


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       __magic_name__	(     SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__):


     @register_to_config
     def __init__(      self							:     Any							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     Tuple = None			)  ->      List[str]:





      '''simple docstring'''






      super().__init__()

      UpperCamelCase__					:    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"

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

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



class       __magic_name__	(     SCREAMING_SNAKE_CASE__):
     A:		Any			=      4_2
     A:		int			=      4_2
     A:		str			=      4_2
     A:		Union[str, Any]			=      4_2
     A:		Optional[int]			=      4_2
     A:		Optional[Any]			=      4_2


     def __init__(      self							:     List[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     str							,	)  ->      Optional[Any]:





      '''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      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     int			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   len(_lowercase			) if isinstance(_lowercase							,	_lowercase			) else 1

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

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

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

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

        UpperCamelCase__					:    Tuple          =   text_input_ids.shape[-1]
        UpperCamelCase__					:    Dict          =   self.tokenizer(
            _lowercase							,	padding='''max_length'''							,	max_length=_lowercase							,	truncation=_lowercase							,	return_tensors='''pt'''							,	)
        UpperCamelCase__					:    Dict          =   self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
        # See comment for normalizing text embeddings
        UpperCamelCase__					:    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
       UpperCamelCase__					:    List[Any]          =   negative_prompt_embeds.shape[1]
       UpperCamelCase__					:    int          =   negative_prompt_embeds.repeat(1							,	_lowercase							,	1			)
       UpperCamelCase__					:    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
       UpperCamelCase__					:    Tuple          =   torch.cat([negative_prompt_embeds, prompt_embeds]			)

      return prompt_embeds


     @torch.no_grad()
     def __call__(      self							:     Tuple							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     List[Any] = 100							,	lowerCamelCase__							:     Dict = 5.0							,	lowerCamelCase__							:     Dict = 1.0							,	lowerCamelCase__							:     Optional[Any] = 1							,	lowerCamelCase__							:     Optional[Any] = None							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     List[str] = "pil"							,	lowerCamelCase__							:     Tuple = True							,	lowerCamelCase__							:     Optional[int] = None							,	lowerCamelCase__							:     List[Any] = 1							,	)  ->      Union[ImagePipelineOutput, Tuple]:





      '''simple docstring'''






      if isinstance(_lowercase							,	_lowercase			):
       UpperCamelCase__					:    Optional[Any]          =   1
      elif isinstance(_lowercase							,	_lowercase			):
       UpperCamelCase__					:    List[str]          =   len(_lowercase			)
      else:
       raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_lowercase			)}"			)

      UpperCamelCase__					:    List[str]          =   batch_size * num_images_per_prompt

      UpperCamelCase__					:    List[str]          =   guidance_scale > 1.0

      UpperCamelCase__					:    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

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

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

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

      UpperCamelCase__					:    int          =   latents

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

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

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

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

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

       # compute the previous noisy sample x_t -> x_t-1
       UpperCamelCase__					:    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			)

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

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

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

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=_lowercase			)


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     str			)  ->      torch.FloatTensor:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   torch.sort(_lowercase							,	1							,	descending=_lowercase			)
      UpperCamelCase__					:    Optional[Any]          =   torch.exp(_lowercase			)
      UpperCamelCase__					:    Optional[Any]          =   sorted_p_x_0.cumsum(dim=1			) < truncation_rate

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

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

      UpperCamelCase__					:    int          =   log_p_x_0.clone()

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

      return rv










from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 			_a  (				SCREAMING_SNAKE_CASE	:				Namespace     ):







 """simple docstring"""



 return ConvertCommand(
     args.model_type    , args.tf_checkpoint    , args.pytorch_dump_output    , args.config    , args.finetuning_task_name     )


__UpperCamelCase       :       List[Any]						       =				"\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"






class       __magic_name__	(     __lowerCAmelCase):


     @staticmethod
     def      UpperCAmelCase__	(      lowerCamelCase__							:     ArgumentParser			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   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=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''Model\'s type.'''			)
      train_parser.add_argument(
          '''--tf_checkpoint'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''TensorFlow checkpoint path or folder.'''			)
      train_parser.add_argument(
          '''--pytorch_dump_output'''							,	type=lowerCamelCase__							,	required=lowerCamelCase__							,	help='''Path to the PyTorch saved model output.'''			)
      train_parser.add_argument('''--config'''							,	type=lowerCamelCase__							,	default=''''''							,	help='''Configuration file path or folder.'''			)
      train_parser.add_argument(
          '''--finetuning_task_name'''							,	type=lowerCamelCase__							,	default=lowerCamelCase__							,	help='''Optional fine-tuning task name if the TF model was a finetuned model.'''							,	)
      train_parser.set_defaults(func=lowerCamelCase__			)


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	*lowerCamelCase__							:     Optional[int]							,	)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   logging.get_logger('''transformers-cli/converting'''			)

      self._logger.info(F"Loading model {model_type}"			)
      UpperCamelCase__					:    List[str]          =   model_type
      UpperCamelCase__					:    Optional[int]          =   tf_checkpoint
      UpperCamelCase__					:    List[Any]          =   pytorch_dump_output
      UpperCamelCase__					:    List[Any]          =   config
      UpperCamelCase__					:    Any          =   finetuning_task_name


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       if "ckpt" in self._tf_checkpoint.lower():
        UpperCamelCase__					:    str          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       else:
        UpperCamelCase__					:    Any          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       convert_transfo_xl_checkpoint_to_pytorch(
           lowerCamelCase__							,	self._config							,	self._pytorch_dump_output							,	lowerCamelCase__			)
      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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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]'''			)


