

"""simple docstring"""




import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


__A     :    Any    =		logging.get_logger(__name__)  # pylint: disable=invalid-name
def    __SCREAMING_SNAKE_CASE						(						self			,							lowercase__			,							lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					image.to(device=lowercase__      )
					A			=					image  # take image as image_embeddings
					A			=					image_embeddings.unsqueeze(1      )

					# duplicate image embeddings for each generation per prompt, using mps friendly method
					A			,    A			,    A			=					image_embeddings.shape
					A			=					image_embeddings.repeat(1			,							lowercase__			,							1      )
					A			=					image_embeddings.view(bs_embed * num_images_per_prompt			,							lowercase__			,							-1      )

					if do_classifier_free_guidance:
										A			=					torch.zeros_like(lowercase__      )

										# 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			=					torch.cat([uncond_embeddings, image_embeddings]      )

					return image_embeddings





class 			__UpperCamelCase (   _A						):


		def __init__(self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		PriorTransformer     ,     __SCREAMING_SNAKE_CASE :		CLIPTokenizer     ,     __SCREAMING_SNAKE_CASE :		CLIPTextModelWithProjection     ,     __SCREAMING_SNAKE_CASE :		UnCLIPScheduler     ,     __SCREAMING_SNAKE_CASE :		Optional[dict] = None     ,     ):
							super().__init__()

							A			=					{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

							A			=					decoder_pipe_kwargs.get("torch_dtype"     ,     __SCREAMING_SNAKE_CASE) or prior.dtype

							A			=					StableDiffusionImageVariationPipeline.from_pretrained(
							    "lambdalabs/sd-image-variations-diffusers"     ,     **__SCREAMING_SNAKE_CASE)

							# replace `_encode_image` method
							A			=					types.MethodType(_encode_image     ,     self.decoder_pipe)

							self.register_modules(
							    prior=__SCREAMING_SNAKE_CASE     ,     tokenizer=__SCREAMING_SNAKE_CASE     ,     text_encoder=__SCREAMING_SNAKE_CASE     ,     prior_scheduler=__SCREAMING_SNAKE_CASE     ,     )


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		List[str]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[CLIPTextModelOutput, Tuple]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[torch.Tensor] = None     ,     ):
							if text_model_output is None:
												A			=					len(__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE) else 1
												# get prompt text embeddings
												A			=					self.tokenizer(
												    __SCREAMING_SNAKE_CASE     ,     padding="max_length"     ,     max_length=self.tokenizer.model_max_length     ,     return_tensors="pt"     ,     )
												A			=					text_inputs.input_ids
												A			=					text_inputs.attention_mask.bool().to(__SCREAMING_SNAKE_CASE)

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	A			=					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			=					text_input_ids[:, : self.tokenizer.model_max_length]

												A			=					self.text_encoder(text_input_ids.to(__SCREAMING_SNAKE_CASE))

												A			=					text_encoder_output.text_embeds
												A			=					text_encoder_output.last_hidden_state

							else:
												A			=					text_model_output[0].shape[0]
												A			,    A			=					text_model_output[0], text_model_output[1]
												A			=					text_attention_mask

							A			=					text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE     ,     dim=0)
							A			=					text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE     ,     dim=0)
							A			=					text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE     ,     dim=0)

							if do_classifier_free_guidance:
												A			=					[""] * batch_size

												A			=					self.tokenizer(
												    __SCREAMING_SNAKE_CASE     ,     padding="max_length"     ,     max_length=self.tokenizer.model_max_length     ,     truncation=__SCREAMING_SNAKE_CASE     ,     return_tensors="pt"     ,     )
												A			=					uncond_input.attention_mask.bool().to(__SCREAMING_SNAKE_CASE)
												A			=					self.text_encoder(uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE))

												A			=					uncond_embeddings_text_encoder_output.text_embeds
												A			=					uncond_embeddings_text_encoder_output.last_hidden_state

