
'''simple docstring'''



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_lowercase    =		subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
_lowercase    =		(
    subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()
)

_lowercase    =		"""|""".join(sys.argv[1:])
_lowercase    =		re.compile(RF"""^({joined_dirs}).*?\.py$""")

_lowercase    =		[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")


'''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


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




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@register_to_config
						def __init__( self   ,       _lowercase   ,       _lowercase = None   ,       _lowercase = None				):

												"""simple docstring"""



												super().__init__()

												_lowerCAmelCase  						=			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  						=			torch.zeros(_lowercase   ,       _lowercase				)
												else:
																		_lowerCAmelCase  						=			None

												_lowerCAmelCase  						=			torch.nn.Parameter(_lowercase				)






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				VQModel
						_lowercase		:				CLIPTextModel
						_lowercase		:				CLIPTokenizer
						_lowercase		:				TransformeraDModel
						_lowercase		:				LearnedClassifierFreeSamplingEmbeddings
						_lowercase		:				VQDiffusionScheduler




						def __init__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       ):

												"""simple docstring"""



												super().__init__()

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




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			len(_lowercase				) if isinstance(_lowercase   ,       _lowercase				) else 1

												# get prompt text embeddings
												_lowerCAmelCase  						=			self.tokenizer(
												    _lowercase   ,       padding="""max_length"""   ,       max_length=self.tokenizer.model_max_length   ,       return_tensors="""pt"""   ,       )
												_lowerCAmelCase  						=			text_inputs.input_ids

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

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

												if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																								_lowerCAmelCase  						=			self.learned_classifier_free_sampling_embeddings.embeddings
																								_lowerCAmelCase  						=			negative_prompt_embeds.unsqueeze(0				).repeat(_lowercase   ,       1   ,       1				)
																		else:
																								_lowerCAmelCase  						=			[""""""] * batch_size

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

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

												return prompt_embeds




						@torch.no_grad()
						def __call__( self   ,       _lowercase   ,       _lowercase = 100   ,       _lowercase = 5.0   ,       _lowercase = 1.0   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = 1   ,       ):

												"""simple docstring"""



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

												_lowerCAmelCase  						=			batch_size * num_images_per_prompt

												_lowerCAmelCase  						=			guidance_scale > 1.0

												_lowerCAmelCase  						=			self._encode_prompt(_lowercase   ,       _lowercase   ,       _lowercase				)

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

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

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

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

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

												_lowerCAmelCase  						=			latents

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

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

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

																		_lowerCAmelCase  						=			self.truncate(_lowercase   ,       _lowercase				)

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

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

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

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

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

												if output_type == "pil":
																		_lowerCAmelCase  						=			self.numpy_to_pil(_lowercase				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			torch.sort(_lowercase   ,       1   ,       descending=_lowercase				)
												_lowerCAmelCase  						=			torch.exp(_lowercase				)
												_lowerCAmelCase  						=			sorted_p_x_0.cumsum(dim=1				) < truncation_rate

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

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

												_lowerCAmelCase  						=			log_p_x_0.clone()

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

												return rv
