
"""simple docstring"""

from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


_A				=					getLogger(__name__)


_A				=					4
_A				=					7

_A				=					{
    'float16': torch.floataa,
    'float32': torch.floataa,
}
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		if isinstance(a_,		torch.Tensor     ):
				return image
		elif isinstance(a_,		PIL.Image.Image     ):
				lowerCamelCase	:     Optional[int]            =       [image]

		if isinstance(image[0],		PIL.Image.Image     ):
				lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       image[0].size
				lowerCamelCase      ,       lowerCamelCase	:     int            =       (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

				lowerCamelCase	:     Union[str, Any]            =       [np.array(i.resize((w, h)     )     )[None, :] for i in image]
				lowerCamelCase	:     Tuple            =       np.concatenate(a_,		axis=0     )
				lowerCamelCase	:     List[Any]            =       np.array(a_     ).astype(np.floataa     ) / 2_5_5.0
				lowerCamelCase	:     Any            =       image.transpose(0,		3,		1,		2     )
				lowerCamelCase	:     Optional[Any]            =       2.0 * image - 1.0
				lowerCamelCase	:     str            =       torch.from_numpy(a_     )
		elif isinstance(image[0],		torch.Tensor     ):
				lowerCamelCase	:     List[Any]            =       torch.cat(a_,		dim=0     )

		return image






class 			_lowercase  (							__UpperCAmelCase						):




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_ = 350						,					)						->				Any:
			super().__init__(
			    vae=UpperCAmelCase_						,					text_encoder=UpperCAmelCase_						,					tokenizer=UpperCAmelCase_						,					unet=UpperCAmelCase_						,					low_res_scheduler=UpperCAmelCase_						,					scheduler=UpperCAmelCase_						,					safety_checker=UpperCAmelCase_						,					feature_extractor=UpperCAmelCase_						,					watermarker=UpperCAmelCase_						,					max_noise_level=UpperCAmelCase_						,					)




	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_ = 75						,					UpperCAmelCase_ = 9.0						,					UpperCAmelCase_ = 20						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = 1						,					UpperCAmelCase_ = 0.0						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = "pil"						,					UpperCAmelCase_ = True						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = 1						,					)						->				Optional[Any]:
			self.check_inputs(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

			# 2. Define call parameters
			if prompt is not None and isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Any            =       1
			elif prompt is not None and isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       len(UpperCAmelCase_   )
			else:
					lowerCamelCase	:     int            =       prompt_embeds.shape[0]

			lowerCamelCase	:     int            =       self._execution_device
			# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
			# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
			# corresponds to doing no classifier free guidance.
			lowerCamelCase	:     str            =       guidance_scale > 1.0

			# 3. Encode input prompt
			lowerCamelCase	:     List[Any]            =       self._encode_prompt(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					prompt_embeds=UpperCAmelCase_						,					negative_prompt_embeds=UpperCAmelCase_						,					)

			lowerCamelCase	:     Tuple            =       ORT_TO_PT_TYPE[str(text_embeddings.dtype   )]

			# 4. Preprocess image
			lowerCamelCase	:     Tuple            =       preprocess(UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       image.cpu()

			# 5. set timesteps
			self.scheduler.set_timesteps(UpperCAmelCase_						,					device=UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       self.scheduler.timesteps

			# 5. Add noise to image
			lowerCamelCase	:     str            =       torch.tensor([noise_level]						,					dtype=torch.long						,					device=UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       torch.randn(image.shape						,					generator=UpperCAmelCase_						,					device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )
			lowerCamelCase	:     Tuple            =       self.low_res_scheduler.add_noise(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

			lowerCamelCase	:     int            =       2 if do_classifier_free_guidance else 1
			lowerCamelCase	:     Union[str, Any]            =       np.concatenate([image] * batch_multiplier * num_images_per_prompt   )
			lowerCamelCase	:     Dict            =       np.concatenate([noise_level] * image.shape[0]   )

			# 6. Prepare latent variables
			lowerCamelCase      ,       lowerCamelCase	:     List[str]            =       image.shape[2:]
			lowerCamelCase	:     Tuple            =       self.prepare_latents(
			    batch_size * num_images_per_prompt						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)

