


"""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


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







class _lowerCAmelCase				(      snake_case_    ,							snake_case_	):







   @register_to_config
   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						)	->		Union[str, Any]:

       '''simple docstring'''




       super().__init__()

       snake_case    :      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"

           snake_case    :      Optional[int]      					=    torch.zeros(UpperCamelCase__						,	UpperCamelCase__						)
       else:
           snake_case    :      str      					=    None

       snake_case    :      str      					=    torch.nn.Parameter(UpperCamelCase__						)







class _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       VQModel
   __UpperCAmelCase :       CLIPTextModel
   __UpperCAmelCase :       CLIPTokenizer
   __UpperCAmelCase :       TransformeraDModel
   __UpperCAmelCase :       LearnedClassifierFreeSamplingEmbeddings
   __UpperCAmelCase :       VQDiffusionScheduler







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

       '''simple docstring'''




       super().__init__()

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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Dict:

       '''simple docstring'''




       snake_case    :      Dict      					=    len(UpperCamelCase__						) if isinstance(UpperCamelCase__						,	UpperCamelCase__						) else 1

       # get prompt text embeddings
       snake_case    :      Optional[int]      					=    self.tokenizer(
           UpperCamelCase__						,	padding="max_length"						,	max_length=self.tokenizer.model_max_length						,	return_tensors="pt"						,	)
       snake_case    :      str      					=    text_inputs.input_ids

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

       # duplicate text embeddings for each generation per prompt
       snake_case    :      List[str]      					=    prompt_embeds.repeat_interleave(UpperCamelCase__						,	dim=0						)

       if do_classifier_free_guidance:
           if self.learned_classifier_free_sampling_embeddings.learnable:
               snake_case    :      Dict      					=    self.learned_classifier_free_sampling_embeddings.embeddings
               snake_case    :      List[Any]      					=    negative_prompt_embeds.unsqueeze(0						).repeat(UpperCamelCase__						,	1						,	1						)
           else:
               snake_case    :      Any      					=    [""] * batch_size

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

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           snake_case    :      str      					=    negative_prompt_embeds.shape[1]
           snake_case    :      Union[str, Any]      					=    negative_prompt_embeds.repeat(1						,	UpperCamelCase__						,	1						)
           snake_case    :      Dict      					=    negative_prompt_embeds.view(batch_size * num_images_per_prompt						,	UpperCamelCase__						,	-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
           snake_case    :      Dict      					=    torch.cat([negative_prompt_embeds, prompt_embeds]						)

       return prompt_embeds







   @torch.no_grad()
   def __call__(	self						,	UpperCamelCase__						,	UpperCamelCase__ = 100						,	UpperCamelCase__ = 5.0						,	UpperCamelCase__ = 1.0						,	UpperCamelCase__ = 1						,	UpperCamelCase__ = None						,	UpperCamelCase__ = None						,	UpperCamelCase__ = "pil"						,	UpperCamelCase__ = True						,	UpperCamelCase__ = None						,	UpperCamelCase__ = 1						,	)	->		Union[ImagePipelineOutput, Tuple]:

       '''simple docstring'''




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

       snake_case    :      Dict      					=    batch_size * num_images_per_prompt

       snake_case    :      Optional[Any]      					=    guidance_scale > 1.0

       snake_case    :      Any      					=    self._encode_prompt(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)

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

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

       snake_case    :      List[str]      					=    (batch_size, self.transformer.num_latent_pixels)
       if latents is None:
           snake_case    :      Any      					=    self.transformer.num_vector_embeds - 1
           snake_case    :      int      					=    torch.full(UpperCamelCase__						,	UpperCamelCase__						).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).'						)
           snake_case    :      List[Any]      					=    latents.to(self.device						)

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

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

       snake_case    :      List[Any]      					=    latents

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

           # predict the un-noised image
           # model_output == `log_p_x_0`
           snake_case    :      Union[str, Any]      					=    self.transformer(UpperCamelCase__						,	encoder_hidden_states=UpperCamelCase__						,	timestep=UpperCamelCase__						).sample

           if do_classifier_free_guidance:
               snake_case					,snake_case    :      Union[str, Any]      					=    model_output.chunk(2						)
               snake_case    :      Tuple      					=    model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
               model_output -= torch.logsumexp(UpperCamelCase__						,	dim=1						,	keepdim=UpperCamelCase__						)

           snake_case    :      int      					=    self.truncate(UpperCamelCase__						,	UpperCamelCase__						)

