'''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


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






class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase						):







     """simple docstring"""






     @register_to_config
     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None    ):
         super().__init__()

         __UpperCamelCase	:	Tuple		    =  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	:	int		    =  torch.zeros(_lowerCamelCase     ,			_lowerCamelCase    )
         else:
             __UpperCamelCase	:	int		    =  None

         __UpperCamelCase	:	int		    =  torch.nn.Parameter(_lowerCamelCase    )






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						42
     SCREAMING_SNAKE_CASE__		   =						42
     SCREAMING_SNAKE_CASE__		   =						42
     SCREAMING_SNAKE_CASE__		   =						42
     SCREAMING_SNAKE_CASE__		   =						42
     SCREAMING_SNAKE_CASE__		   =						42







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
         super().__init__()

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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  len(_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else 1

         # get prompt text embeddings
         __UpperCamelCase	:	str		    =  self.tokenizer(
             _lowerCamelCase     ,			padding='max_length'     ,			max_length=self.tokenizer.model_max_length     ,			return_tensors='pt'     ,			)
         __UpperCamelCase	:	str		    =  text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             __UpperCamelCase	:	Optional[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	:	Any		    =  text_input_ids[:, : self.tokenizer.model_max_length]
         __UpperCamelCase	:	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
         __UpperCamelCase	:	Tuple		    =  prompt_embeds / prompt_embeds.norm(dim=-1     ,			keepdim=_lowerCamelCase    )

         # duplicate text embeddings for each generation per prompt
         __UpperCamelCase	:	Any		    =  prompt_embeds.repeat_interleave(_lowerCamelCase     ,			dim=0    )

         if do_classifier_free_guidance:
             if self.learned_classifier_free_sampling_embeddings.learnable:
                 __UpperCamelCase	:	List[Any]		    =  self.learned_classifier_free_sampling_embeddings.embeddings
                 __UpperCamelCase	:	Optional[Any]		    =  negative_prompt_embeds.unsqueeze(0    ).repeat(_lowerCamelCase     ,			1     ,			1    )
             else:
                 __UpperCamelCase	:	str		    =  [''] * batch_size

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

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             __UpperCamelCase	:	List[Any]		    =  negative_prompt_embeds.shape[1]
             __UpperCamelCase	:	List[Any]		    =  negative_prompt_embeds.repeat(1     ,			_lowerCamelCase     ,			1    )
             __UpperCamelCase	:	Optional[Any]		    =  negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,			_lowerCamelCase     ,			-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	:	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     ,			):
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Dict		    =  1
         elif isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	int		    =  len(_lowerCamelCase    )
         else:
             raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_lowerCamelCase    )}"""    )

         __UpperCamelCase	:	int		    =  batch_size * num_images_per_prompt

         __UpperCamelCase	:	Optional[int]		    =  guidance_scale > 1.0

         __UpperCamelCase	:	Optional[int]		    =  self._encode_prompt(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )

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

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

         __UpperCamelCase	:	Any		    =  (batch_size, self.transformer.num_latent_pixels)
         if latents is None:
             __UpperCamelCase	:	Dict		    =  self.transformer.num_vector_embeds - 1
             __UpperCamelCase	:	Optional[Any]		    =  torch.full(_lowerCamelCase     ,			_lowerCamelCase    ).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	:	Dict		    =  latents.to(self.device    )

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

         __UpperCamelCase	:	int		    =  self.scheduler.timesteps.to(self.device    )

         __UpperCamelCase	:	Optional[int]		    =  latents

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

             # predict the un-noised image
             # model_output == `log_p_x_0`
             __UpperCamelCase	:	Optional[int]		    =  self.transformer(_lowerCamelCase     ,			encoder_hidden_states=_lowerCamelCase     ,			timestep=_lowerCamelCase    ).sample

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

             __UpperCamelCase	:	Dict		    =  self.truncate(_lowerCamelCase     ,			_lowerCamelCase    )

             # remove `log(0)`'s (`-inf`s)
             __UpperCamelCase	:	Optional[int]		    =  model_output.clamp(-7_0    )

             # compute the previous noisy sample x_t -> x_t-1
             __UpperCamelCase	:	str		    =  self.scheduler.step(_lowerCamelCase     ,			timestep=_lowerCamelCase     ,			sample=_lowerCamelCase     ,			generator=_lowerCamelCase    ).prev_sample

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

         __UpperCamelCase	:	Dict		    =  self.vqvae.config.vq_embed_dim
         __UpperCamelCase	:	Tuple		    =  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
         __UpperCamelCase	:	int		    =  self.vqvae.quantize.get_codebook_entry(_lowerCamelCase     ,			shape=_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  self.vqvae.decode(_lowerCamelCase     ,			force_not_quantize=_lowerCamelCase    ).sample

         __UpperCamelCase	:	Dict		    =  (image / 2 + 0.5).clamp(0     ,			1    )
         __UpperCamelCase	:	Optional[int]		    =  image.cpu().permute(0     ,			2     ,			3     ,			1    ).numpy()

         if output_type == "pil":
             __UpperCamelCase	:	List[str]		    =  self.numpy_to_pil(_lowerCamelCase    )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	int		    =  torch.sort(_lowerCamelCase     ,			1     ,			descending=_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  torch.exp(_lowerCamelCase    )
         __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, :]     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  torch.cat((all_true, keep_mask)     ,			dim=1    )
         __UpperCamelCase	:	Any		    =  keep_mask[:, :-1, :]

         __UpperCamelCase	:	Tuple		    =  keep_mask.gather(1     ,			indices.argsort(1    )    )

         __UpperCamelCase	:	str		    =  log_p_x_0.clone()

         __UpperCamelCase	:	List[Any]		    =  -torch.inf  # -inf = log(0)

         return rv




'''simple docstring'''
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizer
     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	List[str]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __UpperCamelCase	:	Union[str, Any]		    =  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]    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  'UNwant\u00E9d,running'
         __UpperCamelCase	:	Union[str, Any]		    =  'unwanted, running'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.tokenizer_class(self.vocab_file    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.tokenize('UNwant\u00E9d,running'    )
         self.assertListEqual(_lowerCamelCase     ,			['un', '##want', '##ed', ',', 'runn', '##ing']    )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			[7, 4, 5, 1_0, 8, 9]    )







     def lowerCAmelCase     (		self    ):
         pass



