

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



class   __lowercase    (			UpperCAmelCase_		,	UpperCAmelCase_     ):




			"""simple docstring"""







			@register_to_config
			def __init__(			self  :					Dict       ,						lowerCAmelCase__  :					bool       ,						lowerCAmelCase__  :					Optional[int] = None       ,						lowerCAmelCase__  :					Optional[int] = None):
									super().__init__()

									SCREAMING_SNAKE_CASE_:			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"

															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	torch.zeros(lowerCAmelCase__       ,						lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			Any  		=	None

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.nn.Parameter(lowerCAmelCase__)







class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							VQModel
			_UpperCAmelCase       :							CLIPTextModel
			_UpperCAmelCase       :							CLIPTokenizer
			_UpperCAmelCase       :							TransformeraDModel
			_UpperCAmelCase       :							LearnedClassifierFreeSamplingEmbeddings
			_UpperCAmelCase       :							VQDiffusionScheduler



			def __init__(			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					VQModel       ,						lowerCAmelCase__  :					CLIPTextModel       ,						lowerCAmelCase__  :					CLIPTokenizer       ,						lowerCAmelCase__  :					TransformeraDModel       ,						lowerCAmelCase__  :					VQDiffusionScheduler       ,						lowerCAmelCase__  :					LearnedClassifierFreeSamplingEmbeddings       ,						):
									super().__init__()

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Any       ,						lowerCAmelCase__  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	len(lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else 1

									# get prompt text embeddings
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.tokenizer(
									    lowerCAmelCase__       ,						padding="max_length"       ,						max_length=self.tokenizer.model_max_length       ,						return_tensors="pt"       ,						)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	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}")
															SCREAMING_SNAKE_CASE_:			int  		=	text_input_ids[:, : self.tokenizer.model_max_length]
									SCREAMING_SNAKE_CASE_:			Union[str, 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
									SCREAMING_SNAKE_CASE_:			Tuple  		=	prompt_embeds / prompt_embeds.norm(dim=-1       ,						keepdim=lowerCAmelCase__)

									# duplicate text embeddings for each generation per prompt
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	prompt_embeds.repeat_interleave(lowerCAmelCase__       ,						dim=0)

									if do_classifier_free_guidance:
															if self.learned_classifier_free_sampling_embeddings.learnable:
																					SCREAMING_SNAKE_CASE_:			int  		=	self.learned_classifier_free_sampling_embeddings.embeddings
																					SCREAMING_SNAKE_CASE_:			Optional[int]  		=	negative_prompt_embeds.unsqueeze(0).repeat(lowerCAmelCase__       ,						1       ,						1)
															else:
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	[""] * batch_size

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

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	negative_prompt_embeds.shape[1]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	negative_prompt_embeds.repeat(1       ,						lowerCAmelCase__       ,						1)
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	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
															SCREAMING_SNAKE_CASE_:			Dict  		=	torch.cat([negative_prompt_embeds, prompt_embeds])

									return prompt_embeds



			@torch.no_grad()
			def __call__(			self  :					Any       ,						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       ,						):
									if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	1
									elif isinstance(lowerCAmelCase__       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	len(lowerCAmelCase__)
									else:
															raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__)}")

									SCREAMING_SNAKE_CASE_:			Any  		=	batch_size * num_images_per_prompt

									SCREAMING_SNAKE_CASE_:			List[str]  		=	guidance_scale > 1.0

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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

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

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

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.scheduler.timesteps.to(self.device)

									SCREAMING_SNAKE_CASE_:			int  		=	latents

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

															# predict the un-noised image
															# model_output == `log_p_x_0`
															SCREAMING_SNAKE_CASE_:			Any  		=	self.transformer(lowerCAmelCase__       ,						encoder_hidden_states=lowerCAmelCase__       ,						timestep=lowerCAmelCase__).sample

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

															SCREAMING_SNAKE_CASE_:			str  		=	self.truncate(lowerCAmelCase__       ,						lowerCAmelCase__)

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

															# compute the previous noisy sample x_t -> x_t-1
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	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__)

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

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

									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:			Dict  		=	self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					torch.FloatTensor       ,						lowerCAmelCase__  :					float):
									SCREAMING_SNAKE_CASE_:			int  		=	torch.sort(lowerCAmelCase__       ,						1       ,						descending=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	torch.exp(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	sorted_p_x_0.cumsum(dim=1) < truncation_rate

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

									SCREAMING_SNAKE_CASE_:			int  		=	keep_mask.gather(1       ,						indices.argsort(1))

									SCREAMING_SNAKE_CASE_:			str  		=	log_p_x_0.clone()

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

									return rv









import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers import AutoModelForCausalLM



@require_torch
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			str  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			str  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	tokenizer.decode(greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			int  		=	TextStreamer(lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			int  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			int  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer.decode(greedy_ids[0])

									SCREAMING_SNAKE_CASE_:			int  		=	TextIteratorStreamer(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Tuple  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	""
									for new_text in streamer:
															streamer_text += new_text

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			int  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			int  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	greedy_ids[:, input_ids.shape[1] :]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.decode(new_greedy_ids[0])

									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Dict  		=	TextStreamer(lowerCAmelCase__       ,						skip_prompt=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=10       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)
									# The greedy text should be printed to stdout, except for the final "\n" in the streamer
									SCREAMING_SNAKE_CASE_:			Any  		=	cs.out[:-1]

									self.assertEqual(lowerCAmelCase__       ,						lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
									# with actual models -- the dummy models' tokenizers are not aligned with their models, and
									# `skip_special_tokens=True` has no effect on them
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained("distilgpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("distilgpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	torch.ones((1, 5)       ,						device=lowerCAmelCase__).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	TextStreamer(lowerCAmelCase__       ,						skip_special_tokens=lowerCAmelCase__)
															model.generate(lowerCAmelCase__       ,						max_new_tokens=1       ,						do_sample=lowerCAmelCase__       ,						streamer=lowerCAmelCase__)

									# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
									# re-tokenized, must only contain one token
									SCREAMING_SNAKE_CASE_:			str  		=	cs.out[:-1]  # Remove the final "\n"
									SCREAMING_SNAKE_CASE_:			Tuple  		=	tokenizer(lowerCAmelCase__       ,						return_tensors="pt")
									self.assertEqual(streamer_text_tokenized.input_ids.shape       ,						(1, 1))




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
									SCREAMING_SNAKE_CASE_:			List[str]  		=	AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Dict  		=	-1

									SCREAMING_SNAKE_CASE_:			List[str]  		=	ids_tensor((1, 5)       ,						vocab_size=model.config.vocab_size).to(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	TextIteratorStreamer(lowerCAmelCase__       ,						timeout=0.001)
									SCREAMING_SNAKE_CASE_:			Any  		=	{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Thread(target=model.generate       ,						kwargs=lowerCAmelCase__)
									thread.start()

									# The streamer will timeout after 0.001 seconds, so an exception will be raised
									with self.assertRaises(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	""
															for new_text in streamer:
																					streamer_text += new_text