			# 7. Check that sizes of image and latents match
			lowerCamelCase	:     Tuple            =       image.shape[1]
			if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
					raise ValueError(
					    'Incorrect configuration settings! The config of `pipeline.unet` expects'
					    F""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
					    F""" `num_channels_image`: {num_channels_image} """
					    F""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
					    ' `pipeline.unet` or your `image` input.'   )

			# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
			lowerCamelCase	:     Union[str, Any]            =       self.prepare_extra_step_kwargs(UpperCAmelCase_						,					UpperCAmelCase_   )

			lowerCamelCase	:     Optional[int]            =       next(
			    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')						,					'tensor(float)'   )
			lowerCamelCase	:     str            =       ORT_TO_NP_TYPE[timestep_dtype]

			# 9. Denoising loop
			lowerCamelCase	:     List[Any]            =       len(UpperCAmelCase_   ) - num_inference_steps * self.scheduler.order
			with self.progress_bar(total=UpperCAmelCase_   ) as progress_bar:
					for i, t in enumerate(UpperCAmelCase_   ):
							# expand the latents if we are doing classifier free guidance
							lowerCamelCase	:     Union[str, Any]            =       np.concatenate([latents] * 2   ) if do_classifier_free_guidance else latents

							# concat latents, mask, masked_image_latents in the channel dimension
							lowerCamelCase	:     str            =       self.scheduler.scale_model_input(UpperCAmelCase_						,					UpperCAmelCase_   )
							lowerCamelCase	:     str            =       np.concatenate([latent_model_input, image]						,					axis=1   )

							# timestep to tensor
							lowerCamelCase	:     Union[str, Any]            =       np.array([t]						,					dtype=UpperCAmelCase_   )

							# predict the noise residual
							lowerCamelCase	:     Dict            =       self.unet(
							    sample=UpperCAmelCase_						,					timestep=UpperCAmelCase_						,					encoder_hidden_states=UpperCAmelCase_						,					class_labels=noise_level.astype(np.intaa   )						,					)[0]

							# perform guidance
							if do_classifier_free_guidance:
									lowerCamelCase      ,       lowerCamelCase	:     Dict            =       np.split(UpperCAmelCase_						,					2   )
							lowerCamelCase	:     Tuple            =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# compute the previous noisy sample x_t -> x_t-1
							lowerCamelCase	:     Dict            =       self.scheduler.step(
							    torch.from_numpy(UpperCAmelCase_   )						,					UpperCAmelCase_						,					UpperCAmelCase_						,					**UpperCAmelCase_   ).prev_sample

							# call the callback, if provided
							if i == len(UpperCAmelCase_   ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
									progress_bar.update()
									if callback is not None and i % callback_steps == 0:
											callback(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

        # 10. Post-processing
			lowerCamelCase	:     List[str]            =       self.decode_latents(latents.float()   )

			# 11. Convert to PIL
			if output_type == "pil":
					lowerCamelCase	:     Union[str, Any]            =       self.numpy_to_pil(UpperCAmelCase_   )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				List[Any]:
			lowerCamelCase	:     Optional[int]            =       1 / 0.08333 * latents
			lowerCamelCase	:     Union[str, Any]            =       self.vae(latent_sample=UpperCAmelCase_   )[0]
			lowerCamelCase	:     Tuple            =       np.clip(image / 2 + 0.5						,					0						,					1   )
			lowerCamelCase	:     Optional[int]            =       image.transpose((0, 2, 3, 1)   )
			return image




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					)						->				Tuple:
			if prompt is not None and isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       1
			elif prompt is not None and isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[str]            =       len(UpperCAmelCase_   )
			else:
					lowerCamelCase	:     Optional[Any]            =       prompt_embeds.shape[0]

			if prompt_embeds is None:
					lowerCamelCase	:     Any            =       self.tokenizer(
					    UpperCAmelCase_						,					padding='max_length'						,					max_length=self.tokenizer.model_max_length						,					truncation=UpperCAmelCase_						,					return_tensors='pt'						,					)
					lowerCamelCase	:     Optional[Any]            =       text_inputs.input_ids
					lowerCamelCase	:     str            =       self.tokenizer(UpperCAmelCase_						,					padding='longest'						,					return_tensors='pt'   ).input_ids

					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
					    UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     List[str]            =       self.tokenizer.batch_decode(
							    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
							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}"""   )

