






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



class 				__UpperCAmelCase		(_UpperCAmelCase	,_UpperCAmelCase   ):







			@register_to_config
			def __init__(     self:  List[str]				,     UpperCAmelCase_:  bool				,     UpperCAmelCase_:  Optional[int] = None				,     UpperCAmelCase_:  Optional[int] = None					):







									'''simple docstring'''




									super().__init__()

									_SCREAMING_SNAKE_CASE     				=  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"

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

									_SCREAMING_SNAKE_CASE     				=  torch.nn.Parameter(UpperCAmelCase_					)



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				VQModel
			__snake_case    :				CLIPTextModel
			__snake_case    :				CLIPTokenizer
			__snake_case    :				TransformeraDModel
			__snake_case    :				LearnedClassifierFreeSamplingEmbeddings
			__snake_case    :				VQDiffusionScheduler







			def __init__(     self:  List[Any]				,     UpperCAmelCase_:  VQModel				,     UpperCAmelCase_:  CLIPTextModel				,     UpperCAmelCase_:  CLIPTokenizer				,     UpperCAmelCase_:  TransformeraDModel				,     UpperCAmelCase_:  VQDiffusionScheduler				,     UpperCAmelCase_:  LearnedClassifierFreeSamplingEmbeddings				,     ):







									'''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 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Tuple				,     UpperCAmelCase_:  Tuple				,     UpperCAmelCase_:  Tuple					):







									'''simple docstring'''




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

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

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

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

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

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

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

									return prompt_embeds







			@torch.no_grad()
			def __call__(     self:  int				,     UpperCAmelCase_:  Union[str, List[str]]				,     UpperCAmelCase_:  int = 100				,     UpperCAmelCase_:  float = 5.0				,     UpperCAmelCase_:  float = 1.0				,     UpperCAmelCase_:  int = 1				,     UpperCAmelCase_:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,     UpperCAmelCase_:  Optional[torch.FloatTensor] = None				,     UpperCAmelCase_:  Optional[str] = "pil"				,     UpperCAmelCase_:  bool = True				,     UpperCAmelCase_:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,     UpperCAmelCase_:  int = 1				,     ):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE     				=  batch_size * num_images_per_prompt

									_SCREAMING_SNAKE_CASE     				=  guidance_scale > 1.0

									_SCREAMING_SNAKE_CASE     				=  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

									_SCREAMING_SNAKE_CASE     				=  (batch_size, self.transformer.num_latent_pixels)
									if latents is None:
															_SCREAMING_SNAKE_CASE     				=  self.transformer.num_vector_embeds - 1
															_SCREAMING_SNAKE_CASE     				=  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).'					)
															_SCREAMING_SNAKE_CASE     				=  latents.to(self.device					)

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

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

									_SCREAMING_SNAKE_CASE     				=  latents

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

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

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

															_SCREAMING_SNAKE_CASE     				=  self.truncate(UpperCAmelCase_				,     UpperCAmelCase_					)

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

															# compute the previous noisy sample x_t -> x_t-1
															_SCREAMING_SNAKE_CASE     				=  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_					)

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

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

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

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  torch.FloatTensor				,     UpperCAmelCase_:  float					):







									'''simple docstring'''




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

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

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

									_SCREAMING_SNAKE_CASE     				=  log_p_x_0.clone()

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

									return rv









import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				Optional[Any]									=							OpenAIGPTTokenizer
			__snake_case    :				Dict									=							OpenAIGPTTokenizerFast
			__snake_case    :				Optional[Any]									=							True
			__snake_case    :				Dict									=							False







			def 							UpperCamelCase				(     self:  Union[str, Any]					):







									'''simple docstring'''




									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_SCREAMING_SNAKE_CASE     				=  [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """w</w>""",
									    """r</w>""",
									    """t</w>""",
									    """lo""",
									    """low""",
									    """er</w>""",
									    """low</w>""",
									    """lowest</w>""",
									    """newer</w>""",
									    """wider</w>""",
									    """<unk>""",
									]
									_SCREAMING_SNAKE_CASE     				=  dict(zip(UpperCAmelCase_				,     range(len(UpperCAmelCase_					)					)					)					)
									_SCREAMING_SNAKE_CASE     				=  ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""vocab_file"""]					)
									_SCREAMING_SNAKE_CASE     				=  os.path.join(self.tmpdirname				,     VOCAB_FILES_NAMES["""merges_file"""]					)
									with open(self.vocab_file				,     """w"""					) as fp:
															fp.write(json.dumps(UpperCAmelCase_					)					)
									with open(self.merges_file				,     """w"""					) as fp:
															fp.write("""\n""".join(UpperCAmelCase_					)					)







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Dict					):







									'''simple docstring'''




									return "lower newer", "lower newer"







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  OpenAIGPTTokenizer(self.vocab_file				,     self.merges_file					)

									_SCREAMING_SNAKE_CASE     				=  """lower"""
									_SCREAMING_SNAKE_CASE     				=  ["""low""", """er</w>"""]
									_SCREAMING_SNAKE_CASE     				=  tokenizer.tokenize(UpperCAmelCase_					)
									self.assertListEqual(UpperCAmelCase_				,     UpperCAmelCase_					)

									_SCREAMING_SNAKE_CASE     				=  tokens + ["""<unk>"""]
									_SCREAMING_SNAKE_CASE     				=  [14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_					)				,     UpperCAmelCase_					)







			def 							UpperCamelCase				(     self:  Optional[Any]				,     UpperCAmelCase_:  Union[str, Any]=15					):







									'''simple docstring'''




									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																					_SCREAMING_SNAKE_CASE     				=  self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,     **UpperCAmelCase_					)

																					# Simple input
																					_SCREAMING_SNAKE_CASE     				=  """This is a simple input"""
																					_SCREAMING_SNAKE_CASE     				=  ["""This is a simple input 1""", """This is a simple input 2"""]
																					_SCREAMING_SNAKE_CASE     				=  ("""This is a simple input""", """This is a pair""")
																					_SCREAMING_SNAKE_CASE     				=  [
																					    ("""This is a simple input 1""", """This is a simple input 2"""),
																					    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																					]

																					# Simple input tests
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Simple input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(UpperCAmelCase_				,     tokenizer_r.encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""					)

																					# Pair input
																					self.assertRaises(
																					    UpperCAmelCase_				,     tokenizer_r.batch_encode_plus				,     UpperCAmelCase_				,     max_length=UpperCAmelCase_				,     padding="""max_length"""				,     )







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									pass



@require_ftfy
@require_spacy
@require_tokenizers
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			pass