           # remove `log(0)`'s (`-inf`s)
           snake_case    :      List[Any]      					=    model_output.clamp(-70						)

           # compute the previous noisy sample x_t -> x_t-1
           snake_case    :      List[str]      					=    self.scheduler.step(UpperCamelCase__						,	timestep=UpperCamelCase__						,	sample=UpperCamelCase__						,	generator=UpperCamelCase__						).prev_sample

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

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

       snake_case    :      Dict      					=    (image / 2 + 0.5).clamp(0						,	1						)
       snake_case    :      Tuple      					=    image.cpu().permute(0						,	2						,	3						,	1						).numpy()

       if output_type == "pil":
           snake_case    :      Optional[int]      					=    self.numpy_to_pil(UpperCamelCase__						)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						)	->		torch.FloatTensor:

       '''simple docstring'''




       snake_case					,snake_case    :      Tuple      					=    torch.sort(UpperCamelCase__						,	1						,	descending=UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    torch.exp(UpperCamelCase__						)
       snake_case    :      str      					=    sorted_p_x_0.cumsum(dim=1						) < truncation_rate

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

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

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

       snake_case    :      Tuple      					=    -torch.inf  # -inf = log(0)

       return rv









"""simple docstring"""



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       str							  =  DebertaTokenizer
   __UpperCAmelCase :       Optional[int]							  =  True
   __UpperCAmelCase :       int							  =  DebertaTokenizerFast







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       snake_case    :      Union[str, Any]      					=    [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "[UNK]",
       ]
       snake_case    :      str      					=    dict(zip(UpperCamelCase__						,	range(len(UpperCamelCase__						)						)						)						)
       snake_case    :      int      					=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       snake_case    :      Optional[Any]      					=    {"unk_token": "[UNK]"}

       snake_case    :      Union[str, Any]      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]						)
       snake_case    :      Dict      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["merges_file"]						)
       with open(self.vocab_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)
       with open(self.merges_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write("\n".join(UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    "lower newer"
       snake_case    :      Any      					=    "lower newer"
       return input_text, output_text







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    self.get_tokenizer()
       snake_case    :      str      					=    "lower newer"
       snake_case    :      Dict      					=    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       snake_case    :      Optional[Any]      					=    tokenizer.tokenize(UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)

       snake_case    :      List[Any]      					=    tokens + [tokenizer.unk_token]
       snake_case    :      Dict      					=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__						)						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_tokenizer()
       snake_case    :      Dict      					=    tokenizer("Hello"						,	"World"						)
       snake_case    :      str      					=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["token_type_ids"]						,	UpperCamelCase__						)







   @slow
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.tokenizer_class.from_pretrained("microsoft/deberta-base"						)

       snake_case    :      Dict      					=    tokenizer.encode("sequence builders"						,	add_special_tokens=UpperCamelCase__						)
       snake_case    :      Tuple      					=    tokenizer.encode("multi-sequence build"						,	add_special_tokens=UpperCamelCase__						)

       snake_case    :      List[str]      					=    tokenizer.encode(
           "sequence builders"						,	add_special_tokens=UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    tokenizer.encode(
           "sequence builders"						,	"multi-sequence build"						,	add_special_tokens=UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)

       snake_case    :      int      					=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase__						)
       snake_case    :      int      					=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase__						,	UpperCamelCase__						)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode







   @slow
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.tokenizer_class]
       if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

       for tokenizer_class in tokenizer_classes:
           snake_case    :      str      					=    tokenizer_class.from_pretrained("microsoft/deberta-base"						)

           snake_case    :      Union[str, Any]      					=    [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           snake_case    :      Optional[int]      					=    tokenizer(UpperCamelCase__						,	padding=UpperCamelCase__						)
           snake_case    :      Optional[int]      					=    [tokenizer.decode(UpperCamelCase__						,	skip_special_tokens=UpperCamelCase__						) for seq in encoding["input_ids"]]

           # fmt: off
           snake_case    :      Dict      					=    {
               "input_ids": [
                   [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
               ],
               "token_type_ids": [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               "attention_mask": [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           snake_case    :      int      					=    [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           self.assertDictEqual(encoding.data						,	UpperCamelCase__						)

           for expected, decoded in zip(UpperCamelCase__						,	UpperCamelCase__						):
               self.assertEqual(UpperCamelCase__						,	UpperCamelCase__						)





