




"""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      a__				(	__magic_name__							,    __magic_name__   ):


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


						"""simple docstring"""




						super().__init__()

						__UpperCAmelCase	: List[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"

									__UpperCAmelCase	: List[Any]		=					torch.zeros(UpperCamelCase_      ,  UpperCamelCase_)
						else:
									__UpperCAmelCase	: Tuple		=					None

						__UpperCAmelCase	: Dict		=					torch.nn.Parameter(UpperCamelCase_)






class      a__				(	__magic_name__   ):
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	42


			def __init__(      self      :    str      ,  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       a_     (      self      :    Dict      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    List[Any]      ,  UpperCamelCase_      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: int		=					len(UpperCamelCase_) if isinstance(UpperCamelCase_      ,  UpperCamelCase_) else 1

						# get prompt text embeddings
						__UpperCAmelCase	: Optional[int]		=					self.tokenizer(
						    UpperCamelCase_      ,  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[int]		=					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	: Union[str, Any]		=					text_input_ids[:, : self.tokenizer.model_max_length]
						__UpperCAmelCase	: List[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	: List[Any]		=					prompt_embeds / prompt_embeds.norm(dim=-1      ,  keepdim=UpperCamelCase_)

						# duplicate text embeddings for each generation per prompt
						__UpperCAmelCase	: Union[str, Any]		=					prompt_embeds.repeat_interleave(UpperCamelCase_      ,  dim=0)

						if do_classifier_free_guidance:
									if self.learned_classifier_free_sampling_embeddings.learnable:
												__UpperCAmelCase	: Optional[int]		=					self.learned_classifier_free_sampling_embeddings.embeddings
												__UpperCAmelCase	: int		=					negative_prompt_embeds.unsqueeze(0).repeat(UpperCamelCase_      ,  1      ,  1)
									else:
												__UpperCAmelCase	: int		=					[""] * batch_size

												__UpperCAmelCase	: List[Any]		=					text_input_ids.shape[-1]
												__UpperCAmelCase	: List[Any]		=					self.tokenizer(
												    UpperCamelCase_      ,  padding="max_length"      ,  max_length=UpperCamelCase_      ,  truncation=UpperCamelCase_      ,  return_tensors="pt"      ,  )
												__UpperCAmelCase	: List[str]		=					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=UpperCamelCase_)

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

						return prompt_embeds


			@torch.no_grad()
			def __call__(      self      :    List[str]      ,  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_):
									__UpperCAmelCase	: List[Any]		=					1
						elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
									__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase_)
						else:
									raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase_)}")

						__UpperCAmelCase	: Union[str, Any]		=					batch_size * num_images_per_prompt

						__UpperCAmelCase	: Union[str, Any]		=					guidance_scale > 1.0

						__UpperCAmelCase	: Dict		=					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

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

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

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

						__UpperCAmelCase	: Dict		=					latents

						for i, t in enumerate(self.progress_bar(UpperCamelCase_)):
									# 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	: List[Any]		=					self.transformer(UpperCamelCase_      ,  encoder_hidden_states=UpperCamelCase_      ,  timestep=UpperCamelCase_).sample

									if do_classifier_free_guidance:
												__UpperCAmelCase	,     __UpperCAmelCase	: str		=					model_output.chunk(2)
												__UpperCAmelCase	: Dict		=					model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
												model_output -= torch.logsumexp(UpperCamelCase_      ,  dim=1      ,  keepdim=UpperCamelCase_)

									__UpperCAmelCase	: Optional[int]		=					self.truncate(UpperCamelCase_      ,  UpperCamelCase_)

									# 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	: Any		=					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_)

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

						__UpperCAmelCase	: Optional[Any]		=					(image / 2 + 0.5).clamp(0      ,  1)
						__UpperCAmelCase	: Union[str, Any]		=					image.cpu().permute(0      ,  2      ,  3      ,  1).numpy()

						if output_type == "pil":
									__UpperCAmelCase	: str		=					self.numpy_to_pil(UpperCamelCase_)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=UpperCamelCase_)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    torch.FloatTensor      ,  UpperCamelCase_      :    float):


						"""simple docstring"""




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

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

						__UpperCAmelCase	: List[str]		=					keep_mask.gather(1      ,  indices.argsort(1))

						__UpperCAmelCase	: int		=					log_p_x_0.clone()

						__UpperCAmelCase	: List[str]		=					-torch.inf  # -inf = log(0)

						return rv








"""simple docstring"""





import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
A   =       """src/transformers"""
A   =       """docs/source/en"""
A   =       """."""