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

												A			=					uncond_embeddings.shape[1]
												A			=					uncond_embeddings.repeat(1     ,     __SCREAMING_SNAKE_CASE)
												A			=					uncond_embeddings.view(batch_size * num_images_per_prompt     ,     __SCREAMING_SNAKE_CASE)

												A			=					uncond_text_encoder_hidden_states.shape[1]
												A			=					uncond_text_encoder_hidden_states.repeat(1     ,     __SCREAMING_SNAKE_CASE     ,     1)
												A			=					uncond_text_encoder_hidden_states.view(
												    batch_size * num_images_per_prompt     ,     __SCREAMING_SNAKE_CASE     ,     -1)
												A			=					uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE     ,     dim=0)

												# done duplicates

												# 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			=					torch.cat([uncond_embeddings, text_embeddings])
												A			=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

												A			=					torch.cat([uncond_text_mask, text_mask])

							return text_embeddings, text_encoder_hidden_states, text_mask


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							if self.device != torch.device("meta") or not hasattr(self.prior     ,     "_hf_hook"):
												return self.device
							for module in self.prior.modules():
												if (
												    hasattr(__SCREAMING_SNAKE_CASE     ,     "_hf_hook")
												    and hasattr(module._hf_hook     ,     "execution_device")
												    and module._hf_hook.execution_device is not None
												):
																	return torch.device(module._hf_hook.execution_device)
							return self.device


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		List[str]):
							if latents is None:
												A			=					randn_tensor(__SCREAMING_SNAKE_CASE     ,     generator=__SCREAMING_SNAKE_CASE     ,     device=__SCREAMING_SNAKE_CASE     ,     dtype=__SCREAMING_SNAKE_CASE)
							else:
												if latents.shape != shape:
																	raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
												A			=					latents.to(__SCREAMING_SNAKE_CASE)

							A			=					latents * scheduler.init_noise_sigma
							return latents


		def 					SCREAMING_SNAKE_CASE__   (self :		Any     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, torch.device]] = None):
							self.decoder_pipe.to(__SCREAMING_SNAKE_CASE)
							super().to(__SCREAMING_SNAKE_CASE)


		@torch.no_grad()
		def __call__(self :		int     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[str, List[str]]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[int] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[int] = None     ,     __SCREAMING_SNAKE_CASE :		int = 1     ,     __SCREAMING_SNAKE_CASE :		int = 2_5     ,     __SCREAMING_SNAKE_CASE :		Optional[torch.Generator] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[torch.FloatTensor] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[Union[CLIPTextModelOutput, Tuple]] = None     ,     __SCREAMING_SNAKE_CASE :		Optional[torch.Tensor] = None     ,     __SCREAMING_SNAKE_CASE :		float = 4.0     ,     __SCREAMING_SNAKE_CASE :		float = 8.0     ,     __SCREAMING_SNAKE_CASE :		int = 5_0     ,     __SCREAMING_SNAKE_CASE :		Optional[int] = 1     ,     __SCREAMING_SNAKE_CASE :		float = 0.0     ,     __SCREAMING_SNAKE_CASE :		Optional[str] = "pil"     ,     __SCREAMING_SNAKE_CASE :		bool = True     ,     ):
							if prompt is not None:
												if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	A			=					1
												elif isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																	A			=					len(__SCREAMING_SNAKE_CASE)
												else:
																	raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE)}""")
							else:
												A			=					text_model_output[0].shape[0]

							A			=					self._execution_device

							A			=					batch_size * num_images_per_prompt

							A			=					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

							A			,    A			,    A			=					self._encode_prompt(
							    __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							# prior

							self.prior_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,     device=__SCREAMING_SNAKE_CASE)
							A			=					self.prior_scheduler.timesteps

							A			=					self.prior.config.embedding_dim

							A			=					self.prepare_latents(
							    (batch_size, embedding_dim)     ,     text_embeddings.dtype     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     self.prior_scheduler     ,     )

