



"""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


a_							=    logging.get_logger(__name__)  # pylint: disable=invalid-name







class 					A_(snake_case_   ,   snake_case_      ):







			"""simple docstring"""



			@register_to_config
			def __init__(	self				,   A				,   A = None				,   A = None							):
						super().__init__()

						_lowerCamelCase					:					List[Any]	       =    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"

									_lowerCamelCase					:					int	       =    torch.zeros(A_				,   A_							)
						else:
									_lowerCamelCase					:					int	       =    None

						_lowerCamelCase					:					str	       =    torch.nn.Parameter(A_							)







class 					A_(snake_case_      ):







			"""simple docstring"""



			a_       :			int												=			42
			a_       :			str												=			42
			a_       :			int												=			42
			a_       :			int												=			42
			a_       :			Optional[Any]												=			42
			a_       :			int												=			42
			def __init__(	self				,   A				,   A				,   A				,   A				,   A				,   A				,   ):
						super().__init__()

						self.register_modules(
						    vqvae=A_				,   transformer=A_				,   text_encoder=A_				,   tokenizer=A_				,   scheduler=A_				,   learned_classifier_free_sampling_embeddings=A_				,   )
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					Any	       =    len(A_							) if isinstance(A_				,   A_							) else 1

						# get prompt text embeddings
						_lowerCamelCase					:					Optional[int]	       =    self.tokenizer(
						    A_				,   padding='max_length'				,   max_length=self.tokenizer.model_max_length				,   return_tensors='pt'				,   )
						_lowerCamelCase					:					Any	       =    text_inputs.input_ids

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									_lowerCamelCase					:					Optional[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}"							)
									_lowerCamelCase					:					List[Any]	       =    text_input_ids[:, : self.tokenizer.model_max_length]
						_lowerCamelCase					:					Dict	       =    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
						_lowerCamelCase					:					Optional[int]	       =    prompt_embeds / prompt_embeds.norm(dim=-1				,   keepdim=A_							)

						# duplicate text embeddings for each generation per prompt
						_lowerCamelCase					:					List[str]	       =    prompt_embeds.repeat_interleave(A_				,   dim=0							)

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

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

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									_lowerCamelCase					:					List[str]	       =    negative_prompt_embeds.shape[1]
									_lowerCamelCase					:					Any	       =    negative_prompt_embeds.repeat(1				,   A_				,   1							)
									_lowerCamelCase					:					Any	       =    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
									_lowerCamelCase					:					Optional[int]	       =    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				,   ):






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

						_lowerCamelCase					:					Optional[Any]	       =    batch_size * num_images_per_prompt

						_lowerCamelCase					:					List[str]	       =    guidance_scale > 1.0

						_lowerCamelCase					:					Tuple	       =    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

						_lowerCamelCase					:					List[Any]	       =    (batch_size, self.transformer.num_latent_pixels)
						if latents is None:
									_lowerCamelCase					:					Optional[int]	       =    self.transformer.num_vector_embeds - 1
									_lowerCamelCase					:					Dict	       =    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)."							)
									_lowerCamelCase					:					str	       =    latents.to(self.device							)

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

						_lowerCamelCase					:					Optional[int]	       =    self.scheduler.timesteps.to(self.device							)

						_lowerCamelCase					:					Optional[Any]	       =    latents

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

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

									if do_classifier_free_guidance:
												_lowerCamelCase       ,							_lowerCamelCase					:					List[Any]	       =    model_output.chunk(2							)
												_lowerCamelCase					:					List[Any]	       =    model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
												model_output -= torch.logsumexp(A_				,   dim=1				,   keepdim=A_							)

									_lowerCamelCase					:					List[str]	       =    self.truncate(A_				,   A_							)

									# remove `log(0)`'s (`-inf`s)
									_lowerCamelCase					:					List[Any]	       =    model_output.clamp(-70							)

									# compute the previous noisy sample x_t -> x_t-1
									_lowerCamelCase					:					Union[str, Any]	       =    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_							)

