





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







class   _UpperCamelCase  (       __lowercase						,       __lowercase						):







					'''simple docstring'''

					@register_to_config
					def __init__(						self    :  str ,					__lowercase    :  Dict ,					__lowercase    :  Optional[Any] = None ,					__lowercase    :  List[Any] = None		):






						'''simple docstring'''







						super().__init__()

						UpperCAmelCase_								=     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"

							UpperCAmelCase_								=     torch.zeros(__A ,					__A		)
						else:
							UpperCAmelCase_								=     None

						UpperCAmelCase_								=     torch.nn.Parameter(__A		)







class   _UpperCamelCase  (       __lowercase						):







					'''simple docstring'''

					lowerCamelCase     :     VQModel
					lowerCamelCase     :     CLIPTextModel
					lowerCamelCase     :     CLIPTokenizer
					lowerCamelCase     :     TransformeraDModel
					lowerCamelCase     :     LearnedClassifierFreeSamplingEmbeddings
					lowerCamelCase     :     VQDiffusionScheduler





					def __init__(						self    :  Optional[Any] ,					__lowercase    :  Dict ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Dict ,					__lowercase    :  List[str] ,					__lowercase    :  List[Any] ,					__lowercase    :  Tuple ,					):






						'''simple docstring'''







						super().__init__()

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





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  Optional[int] ,					__lowercase    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     len(__A		) if isinstance(__A ,					__A		) else 1

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

						if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							UpperCAmelCase_								=     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}"""		)
							UpperCAmelCase_								=     text_input_ids[:, : self.tokenizer.model_max_length]
						UpperCAmelCase_								=     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
						UpperCAmelCase_								=     prompt_embeds / prompt_embeds.norm(dim=-1 ,					keepdim=__A		)

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

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

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

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

						return prompt_embeds





					@torch.no_grad()
					def __call__(						self    :  Tuple ,					__lowercase    :  Optional[int] ,					__lowercase    :  Optional[int] = 1_00 ,					__lowercase    :  Optional[int] = 5.0 ,					__lowercase    :  Optional[int] = 1.0 ,					__lowercase    :  List[Any] = 1 ,					__lowercase    :  int = None ,					__lowercase    :  int = None ,					__lowercase    :  Any = "pil" ,					__lowercase    :  Union[str, Any] = True ,					__lowercase    :  Union[str, Any] = None ,					__lowercase    :  str = 1 ,					):






						'''simple docstring'''







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

						UpperCAmelCase_								=     batch_size * num_images_per_prompt

						UpperCAmelCase_								=     guidance_scale > 1.0

						UpperCAmelCase_								=     self._encode_prompt(__A ,					__A ,					__A		)

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

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

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

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

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

						UpperCAmelCase_								=     latents

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

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

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

							UpperCAmelCase_								=     self.truncate(__A ,					__A		)

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

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

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

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

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

						if output_type == "pil":
							UpperCAmelCase_								=     self.numpy_to_pil(__A		)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=__A		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  Tuple ,					__lowercase    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     torch.sort(__A ,					1 ,					descending=__A		)
						UpperCAmelCase_								=     torch.exp(__A		)
						UpperCAmelCase_								=     sorted_p_x_0.cumsum(dim=1		) < truncation_rate

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

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

						UpperCAmelCase_								=     log_p_x_0.clone()

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

						return rv









from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def    A_(			A   ):
	return ConvertCommand(
	    args.model_type				,      args.tf_checkpoint				,      args.pytorch_dump_output				,      args.config				,      args.finetuning_task_name   )


UpperCamelCase__   : Any					  = """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						__lowercase    :  ArgumentParser		):






						'''simple docstring'''







						UpperCAmelCase_								=     parser.add_parser(
						    """convert""" ,					help="""CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.""" ,					)
						train_parser.add_argument("""--model_type""" ,					type=__lowercase ,					required=__lowercase ,					help="""Model's type."""		)
						train_parser.add_argument(
						    """--tf_checkpoint""" ,					type=__lowercase ,					required=__lowercase ,					help="""TensorFlow checkpoint path or folder."""		)
						train_parser.add_argument(
						    """--pytorch_dump_output""" ,					type=__lowercase ,					required=__lowercase ,					help="""Path to the PyTorch saved model output."""		)
						train_parser.add_argument("""--config""" ,					type=__lowercase ,					default="""""" ,					help="""Configuration file path or folder."""		)
						train_parser.add_argument(
						    """--finetuning_task_name""" ,					type=__lowercase ,					default=__lowercase ,					help="""Optional fine-tuning task name if the TF model was a finetuned model.""" ,					)
						train_parser.set_defaults(func=__lowercase		)





					def __init__(						self    :  Optional[int] ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					*__lowercase    :  Tuple ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     logging.get_logger("""transformers-cli/converting"""		)

						self._logger.info(F"""Loading model {model_type}"""		)
						UpperCAmelCase_								=     model_type
						UpperCAmelCase_								=     tf_checkpoint
						UpperCAmelCase_								=     pytorch_dump_output
						UpperCAmelCase_								=     config
						UpperCAmelCase_								=     finetuning_task_name





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						if self._model_type == "albert":
							try:
								from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "bert":
							try:
								from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "funnel":
							try:
								from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
								    convert_tf_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "t5":
							try:
								from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
							except ImportError:
								raise ImportError(__lowercase		)

							convert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "gpt":
							from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
							    convert_openai_checkpoint_to_pytorch,
							)

							convert_openai_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "transfo_xl":
							try:
								from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
								    convert_transfo_xl_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							if "ckpt" in self._tf_checkpoint.lower():
								UpperCAmelCase_								=     self._tf_checkpoint
								UpperCAmelCase_								=     """"""
							else:
								UpperCAmelCase_								=     self._tf_checkpoint
								UpperCAmelCase_								=     """"""
							convert_transfo_xl_checkpoint_to_pytorch(
							    __lowercase ,					self._config ,					self._pytorch_dump_output ,					__lowercase		)
						elif self._model_type == "gpt2":
							try:
								from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
								    convert_gpta_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						elif self._model_type == "xlnet":
							try:
								from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
								    convert_xlnet_checkpoint_to_pytorch,
								)
							except ImportError:
								raise ImportError(__lowercase		)

							convert_xlnet_checkpoint_to_pytorch(
							    self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output ,					self._finetuning_task_name		)
						elif self._model_type == "xlm":
							from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
							    convert_xlm_checkpoint_to_pytorch,
							)

							convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint ,					self._pytorch_dump_output		)
						elif self._model_type == "lxmert":
							from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
							    convert_lxmert_checkpoint_to_pytorch,
							)

							convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint ,					self._pytorch_dump_output		)
						elif self._model_type == "rembert":
							from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
							    convert_rembert_tf_checkpoint_to_pytorch,
							)

							convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint ,					self._config ,					self._pytorch_dump_output		)
						else:
							raise ValueError(
							    """--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"""		)


