



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

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_       ):


					'''simple docstring'''





					@register_to_config
					def __init__(	self							:  str,  lowerCamelCase							:  bool,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[int] = None					)-> Any:
								super().__init__()

								lowerCamelCase__		:		Optional[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"

											lowerCamelCase__		:		Tuple		     =torch.zeros(lowerCamelCase,  lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[str]		     =None

								lowerCamelCase__		:		str		     =torch.nn.Parameter(lowerCamelCase					)





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							4_2
					_a             =							4_2
					_a             =							4_2
					_a             =							4_2
					_a             =							4_2
					_a             =							4_2



					def __init__(	self							:  List[str],  lowerCamelCase							:  VQModel,  lowerCamelCase							:  CLIPTextModel,  lowerCamelCase							:  CLIPTokenizer,  lowerCamelCase							:  TransformeraDModel,  lowerCamelCase							:  VQDiffusionScheduler,  lowerCamelCase							:  LearnedClassifierFreeSamplingEmbeddings,  )-> List[str]:
								super().__init__()

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



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  List[Any]					)-> int:
								lowerCamelCase__		:		Optional[int]		     =len(lowerCamelCase					) if isinstance(lowerCamelCase,  lowerCamelCase					) else 1

								# get prompt text embeddings
								lowerCamelCase__		:		Tuple		     =self.tokenizer(
								    lowerCamelCase,  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__		:		List[Any]		     =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[str]		     =text_input_ids[:, : self.tokenizer.model_max_length]
								lowerCamelCase__		:		Tuple		     =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__		:		Any		     =prompt_embeds / prompt_embeds.norm(dim=-1,  keepdim=lowerCamelCase					)

								# duplicate text embeddings for each generation per prompt
								lowerCamelCase__		:		Dict		     =prompt_embeds.repeat_interleave(lowerCamelCase,  dim=0					)

								if do_classifier_free_guidance:
											if self.learned_classifier_free_sampling_embeddings.learnable:
														lowerCamelCase__		:		Dict		     =self.learned_classifier_free_sampling_embeddings.embeddings
														lowerCamelCase__		:		Union[str, Any]		     =negative_prompt_embeds.unsqueeze(0					).repeat(lowerCamelCase,  1,  1					)
											else:
														lowerCamelCase__		:		Optional[Any]		     =[''''''] * batch_size

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

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											lowerCamelCase__		:		int		     =negative_prompt_embeds.shape[1]
											lowerCamelCase__		:		Optional[Any]		     =negative_prompt_embeds.repeat(1,  lowerCamelCase,  1					)
											lowerCamelCase__		:		int		     =negative_prompt_embeds.view(batch_size * num_images_per_prompt,  lowerCamelCase,  -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__		:		Union[str, Any]		     =torch.cat([negative_prompt_embeds, prompt_embeds]					)

								return prompt_embeds



					@torch.no_grad()
					def __call__(	self							:  str,  lowerCamelCase							:  Union[str, List[str]],  lowerCamelCase							:  int = 100,  lowerCamelCase							:  float = 5.0,  lowerCamelCase							:  float = 1.0,  lowerCamelCase							:  int = 1,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[torch.FloatTensor] = None,  lowerCamelCase							:  Optional[str] = "pil",  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None,  lowerCamelCase							:  int = 1,  )-> Union[ImagePipelineOutput, Tuple]:
								if isinstance(lowerCamelCase,  lowerCamelCase					):
											lowerCamelCase__		:		Union[str, Any]		     =1
								elif isinstance(lowerCamelCase,  lowerCamelCase					):
											lowerCamelCase__		:		List[Any]		     =len(lowerCamelCase					)
								else:
											raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase					)}'''					)

								lowerCamelCase__		:		Tuple		     =batch_size * num_images_per_prompt

								lowerCamelCase__		:		Optional[Any]		     =guidance_scale > 1.0

								lowerCamelCase__		:		Dict		     =self._encode_prompt(lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

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

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

								lowerCamelCase__		:		Any		     =(batch_size, self.transformer.num_latent_pixels)
								if latents is None:
											lowerCamelCase__		:		Union[str, Any]		     =self.transformer.num_vector_embeds - 1
											lowerCamelCase__		:		Optional[Any]		     =torch.full(lowerCamelCase,  lowerCamelCase					).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__		:		List[Any]		     =latents.to(self.device					)

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

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

								lowerCamelCase__		:		int		     =latents

								for i, t in enumerate(self.progress_bar(lowerCamelCase					)					):
											# 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[int]		     =self.transformer(lowerCamelCase,  encoder_hidden_states=lowerCamelCase,  timestep=lowerCamelCase					).sample

											if do_classifier_free_guidance:
														lowerCamelCase__		:		str		     =model_output.chunk(2					)
														lowerCamelCase__		:		Any		     =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
														model_output -= torch.logsumexp(lowerCamelCase,  dim=1,  keepdim=lowerCamelCase					)

											lowerCamelCase__		:		str		     =self.truncate(lowerCamelCase,  lowerCamelCase					)

