
'''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


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


class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       snake_case_):







				@register_to_config
				def __init__(       self						,				A_						,				A_ = None						,				A_ = None     )->		Tuple:







											'''simple docstring'''






											super().__init__()

											UpperCamelCase   			=     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   			=     torch.zeros(A_						,				A_     )
											else:
																		UpperCamelCase   			=     None

											UpperCamelCase   			=     torch.nn.Parameter(A_     )


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42
				lowerCAmelCase_     			=							42







				def __init__(       self						,				A_						,				A_						,				A_						,				A_						,				A_						,				A_						,				)->		Union[str, Any]:







											'''simple docstring'''






											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_     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     len(A_     ) if isinstance(A_						,				A_     ) else 1

											# get prompt text embeddings
											UpperCamelCase   			=     self.tokenizer(
											    A_						,				padding='max_length'						,				max_length=self.tokenizer.model_max_length						,				return_tensors='pt'						,				)
											UpperCamelCase   			=     text_inputs.input_ids

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		UpperCamelCase   			=     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   			=     text_input_ids[:, : self.tokenizer.model_max_length]
											UpperCamelCase   			=     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   			=     prompt_embeds / prompt_embeds.norm(dim=-1						,				keepdim=A_     )

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

											if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																									UpperCamelCase   			=     self.learned_classifier_free_sampling_embeddings.embeddings
																									UpperCamelCase   			=     negative_prompt_embeds.unsqueeze(0     ).repeat(A_						,				1						,				1     )
																		else:
																									UpperCamelCase   			=     [''] * batch_size

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

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		UpperCamelCase   			=     negative_prompt_embeds.shape[1]
																		UpperCamelCase   			=     negative_prompt_embeds.repeat(1						,				A_						,				1     )
																		UpperCamelCase   			=     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
																		UpperCamelCase   			=     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]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		UpperCamelCase   			=     1
											elif isinstance(A_						,				A_     ):
																		UpperCamelCase   			=     len(A_     )
											else:
																		raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_     )}'''     )

											UpperCamelCase   			=     batch_size * num_images_per_prompt

											UpperCamelCase   			=     guidance_scale > 1.0

											UpperCamelCase   			=     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

											UpperCamelCase   			=     (batch_size, self.transformer.num_latent_pixels)
											if latents is None:
																		UpperCamelCase   			=     self.transformer.num_vector_embeds - 1
																		UpperCamelCase   			=     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).'''     )
																		UpperCamelCase   			=     latents.to(self.device     )

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

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

											UpperCamelCase   			=     latents

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

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

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

																		UpperCamelCase   			=     self.truncate(A_						,				A_     )

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

																		# compute the previous noisy sample x_t -> x_t-1
																		UpperCamelCase   			=     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_     )

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

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

											if output_type == "pil":
																		UpperCamelCase   			=     self.numpy_to_pil(A_     )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=A_     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		torch.FloatTensor:







											'''simple docstring'''






											UpperCamelCase			,	UpperCamelCase   			=     torch.sort(A_						,				1						,				descending=A_     )
											UpperCamelCase   			=     torch.exp(A_     )
											UpperCamelCase   			=     sorted_p_x_0.cumsum(dim=1     ) < truncation_rate

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

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

											UpperCamelCase   			=     log_p_x_0.clone()

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

											return rv









'''simple docstring'''





from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=1E-12			) -> List[Any]:
  __lowerCamelCase			:			int       			=					jnp.divide(emb_a.T			,jnp.clip(jnp.linalg.norm(_lowerCAmelCase			,axis=1			)			,a_min=_lowerCAmelCase			)			).T
  __lowerCamelCase			:			Tuple       			=					jnp.divide(emb_a.T			,jnp.clip(jnp.linalg.norm(_lowerCAmelCase			,axis=1			)			,a_min=_lowerCAmelCase			)			).T
  return jnp.matmul(_lowerCAmelCase			,norm_emb_a.T			)






class    lowerCamelCase_       (							nn.Module	):
  """simple docstring"""




  a_				   =42
  a_				   =jnp.floataa




  def 		_lowercase	(					self :      Optional[Any]  )   ->   Tuple:
    __lowerCamelCase			:			Optional[int]       			=					FlaxCLIPVisionModule(self.config.vision_config  )
    __lowerCamelCase			:			Any       			=					nn.Dense(self.config.projection_dim     ,      use_bias=_a     ,      dtype=self.dtype  )