					# no positional arguments to text_encoder
					lowerCamelCase	:     List[str]            =       self.text_encoder(
					    input_ids=text_input_ids.int().to(UpperCAmelCase_   )						,					)
					lowerCamelCase	:     int            =       prompt_embeds[0]

			lowerCamelCase      ,       lowerCamelCase      ,       lowerCamelCase	:     Optional[Any]            =       prompt_embeds.shape
			# duplicate text embeddings for each generation per prompt, using mps friendly method
			lowerCamelCase	:     List[Any]            =       prompt_embeds.repeat(1						,					UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       prompt_embeds.reshape(bs_embed * num_images_per_prompt						,					UpperCAmelCase_						,					-1   )

			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance and negative_prompt_embeds is None:
					lowerCamelCase	:     List[str]
					if negative_prompt is None:
							lowerCamelCase	:     Optional[int]            =       [''] * batch_size
					elif type(UpperCAmelCase_   ) is not type(UpperCAmelCase_   ):
							raise TypeError(
							    F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase_   )} !="""
							    F""" {type(UpperCAmelCase_   )}."""   )
					elif isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     List[str]            =       [negative_prompt]
					elif batch_size != len(UpperCAmelCase_   ):
							raise ValueError(
							    F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase_   )}, but `prompt`:"""
							    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
							    ' the batch size of `prompt`.'   )
					else:
							lowerCamelCase	:     Tuple            =       negative_prompt

					lowerCamelCase	:     str            =       text_input_ids.shape[-1]
					lowerCamelCase	:     Any            =       self.tokenizer(
					    UpperCAmelCase_						,					padding='max_length'						,					max_length=UpperCAmelCase_						,					truncation=UpperCAmelCase_						,					return_tensors='pt'						,					)

					# if hasattr(uncond_input, "attention_mask"):
					#     attention_mask = uncond_input.attention_mask.to(device)
					# else:
					#     attention_mask = None

					lowerCamelCase	:     str            =       self.text_encoder(
					    input_ids=uncond_input.input_ids.int().to(UpperCAmelCase_   )						,					)
					lowerCamelCase	:     List[str]            =       uncond_embeddings[0]

			if do_classifier_free_guidance:
					lowerCamelCase	:     Dict            =       uncond_embeddings.shape[1]
					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
					lowerCamelCase	:     Tuple            =       uncond_embeddings.repeat(1						,					UpperCAmelCase_   )
					lowerCamelCase	:     Any            =       uncond_embeddings.reshape(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
					lowerCamelCase	:     Dict            =       np.concatenate([uncond_embeddings, prompt_embeds]   )

			return prompt_embeds








"""simple docstring"""

import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import (
					    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
					)

if is_tf_available() and is_tensorflow_probability_available():
					import tensorflow as tf
					import tensorflow_probability as tfp

					from ..models.auto.modeling_tf_auto import (
					    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
					)






class 			_lowercase  (							__UpperCAmelCase						):




	def __call__(					self						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					**UpperCAmelCase_   )						->				Dict:
			# Returns tqa_pipeline_inputs of shape:
			# [
			#   {"table": pd.DataFrame, "query": List[str]},
			#   ...,
			#   {"table": pd.DataFrame, "query" : List[str]}
			# ]
			requires_backends(self						,					'pandas'   )
			import pandas as pd

			if table is None:
					raise ValueError('Keyword argument `table` cannot be None.'   )
			elif query is None:
					if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) and table.get('query'   ) is not None and table.get('table'   ) is not None:
							lowerCamelCase	:     Any            =       [table]
					elif isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) and len(UpperCAmelCase_   ) > 0:
							if not all(isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) for d in table   ):
									raise ValueError(
									    F"""Keyword argument `table` should be a list of dict, but is {(type(UpperCAmelCase_   ) for d in table)}"""   )

							if table[0].get('query'   ) is not None and table[0].get('table'   ) is not None:
									lowerCamelCase	:     List[Any]            =       table
							else:
									raise ValueError(
									    'If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'
									    F""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""   )
					elif Dataset is not None and isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) or isinstance(UpperCAmelCase_						,					types.GeneratorType   ):
							return table
					else:
							raise ValueError(
							    'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
							    F"""is {type(UpperCAmelCase_   )})"""   )
			else:
					lowerCamelCase	:     List[str]            =       [{'table': table, 'query': query}]