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

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

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

								lowerCamelCase__		:		Any		     =self.vqvae.config.vq_embed_dim
								lowerCamelCase__		:		int		     =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
								lowerCamelCase__		:		Tuple		     =self.vqvae.quantize.get_codebook_entry(lowerCamelCase,  shape=lowerCamelCase					)
								lowerCamelCase__		:		str		     =self.vqvae.decode(lowerCamelCase,  force_not_quantize=lowerCamelCase					).sample

								lowerCamelCase__		:		Optional[Any]		     =(image / 2 + 0.5).clamp(0,  1					)
								lowerCamelCase__		:		Dict		     =image.cpu().permute(0,  2,  3,  1					).numpy()

								if output_type == "pil":
											lowerCamelCase__		:		Dict		     =self.numpy_to_pil(lowerCamelCase					)

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=lowerCamelCase					)




					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  torch.FloatTensor,  lowerCamelCase							:  float					)-> torch.FloatTensor:
								lowerCamelCase__		:		Optional[Any]		     =torch.sort(lowerCamelCase,  1,  descending=lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =torch.exp(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =sorted_p_x_0.cumsum(dim=1					) < truncation_rate

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

								lowerCamelCase__		:		List[str]		     =keep_mask.gather(1,  indices.argsort(1					)					)

								lowerCamelCase__		:		Optional[int]		     =log_p_x_0.clone()

								lowerCamelCase__		:		Union[str, Any]		     =-torch.inf  # -inf = log(0)

								return rv









"""simple docstring"""






import numpy as np
from PIL import Image






def 					snake_case__       (		__lowerCamelCase    :   np.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =np.array(__lowerCamelCase     )
			if arr.shape[0] != arr.shape[1]:
						raise ValueError('''The input array is not a square matrix'''     )
			lowerCamelCase__		:		int		     =0
			lowerCamelCase__		:		int		     =0
			lowerCamelCase__		:		Optional[int]		     =0
			lowerCamelCase__		:		List[Any]		     =0

			# compute the shape of the output matrix
			lowerCamelCase__		:		Union[str, Any]		     =(arr.shape[0] - size) // stride + 1
			# initialize the output matrix with zeros of shape maxpool_shape
			lowerCamelCase__		:		Union[str, Any]		     =np.zeros((maxpool_shape, maxpool_shape)     )

			while i < arr.shape[0]:
						if i + size > arr.shape[0]:
									# if the end of the matrix is reached, break
									break
						while j < arr.shape[1]:
									# if the end of the matrix is reached, break
									if j + size > arr.shape[1]:
												break
									# compute the maximum of the pooling matrix
									lowerCamelCase__		:		str		     =np.max(arr[i : i + size, j : j + size]     )
									# shift the pooling matrix by stride of column pixels
									j += stride
									mat_j += 1

						# shift the pooling matrix by stride of row pixels
						i += stride
						mat_i += 1

						# reset the column index to 0
						lowerCamelCase__		:		Optional[int]		     =0
						lowerCamelCase__		:		Optional[int]		     =0

			return updated_arr






def 					snake_case__       (		__lowerCamelCase    :   np.ndarray							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =np.array(__lowerCamelCase     )
			if arr.shape[0] != arr.shape[1]:
						raise ValueError('''The input array is not a square matrix'''     )
			lowerCamelCase__		:		str		     =0
			lowerCamelCase__		:		List[Any]		     =0
			lowerCamelCase__		:		Optional[int]		     =0
			lowerCamelCase__		:		List[Any]		     =0

			# compute the shape of the output matrix
			lowerCamelCase__		:		Dict		     =(arr.shape[0] - size) // stride + 1
			# initialize the output matrix with zeros of shape avgpool_shape
			lowerCamelCase__		:		Optional[Any]		     =np.zeros((avgpool_shape, avgpool_shape)     )

			while i < arr.shape[0]:
						# if the end of the matrix is reached, break
						if i + size > arr.shape[0]:
									break
						while j < arr.shape[1]:
									# if the end of the matrix is reached, break
									if j + size > arr.shape[1]:
												break
									# compute the average of the pooling matrix
									lowerCamelCase__		:		Optional[int]		     =int(np.average(arr[i : i + size, j : j + size]     )     )
									# shift the pooling matrix by stride of column pixels
									j += stride
									mat_j += 1

						# shift the pooling matrix by stride of row pixels
						i += stride
						mat_i += 1
						# reset the column index to 0
						lowerCamelCase__		:		Optional[Any]		     =0
						lowerCamelCase__		:		int		     =0

			return updated_arr


# Main Function
if __name__ == "__main__":
		from doctest import testmod

		testmod(name="avgpooling", verbose=True)

		# Loading the image
		_lowercase			:			int										=						Image.open("path_to_image")

		# Converting the image to numpy array and maxpooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

		# Converting the image to numpy array and averagepooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()