    __lowerCamelCase			:			str       			=					self.param('concept_embeds'     ,      jax.nn.initializers.ones     ,      (17, self.config.projection_dim)  )
    __lowerCamelCase			:			str       			=					self.param(
        'special_care_embeds'     ,      jax.nn.initializers.ones     ,      (3, self.config.projection_dim)  )

    __lowerCamelCase			:			Optional[int]       			=					self.param('concept_embeds_weights'     ,      jax.nn.initializers.ones     ,      (17,)  )
    __lowerCamelCase			:			Dict       			=					self.param('special_care_embeds_weights'     ,      jax.nn.initializers.ones     ,      (3,)  )




  def __call__(					self :      Tuple     ,      _a :      Any  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					self.vision_model(_a  )[1]
    __lowerCamelCase			:			Optional[int]       			=					self.visual_projection(_a  )

    __lowerCamelCase			:			Optional[Any]       			=					jax_cosine_distance(_a     ,      self.special_care_embeds  )
    __lowerCamelCase			:			Tuple       			=					jax_cosine_distance(_a     ,      self.concept_embeds  )

    # increase this value to create a stronger `nfsw` filter
    # at the cost of increasing the possibility of filtering benign image inputs
    __lowerCamelCase			:			Dict       			=					0.0

    __lowerCamelCase			:			str       			=					special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
    __lowerCamelCase			:			Optional[int]       			=					jnp.round(_a     ,      3  )
    __lowerCamelCase			:			Dict       			=					jnp.any(special_scores > 0     ,      axis=1     ,      keepdims=_a  )
    # Use a lower threshold if an image has any special care concept
    __lowerCamelCase			:			Dict       			=					is_special_care * 0.01

    __lowerCamelCase			:			Optional[int]       			=					cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
    __lowerCamelCase			:			List[str]       			=					jnp.round(_a     ,      3  )
    __lowerCamelCase			:			List[Any]       			=					jnp.any(concept_scores > 0     ,      axis=1  )

    return has_nsfw_concepts






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =CLIPConfig
  a_				   ="""clip_input"""
  a_				   =FlaxStableDiffusionSafetyCheckerModule




  def __init__(					self :      List[str]     ,      _a :      CLIPConfig     ,      _a :      Optional[Tuple] = None     ,      _a :      int = 0     ,      _a :      jnp.dtype = jnp.floataa     ,      _a :      bool = True     ,      **_a :      List[Any]     ,      )   ->   List[Any]:
    if input_shape is None:
      __lowerCamelCase			:			str       			=					(1, 224, 224, 3)
    __lowerCamelCase			:			List[str]       			=					self.module_class(config=_a     ,      dtype=_a     ,      **_a  )
    super().__init__(_a     ,      _a     ,      input_shape=_a     ,      seed=_a     ,      dtype=_a     ,      _do_init=_do_init  )




  def 		_lowercase	(					self :      int     ,      _a :      jax.random.KeyArray     ,      _a :      Tuple     ,      _a :      FrozenDict = None  )   ->   FrozenDict:
    # init input tensor
    __lowerCamelCase			:			Optional[int]       			=					jax.random.normal(_a     ,      _a  )

    __lowerCamelCase			,__lowerCamelCase			:			Optional[Any]       			=					jax.random.split(_a  )
    __lowerCamelCase			:			int       			=					{'params': params_rng, 'dropout': dropout_rng}

    __lowerCamelCase			:			Tuple       			=					self.module.init(_a     ,      _a  )['params']

    return random_params




  def __call__(					self :      Optional[int]     ,      _a :      int     ,      _a :      dict = None     ,      )   ->   Dict:
    __lowerCamelCase			:			str       			=					jnp.transpose(_a     ,      (0, 2, 3, 1)  )

    return self.module.apply(
        {'params': params or self.params}     ,      jnp.array(_a     ,      dtype=jnp.floataa  )     ,      rngs={}     ,      )






