

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


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







class 					lowercase							(			lowercase_      ,    lowercase_     ):




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

											snake_case_     							=       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"

																		snake_case_     							=       torch.zeros(snake_case ,  snake_case			)
											else:
																		snake_case_     							=       None

											snake_case_     							=       torch.nn.Parameter(snake_case			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					VQModel
				__SCREAMING_SNAKE_CASE   :					CLIPTextModel
				__SCREAMING_SNAKE_CASE   :					CLIPTokenizer
				__SCREAMING_SNAKE_CASE   :					TransformeraDModel
				__SCREAMING_SNAKE_CASE   :					LearnedClassifierFreeSamplingEmbeddings
				__SCREAMING_SNAKE_CASE   :					VQDiffusionScheduler




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  ):
											super().__init__()

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




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       len(snake_case			) if isinstance(snake_case ,  snake_case			) else 1

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

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		snake_case_     							=       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}'''			)
																		snake_case_     							=       text_input_ids[:, : self.tokenizer.model_max_length]
											snake_case_     							=       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
											snake_case_     							=       prompt_embeds / prompt_embeds.norm(dim=-1 ,  keepdim=snake_case			)

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

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

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

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

											return prompt_embeds




				@torch.no_grad()
				def __call__(   self ,  snake_case ,  snake_case = 100 ,  snake_case = 5.0 ,  snake_case = 1.0 ,  snake_case = 1 ,  snake_case = None ,  snake_case = None ,  snake_case = "pil" ,  snake_case = True ,  snake_case = None ,  snake_case = 1 ,  ):
											if isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       1
											elif isinstance(snake_case ,  snake_case			):
																		snake_case_     							=       len(snake_case			)
											else:
																		raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case			)}'''			)

											snake_case_     							=       batch_size * num_images_per_prompt

											snake_case_     							=       guidance_scale > 1.0

											snake_case_     							=       self._encode_prompt(snake_case ,  snake_case ,  snake_case			)

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

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

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

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

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

											snake_case_     							=       latents

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

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

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

																		snake_case_     							=       self.truncate(snake_case ,  snake_case			)

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

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

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

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

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

											if output_type == "pil":
																		snake_case_     							=       self.numpy_to_pil(snake_case			)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case			)




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_							,       snake_case_     							=       torch.sort(snake_case ,  1 ,  descending=snake_case			)
											snake_case_     							=       torch.exp(snake_case			)
											snake_case_     							=       sorted_p_x_0.cumsum(dim=1			) < truncation_rate

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

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

											snake_case_     							=       log_p_x_0.clone()

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

											return rv







import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       10




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4]
											snake_case_     							=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       ''
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       (
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											snake_case_							,       snake_case_     							=       process_story(snake_case			)

											snake_case_     							=       [
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(snake_case ,  snake_case			)

											snake_case_     							=       ['It was the best of times.']
											self.assertEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  0			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4, 23, 23, 23]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  23			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([8, 2, 3, 4, 1, 1, 1]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  1			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       101
											snake_case_     							=       torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]			)
											snake_case_     							=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]			)

											snake_case_     							=       compute_token_type_ids(snake_case ,  snake_case			)
											np.testing.assert_array_equal(snake_case ,  snake_case			)