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

												A			=					self.prior(
												    __SCREAMING_SNAKE_CASE     ,     timestep=__SCREAMING_SNAKE_CASE     ,     proj_embedding=__SCREAMING_SNAKE_CASE     ,     encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,     attention_mask=__SCREAMING_SNAKE_CASE     ,     ).predicted_image_embedding

												if do_classifier_free_guidance:
																	A			,    A			=					predicted_image_embedding.chunk(2)
																	A			=					predicted_image_embedding_uncond + prior_guidance_scale * (
																	    predicted_image_embedding_text - predicted_image_embedding_uncond
																	)

												if i + 1 == prior_timesteps_tensor.shape[0]:
																	A			=					None
												else:
																	A			=					prior_timesteps_tensor[i + 1]

												A			=					self.prior_scheduler.step(
												    __SCREAMING_SNAKE_CASE     ,     timestep=__SCREAMING_SNAKE_CASE     ,     sample=__SCREAMING_SNAKE_CASE     ,     generator=__SCREAMING_SNAKE_CASE     ,     prev_timestep=__SCREAMING_SNAKE_CASE     ,     ).prev_sample

							A			=					self.prior.post_process_latents(__SCREAMING_SNAKE_CASE)

							A			=					prior_latents

							A			=					self.decoder_pipe(
							    image=__SCREAMING_SNAKE_CASE     ,     height=__SCREAMING_SNAKE_CASE     ,     width=__SCREAMING_SNAKE_CASE     ,     num_inference_steps=__SCREAMING_SNAKE_CASE     ,     guidance_scale=__SCREAMING_SNAKE_CASE     ,     generator=__SCREAMING_SNAKE_CASE     ,     output_type=__SCREAMING_SNAKE_CASE     ,     return_dict=__SCREAMING_SNAKE_CASE     ,     num_images_per_prompt=__SCREAMING_SNAKE_CASE     ,     eta=__SCREAMING_SNAKE_CASE     ,     )
							return output








"""simple docstring"""




import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


__A     :    int    =		'none'
__A     :    List[Any]    =		'none'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					value.split(lowercase__      )
					return len(lowercase__      ) > 1 and any((len(lowercase__      ) == 3 for x in split)      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if isinstance(lowercase__			,							lowercase__      ):
										return value
					if isinstance(lowercase__			,							lowercase__      ):
										return float(lowercase__      )
					if not isinstance(lowercase__			,							lowercase__      ):
										raise ValueError("Argument value is not a string. Can't parse it as float"      )
					A			=					value

					try:
										# Example: 1,000.7
										if "." in sanitized and "," in sanitized:
															return float(sanitized.replace(","			,							""      )      )
										# 1,000
										if "," in sanitized and _split_thousands(","			,							lowercase__      ):
															return float(sanitized.replace(","			,							""      )      )
										# 5,5556
										if "," in sanitized and sanitized.count(","      ) == 1 and not _split_thousands(","			,							lowercase__      ):
															return float(sanitized.replace(","			,							"."      )      )
										# 0.0.0.1
										if sanitized.count("."      ) > 1:
															return float(sanitized.replace("."			,							""      )      )
										# 0,0,0,1
										if sanitized.count(","      ) > 1:
															return float(sanitized.replace(","			,							""      )      )
										return float(lowercase__      )
					except ValueError:
										# Avoid adding the sanitized value in the error message.
										raise ValueError("Unable to convert value to float"      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if answer is None:
										return None
					try:
										A			=					convert_to_float(lowercase__      )
										if isinstance(lowercase__			,							lowercase__      ) and math.isnan(lowercase__      ):
															return None
										return value
					except ValueError:
										return answer.lower()


__A     :    List[str]    =		{
    'text': lambda x: x,
    'real': convert_to_float,
}





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2
		SCREAMING_SNAKE_CASE								=    3
		SCREAMING_SNAKE_CASE								=    4
		SCREAMING_SNAKE_CASE								=    5





