 1       ,  ):

							'''simple docstring'''


							if isinstance(UpperCamelCase_       ,  UpperCamelCase_				):
												UpperCamelCase__  :List[Any]				   =					1
							elif isinstance(UpperCamelCase_       ,  UpperCamelCase_				):
												UpperCamelCase__  :Any				   =					len(UpperCamelCase_				)
							else:
												raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_				)}'''				)

							UpperCamelCase__  :Any				   =					batch_size * num_images_per_prompt

							UpperCamelCase__  :str				   =					guidance_scale > 1.0

							UpperCamelCase__  :Tuple				   =					self._encode_prompt(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				)

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

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

							UpperCamelCase__  :int				   =					(batch_size, self.transformer.num_latent_pixels)
							if latents is None:
												UpperCamelCase__  :Optional[Any]				   =					self.transformer.num_vector_embeds - 1
												UpperCamelCase__  :int				   =					torch.full(UpperCamelCase_       ,  UpperCamelCase_				).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).'''				)
												UpperCamelCase__  :str				   =					latents.to(self.device				)

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

							UpperCamelCase__  :Dict				   =					self.scheduler.timesteps.to(self.device				)

							UpperCamelCase__  :List[str]				   =					latents

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

												# predict the un-noised image
												# model_output == `log_p_x_0`
												UpperCamelCase__  :Optional[Any]				   =					self.transformer(UpperCamelCase_       ,  encoder_hidden_states=UpperCamelCase_       ,  timestep=UpperCamelCase_				).sample

												if do_classifier_free_guidance:
																	UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					model_output.chunk(2				)
																	UpperCamelCase__  :Any				   =					model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																	model_output -= torch.logsumexp(UpperCamelCase_       ,  dim=1       ,  keepdim=UpperCamelCase_				)

												UpperCamelCase__  :List[Any]				   =					self.truncate(UpperCamelCase_       ,  UpperCamelCase_				)

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

												# compute the previous noisy sample x_t -> x_t-1
												UpperCamelCase__  :List[str]				   =					self.scheduler.step(UpperCamelCase_       ,  timestep=UpperCamelCase_       ,  sample=UpperCamelCase_       ,  generator=UpperCamelCase_				).prev_sample

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

							UpperCamelCase__  :Any				   =					self.vqvae.config.vq_embed_dim
							UpperCamelCase__  :str				   =					(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
							UpperCamelCase__  :List[str]				   =					self.vqvae.quantize.get_codebook_entry(UpperCamelCase_       ,  shape=UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					self.vqvae.decode(UpperCamelCase_       ,  force_not_quantize=UpperCamelCase_				).sample

							UpperCamelCase__  :Dict				   =					(image / 2 + 0.5).clamp(0       ,  1				)
							UpperCamelCase__  :Tuple				   =					image.cpu().permute(0       ,  2       ,  3       ,  1				).numpy()

							if output_type == "pil":
												UpperCamelCase__  :Tuple				   =					self.numpy_to_pil(UpperCamelCase_				)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :List[str]				   =					torch.sort(UpperCamelCase_       ,  1       ,  descending=UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					torch.exp(UpperCamelCase_				)
							UpperCamelCase__  :Optional[Any]				   =					sorted_p_x_0.cumsum(dim=1				) < truncation_rate

							# Ensure that at least the largest probability is not zeroed out
							UpperCamelCase__  :Any				   =					torch.full_like(keep_mask[:, 0:1, :]       ,  UpperCamelCase_				)
							UpperCamelCase__  :int				   =					torch.cat((all_true, keep_mask)       ,  dim=1				)
							UpperCamelCase__  :List[str]				   =					keep_mask[:, :-1, :]

							UpperCamelCase__  :List[Any]				   =					keep_mask.gather(1       ,  indices.argsort(1				)				)

							UpperCamelCase__  :str				   =					log_p_x_0.clone()

							UpperCamelCase__  :Any				   =					-torch.inf  # -inf = log(0)

							return rv



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__snake_case				 =	{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoForCausalLM''',
		    '''GPTNeoForQuestionAnswering''',
		    '''GPTNeoForSequenceClassification''',
		    '''GPTNeoForTokenClassification''',
		    '''GPTNeoModel''',
		    '''GPTNeoPreTrainedModel''',
		    '''load_tf_weights_in_gpt_neo''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''FlaxGPTNeoForCausalLM''',
		    '''FlaxGPTNeoModel''',
		    '''FlaxGPTNeoPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neo import (
				    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoForCausalLM,
				    GPTNeoForQuestionAnswering,
				    GPTNeoForSequenceClassification,
				    GPTNeoForTokenClassification,
				    GPTNeoModel,
				    GPTNeoPreTrainedModel,
				    load_tf_weights_in_gpt_neo,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
		import sys

		__snake_case				 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)