





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

class    _UpperCAmelCase							(		SCREAMING_SNAKE_CASE__			,       SCREAMING_SNAKE_CASE__     ):
						@register_to_config
						def __init__(		self      ,       a__      ,       a__ = None      ,       a__ = None       ):
									super().__init__()

									A_     :				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"

												A_     :				Union[str, Any]			      =	torch.zeros(_lowercase      ,       _lowercase       )
									else:
												A_     :				Tuple			      =	None

									A_     :				Optional[Any]			      =	torch.nn.Parameter(_lowercase       )






class    _UpperCAmelCase							(		SCREAMING_SNAKE_CASE__     ):
						a						       =							42
						a						       =							42
						a						       =							42
						a						       =							42
						a						       =							42
						a						       =							42
						def __init__(		self      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       a__      ,       ):
									super().__init__()

									self.register_modules(
									    vqvae=_lowercase      ,       transformer=_lowercase      ,       text_encoder=_lowercase      ,       tokenizer=_lowercase      ,       scheduler=_lowercase      ,       learned_classifier_free_sampling_embeddings=_lowercase      ,       )
						def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
									A_     :				Union[str, Any]			      =	len(_lowercase       ) if isinstance(_lowercase      ,       _lowercase       ) else 1

									# get prompt text embeddings
									A_     :				Dict			      =	self.tokenizer(
									    _lowercase      ,       padding="""max_length"""      ,       max_length=self.tokenizer.model_max_length      ,       return_tensors="""pt"""      ,       )
									A_     :				List[Any]			      =	text_inputs.input_ids

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

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

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

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

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												A_     :				List[Any]			      =	negative_prompt_embeds.shape[1]
												A_     :				int			      =	negative_prompt_embeds.repeat(1      ,       _lowercase      ,       1       )
												A_     :				int			      =	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
												A_     :				Tuple			      =	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(_lowercase      ,       _lowercase       ):
												A_     :				Optional[Any]			      =	1
									elif isinstance(_lowercase      ,       _lowercase       ):
												A_     :				List[str]			      =	len(_lowercase       )
									else:
												raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_lowercase       )}"""       )

									A_     :				List[str]			      =	batch_size * num_images_per_prompt

									A_     :				List[str]			      =	guidance_scale > 1.0

									A_     :				Tuple			      =	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

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

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

									A_     :				Optional[Any]			      =	self.scheduler.timesteps.to(self.device       )

									A_     :				int			      =	latents

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

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

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

												A_     :				str			      =	self.truncate(_lowercase      ,       _lowercase       )

												# remove `log(0)`'s (`-inf`s)
												A_     :				Union[str, Any]			      =	model_output.clamp(-70       )

												# compute the previous noisy sample x_t -> x_t-1
												A_     :				List[str]			      =	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       )

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

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

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

									if not return_dict:
												return (image,)

									return ImagePipelineOutput(images=_lowercase       )






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

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

									A_     :				List[Any]			      =	keep_mask.gather(1      ,       indices.argsort(1       )       )

									A_     :				int			      =	log_p_x_0.clone()

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

									return rv













# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


_lowerCAmelCase 				=     """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
      import torch

      print("""Torch version:""", torch.__version__)
      print("""Cuda available:""", torch.cuda.is_available())
      print("""Cuda version:""", torch.version.cuda)
      print("""CuDNN version:""", torch.backends.cudnn.version())
      print("""Number of GPUs available:""", torch.cuda.device_count())
      print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
      print("""Torch version:""", None)

try:
      import deepspeed

      print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
      print("""DeepSpeed version:""", None)

try:
      import tensorflow as tf

      print("""TensorFlow version:""", tf.__version__)
      print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
      print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
      print("""TensorFlow version:""", None)