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			with open(UpperCamelCase				,       "r"				,       encoding="utf-8"				,       newline="\n"							) as f:
						__UpperCAmelCase	: str		=					f.readlines()
			# Find the start prompt.
			__UpperCAmelCase	: Optional[int]		=					0
			while not lines[start_index].startswith(UpperCamelCase							):
						start_index += 1
			start_index += 1

			__UpperCAmelCase	: Union[str, Any]		=					start_index
			while not lines[end_index].startswith(UpperCamelCase							):
						end_index += 1
			end_index -= 1

			while len(lines[start_index]							) <= 1:
						start_index += 1
			while len(lines[end_index]							) <= 1:
						end_index -= 1
			end_index += 1
			return "".join(lines[start_index:end_index]							), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
A   =       """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
A   =       re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
A   =       re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
A   =       re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
A   =       direct_transformers_import(TRANSFORMERS_PATH)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			str:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"				,       UpperCamelCase							)
			return [m.group(0							) for m in matches]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					2 if text == "✅" or text == "❌" else len(UpperCamelCase							)
			__UpperCAmelCase	: Optional[Any]		=					(width - text_length) // 2
			__UpperCAmelCase	: Tuple		=					width - text_length - left_indent
			return " " * left_indent + text + " " * right_indent

def 					_UpperCamelCase    (			)			->			Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase	: str		=					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			__UpperCAmelCase	: Dict		=					{
			    name: config_maping_names[code]
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if code in config_maping_names
			}
			__UpperCAmelCase	: Tuple		=					{name: config.replace("Config"				,       ""							) for name, config in model_name_to_config.items()}

			# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
			__UpperCAmelCase	: int		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: Optional[Any]		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: Optional[int]		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: Any		=					collections.defaultdict(UpperCamelCase							)
			__UpperCAmelCase	: str		=					collections.defaultdict(UpperCamelCase							)

			# Let's lookup through all transformers object (once).
			for attr_name in dir(UpperCamelCase							):
						__UpperCAmelCase	: Union[str, Any]		=					None
						if attr_name.endswith("Tokenizer"							):
									__UpperCAmelCase	: int		=					slow_tokenizers
									__UpperCAmelCase	: Optional[Any]		=					attr_name[:-9]
						elif attr_name.endswith("TokenizerFast"							):
									__UpperCAmelCase	: Any		=					fast_tokenizers
									__UpperCAmelCase	: Union[str, Any]		=					attr_name[:-13]
						elif _re_tf_models.match(UpperCamelCase							) is not None:
									__UpperCAmelCase	: Dict		=					tf_models
									__UpperCAmelCase	: List[str]		=					_re_tf_models.match(UpperCamelCase							).groups()[0]
						elif _re_flax_models.match(UpperCamelCase							) is not None:
									__UpperCAmelCase	: List[str]		=					flax_models
									__UpperCAmelCase	: List[str]		=					_re_flax_models.match(UpperCamelCase							).groups()[0]
						elif _re_pt_models.match(UpperCamelCase							) is not None:
									__UpperCAmelCase	: Dict		=					pt_models
									__UpperCAmelCase	: List[str]		=					_re_pt_models.match(UpperCamelCase							).groups()[0]

						if lookup_dict is not None:
									while len(UpperCamelCase							) > 0:
												if attr_name in model_name_to_prefix.values():
															__UpperCAmelCase	: List[Any]		=					True
															break
												# Try again after removing the last word in the name
												__UpperCAmelCase	: List[Any]		=					"".join(camel_case_split(UpperCamelCase							)[:-1]							)

    # Let's build that table!
			__UpperCAmelCase	: List[str]		=					list(model_name_to_config.keys()							)
			model_names.sort(key=str.lower							)
			__UpperCAmelCase	: Optional[Any]		=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
			# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
			__UpperCAmelCase	: Optional[int]		=					[len(UpperCamelCase							) + 2 for c in columns]
			__UpperCAmelCase	: Tuple		=					max([len(UpperCamelCase							) for name in model_names]							) + 2

			# Build the table per se
			__UpperCAmelCase	: List[Any]		=					"|" + "|".join([_center_text(UpperCamelCase				,       UpperCamelCase							) for c, w in zip(UpperCamelCase				,       UpperCamelCase							)]							) + "|\n"
			# Use ":-----:" format to center-aligned table cell texts
			table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]							) + "|\n"

			__UpperCAmelCase	: Optional[Any]		=					{True: "✅", False: "❌"}
			for name in model_names:
						__UpperCAmelCase	: List[Any]		=					model_name_to_prefix[name]
						__UpperCAmelCase	: int		=					[
						    name,
						    check[slow_tokenizers[prefix]],
						    check[fast_tokenizers[prefix]],
						    check[pt_models[prefix]],
						    check[tf_models[prefix]],
						    check[flax_models[prefix]],
						]
						table += "|" + "|".join([_center_text(UpperCamelCase				,       UpperCamelCase							) for l, w in zip(UpperCamelCase				,       UpperCamelCase							)]							) + "|\n"
			return table

def 					_UpperCamelCase    (			UpperCamelCase=False							)			->			str:


			"""simple docstring"""
			__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: Union[str, Any]		=					_find_text_in_file(
			    filename=os.path.join(UpperCamelCase				,       "index.md"							)				,       start_prompt="<!--This table is updated automatically from the auto modules"				,       end_prompt="<!-- End table-->"				,       )
			__UpperCAmelCase	: Optional[Any]		=					get_model_table_from_auto_modules()

			if current_table != new_table:
						if overwrite:
									with open(os.path.join(UpperCamelCase				,       "index.md"							)				,       "w"				,       encoding="utf-8"				,       newline="\n"							) as f:
												f.writelines(lines[:start_index] + [new_table] + lines[end_index:]							)
						else:
									raise ValueError(
									    "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."							)


if __name__ == "__main__":
					A   =       argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					A   =       parser.parse_args()

					check_model_table(args.fix_and_overwrite)