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2





@dataclasses.dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42

__A     :    int    =		re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return list(_TOKENIZER.findall(x.lower()      )      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if operator == _Operator.EQUALS:
										return src == tgt
					elif operator == _Operator.GREATER:
										return src > tgt
					elif operator == _Operator.LESSER:
										return src < tgt
					raise ValueError(F"""Unknown operator: {operator}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					table["types"]
					return _TYPE_CONVERTER[types[column]](lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return isinstance(lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					for cond in conditions:
										A			=					row[cond.column]

										A			=					_parse_value(lowercase__			,							cond.column			,							cond.cmp_value      )

										if _is_string(lowercase__      ) and _is_string(lowercase__      ):
															A			=					_normalize_for_match(lowercase__      )
															A			=					_normalize_for_match(lowercase__      )

										if not isinstance(lowercase__			,							type(lowercase__      )      ):
															raise ValueError("Type difference {} != {}".format(type(lowercase__      )			,							type(lowercase__      )      )      )

										if not _compare(cond.operator			,							lowercase__			,							lowercase__      ):
															return False
					return True
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if not answer_coordinates:
										if aggregation_op == _Aggregation.COUNT:
															return 0.0
										else:
															return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
					if aggregation_op == _Aggregation.COUNT:
										return float(len(lowercase__      )      )

					# If we have just one answer, if float returns it or try a conversion.
					A			=					[table["rows"][i][j] for (i, j) in answer_coordinates]
					if len(lowercase__      ) == 1:
										try:
															return convert_to_float(values[0]      )
										except ValueError as e:
															if aggregation_op != _Aggregation.NONE:
																				raise e

					if aggregation_op == _Aggregation.NONE:
										return None

					# Other aggregation only support numeric values. Bail out if we have strings.
					if not all((isinstance(lowercase__			,							(int, float)      ) for v in values)      ):
										return None

					if aggregation_op == _Aggregation.SUM:
										return float(sum(lowercase__      )      )
					elif aggregation_op == _Aggregation.AVERAGE:
										return sum(lowercase__      ) / len(lowercase__      )
					else:
										raise ValueError(F"""Unknown aggregation: {aggregation_op}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					sql_query["agg"]
					if aggregation_op_index >= 3:
										A			=					_Aggregation(lowercase__      )
					else:
										A			=					_Aggregation.NONE

					A			=					sql_query["sel"]
					A			=					[
					    _Condition(lowercase__			,							_Operator(lowercase__      )			,							lowercase__      )
					    for column, operator, cmp_value in zip(
					        sql_query["conds"]["column_index"]			,							sql_query["conds"]["operator_index"]			,							sql_query["conds"]["condition"]      )
					]

					A			=					[]
					for row in range(len(table["rows"]      )      ):
										if _respect_conditions(lowercase__			,							table["rows"][row]			,							lowercase__      ):
															indices.append((row, target_column)      )

					if not indices:
										return [], aggregation_op

					if len(lowercase__      ) == 1:
										return indices, aggregation_op

					# Parsing of MIN/MAX.
					if aggregation_op_index in (1, 2):
										A			=					{2: min, 1: max}
										A			=					[(table["rows"][i][j], index) for index, (i, j) in enumerate(lowercase__      )]
										A			=					functools.reduce(operators[sql_query["agg"]]			,							lowercase__      )

										A			=					[indices[reduced[1]]]
										return ret, _Aggregation.NONE

					return indices, aggregation_op
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if float_answer is not None:
										return [str(lowercase__      )]
					return [str(table["real_rows"][r][c]      ) for r, c in answer_coordinates]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			,    A			=					_get_answer_coordinates(lowercase__			,							lowercase__      )
					A			=					_get_float_answer(lowercase__			,							lowercase__			,							lowercase__      )
					A			=					_get_answer_text(lowercase__			,							lowercase__			,							lowercase__      )
					# keep the original data the same with TaPas
					if len(lowercase__      ) == 0:
										A			=					[EMPTY_ANSWER]
					return answer_text





