





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	:	List[Any]		     =       logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				__lowerCAmelCase       (    a_						,				a_):







      @register_to_config
      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None							)   ->						List[str]:



        '''simple docstring'''




        super().__init__()

        a__	:					Optional[Any]										=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__	:					List[str]										=torch.zeros(_a		,      _a							)
        else:
          a__	:					Optional[int]										=None

        a__	:					str										=torch.nn.Parameter(_a							)







class 				__lowerCAmelCase       (    a_):
      _lowercase				:					Optional[Any]						=			42
      _lowercase				:					int						=			42
      _lowercase				:					Dict						=			42
      _lowercase				:					Tuple						=			42
      _lowercase				:					List[str]						=			42
      _lowercase				:					List[Any]						=			42







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      )   ->						Any:



        '''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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=len(_a							) if isinstance(_a		,      _a							) else 1

        # get prompt text embeddings
        a__	:					int										=self.tokenizer(
            _a		,      padding="max_length"		,      max_length=self.tokenizer.model_max_length		,      return_tensors="pt"		,      )
        a__	:					Dict										=text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          a__	:					List[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__	:					int										=text_input_ids[:, : self.tokenizer.model_max_length]
        a__	:					Optional[int]										=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__	:					Optional[int]										=prompt_embeds / prompt_embeds.norm(dim=-1		,      keepdim=_a							)

        # duplicate text embeddings for each generation per prompt
        a__	:					Dict										=prompt_embeds.repeat_interleave(_a		,      dim=0							)

        if do_classifier_free_guidance:
          if self.learned_classifier_free_sampling_embeddings.learnable:
            a__	:					str										=self.learned_classifier_free_sampling_embeddings.embeddings
            a__	:					str										=negative_prompt_embeds.unsqueeze(0							).repeat(_a		,      1		,      1							)
          else:
            a__	:					List[str]										=[""] * batch_size

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

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

        return prompt_embeds







      @torch.no_grad()
      def __call__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = 1_0_0		,      lowerCAmelCase__ = 5.0		,      lowerCAmelCase__ = 1.0		,      lowerCAmelCase__ = 1		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = "pil"		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = 1		,      )   ->						Dict:



        '''simple docstring'''




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

        a__	:					Any										=batch_size * num_images_per_prompt

        a__	:					List[str]										=guidance_scale > 1.0

        a__	:					Tuple										=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

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

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

        a__	:					List[str]										=self.scheduler.timesteps.to(self.device							)

        a__	:					Optional[int]										=latents

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

          # predict the un-noised image
          # model_output == `log_p_x_0`
          a__	:					List[Any]										=self.transformer(_a		,      encoder_hidden_states=_a		,      timestep=_a							).sample

          if do_classifier_free_guidance:
            a__	:					Optional[int]										=model_output.chunk(2							)
            a__	:					str										=model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
            model_output -= torch.logsumexp(_a		,      dim=1		,      keepdim=_a							)

          a__	:					Optional[int]										=self.truncate(_a		,      _a							)

          # remove `log(0)`'s (`-inf`s)
          a__	:					Union[str, Any]										=model_output.clamp(-7_0							)

          # compute the previous noisy sample x_t -> x_t-1
          a__	:					int										=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							)

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

        a__	:					List[Any]										=(image / 2 + 0.5).clamp(0		,      1							)
        a__	:					Dict										=image.cpu().permute(0		,      2		,      3		,      1							).numpy()

        if output_type == "pil":
          a__	:					List[str]										=self.numpy_to_pil(_a							)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=_a							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Dict										=torch.sort(_a		,      1		,      descending=_a							)
        a__	:					List[Any]										=torch.exp(_a							)
        a__	:					Union[str, Any]										=sorted_p_x_0.cumsum(dim=1							) < truncation_rate

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

        a__	:					Optional[int]										=keep_mask.gather(1		,      indices.argsort(1							)							)

        a__	:					Union[str, Any]										=log_p_x_0.clone()

        a__	:					Union[str, Any]										=-torch.inf  # -inf = log(0)

        return rv








"""simple docstring"""




import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin

@require_jieba
class 		a__						(      a_,			unittest.TestCase						):
   __lowerCAmelCase            =				CpmAntTokenizer
   __lowerCAmelCase            =				False
   def 			__magic_name__			(			self				):
          super().setUp()

          lowercase    :				List[str]       		=							[
              "<d>",
              "</d>",
              "<s>",
              "</s>",
              "</_>",
              "<unk>",
              "<pad>",
              "</n>",
              "我",
              "是",
              "C",
              "P",
              "M",
              "A",
              "n",
              "t",
          ]
          lowercase    :				Dict       		=							os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["vocab_file"]				)
          with open(self.vocab_file	,    "w"	,    encoding="utf-8"				) as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)

   @tooslow
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"				)
          lowercase    :				str       		=							"今天天气真好！"
          lowercase    :				Optional[int]       		=							["今天", "天气", "真", "好", "！"]
          lowercase    :				int       		=							tokenizer.tokenize(_a				)
          self.assertListEqual(_a	,    _a				)
          lowercase    :				Any       		=							"今天天气真好！"
          lowercase    :				int       		=							[tokenizer.bos_token] + tokens

          lowercase    :				List[str]       		=							[6, 9_802, 14_962, 2_082, 831, 244]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a				)	,    _a				)

          lowercase    :				str       		=							tokenizer.decode(_a				)
          self.assertEqual(_a	,    _a				)