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

						_lowerCamelCase					:					str	       =    (image / 2 + 0.5).clamp(0				,   1							)
						_lowerCamelCase					:					List[Any]	       =    image.cpu().permute(0				,   2				,   3				,   1							).numpy()

						if output_type == "pil":
									_lowerCamelCase					:					List[str]	       =    self.numpy_to_pil(A_							)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=A_							)

			def    _lowerCAmelCase		(	self				,   A				,   A							):
						_lowerCamelCase       ,							_lowerCamelCase					:					Any	       =    torch.sort(A_				,   1				,   descending=A_							)
						_lowerCamelCase					:					List[Any]	       =    torch.exp(A_							)
						_lowerCamelCase					:					List[Any]	       =    sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

						_lowerCamelCase					:					Optional[Any]	       =    keep_mask.gather(1				,   indices.argsort(1							)							)

						_lowerCamelCase					:					Dict	       =    log_p_x_0.clone()

						_lowerCamelCase					:					Optional[int]	       =    -torch.inf  # -inf = log(0)

						return rv







'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[int]           = {
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""perceiver"""







				def __init__(       self						,				A_=256						,				A_=1280						,				A_=768						,				A_=1						,				A_=26						,				A_=8						,				A_=8						,				A_=None						,				A_=None						,				A_="kv"						,				A_=1						,				A_=1						,				A_="gelu"						,				A_=0.1						,				A_=0.02						,				A_=1e-12						,				A_=True						,				A_=262						,				A_=2048						,				A_=56						,				A_=[368, 496]						,				A_=16						,				A_=1920						,				A_=16						,				A_=[1, 16, 224, 224]						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     num_latents
											UpperCamelCase   			=     d_latents
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_blocks
											UpperCamelCase   			=     num_self_attends_per_block
											UpperCamelCase   			=     num_self_attention_heads
											UpperCamelCase   			=     num_cross_attention_heads
											UpperCamelCase   			=     qk_channels
											UpperCamelCase   			=     v_channels
											UpperCamelCase   			=     cross_attention_shape_for_attention
											UpperCamelCase   			=     self_attention_widening_factor
											UpperCamelCase   			=     cross_attention_widening_factor
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     use_query_residual
											# masked language modeling attributes
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     max_position_embeddings
											# image classification attributes
											UpperCamelCase   			=     image_size
											# flow attributes
											UpperCamelCase   			=     train_size
											# multimodal autoencoding attributes
											UpperCamelCase   			=     num_frames
											UpperCamelCase   			=     audio_samples_per_frame
											UpperCamelCase   			=     samples_per_patch
											UpperCamelCase   			=     output_shape


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@property
				def 						UpperCAmelCase_    (       self     )->		Mapping[str, Mapping[int, str]]:







											'''simple docstring'''






											if self.task == "multiple-choice":
																		UpperCamelCase   			=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		UpperCamelCase   			=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('inputs', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]     )







				@property
				def 						UpperCAmelCase_    (       self     )->		float:







											'''simple docstring'''






											return 1e-4







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = -1						,				A_ = -1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 40						,				A_ = 40						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_batch						,				num_token_to_add=0     )
																		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																		UpperCamelCase   			=     preprocessor.num_special_tokens_to_add(A_     )
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_sequence						,				num_token_to_add=A_     )
																		# Generate dummy inputs according to compute batch and sequence
																		UpperCamelCase   			=     [' '.join(['a']     ) * seq_length] * batch_size
																		UpperCamelCase   			=     dict(preprocessor(A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('input_ids'     )
																		return inputs
											elif isinstance(A_						,				A_     ) and preprocessor.model_input_names[0] == "pixel_values":
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(A_						,				fixed_dimension=OnnxConfig.default_fixed_batch     )
																		UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
																		UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('pixel_values'     )
																		return inputs
											else:
																		raise ValueError(
																		    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'     )