			for tqa_pipeline_input in tqa_pipeline_inputs:
					if not isinstance(tqa_pipeline_input['table']						,					pd.DataFrame   ):
							if tqa_pipeline_input["table"] is None:
									raise ValueError('Table cannot be None.'   )

							lowerCamelCase	:     Optional[int]            =       pd.DataFrame(tqa_pipeline_input['table']   )

			return tqa_pipeline_inputs






@add_end_docstrings(__UpperCAmelCase						)
class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      'table,query'




	def __init__(					self						,					UpperCAmelCase_=TableQuestionAnsweringArgumentHandler()						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				int:
			super().__init__(*UpperCAmelCase_						,					**UpperCAmelCase_   )
			lowerCamelCase	:     List[str]            =       args_parser

			self.check_model_type(
			    dict(
			        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
			        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()   )
			    if self.framework == 'tf'
			    else dict(
			        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()   )   )

			lowerCamelCase	:     Dict            =       bool(getattr(self.model.config						,					'aggregation_labels'						,					UpperCAmelCase_   )   ) and bool(
			    getattr(self.model.config						,					'num_aggregation_labels'						,					UpperCAmelCase_   )   )
			lowerCamelCase	:     str            =       'tapas' if hasattr(self.model.config						,					'aggregation_labels'   ) else None




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Any:
			return self.model(**UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Dict:
			if self.framework == "pt":
					lowerCamelCase	:     List[Any]            =       []
					lowerCamelCase	:     Optional[Any]            =       []
					lowerCamelCase	:     List[str]            =       None
					lowerCamelCase	:     List[str]            =       inputs['input_ids'].shape[0]

					lowerCamelCase	:     Any            =       inputs['input_ids'].to(self.device   )
					lowerCamelCase	:     Optional[Any]            =       inputs['attention_mask'].to(self.device   )
					lowerCamelCase	:     Optional[int]            =       inputs['token_type_ids'].to(self.device   )
					lowerCamelCase	:     str            =       None

					for index in range(UpperCAmelCase_   ):
							# If sequences have already been processed, the token type IDs will be created according to the previous
							# answer.
							if prev_answers is not None:
									lowerCamelCase	:     List[str]            =       token_type_ids_example[:, 3]  # shape (seq_len,)
									lowerCamelCase	:     Dict            =       np.zeros_like(prev_labels_example.cpu().numpy()   )  # shape (seq_len,)

									lowerCamelCase	:     Optional[Any]            =       token_type_ids[index]  # shape (seq_len, 7)
									for i in range(model_labels.shape[0]   ):
											lowerCamelCase	:     str            =       token_type_ids_example[:, 0].tolist()[i]
											lowerCamelCase	:     str            =       token_type_ids_example[:, 1].tolist()[i] - 1
											lowerCamelCase	:     str            =       token_type_ids_example[:, 2].tolist()[i] - 1

											if row_id >= 0 and col_id >= 0 and segment_id == 1:
													lowerCamelCase	:     List[str]            =       int(prev_answers[(col_id, row_id)]   )

									lowerCamelCase	:     Optional[int]            =       torch.from_numpy(UpperCAmelCase_   ).type(torch.long   ).to(self.device   )

							lowerCamelCase	:     List[Any]            =       input_ids[index]
							lowerCamelCase	:     Optional[int]            =       attention_mask[index]  # shape (seq_len,)
							lowerCamelCase	:     List[str]            =       token_type_ids[index]  # shape (seq_len, 7)
							lowerCamelCase	:     Tuple            =       self.model(
							    input_ids=input_ids_example.unsqueeze(0   )						,					attention_mask=attention_mask_example.unsqueeze(0   )						,					token_type_ids=token_type_ids_example.unsqueeze(0   )						,					)
							lowerCamelCase	:     str            =       outputs.logits

							if self.aggregate:
									all_aggregations.append(outputs.logits_aggregation   )

							all_logits.append(UpperCAmelCase_   )

							lowerCamelCase	:     Dict            =       torch.distributions.Bernoulli(logits=UpperCAmelCase_   )
							lowerCamelCase	:     Tuple            =       dist_per_token.probs * attention_mask_example.type(torch.floataa   ).to(
							    dist_per_token.probs.device   )

							lowerCamelCase	:     str            =       collections.defaultdict(UpperCAmelCase_   )
							for i, p in enumerate(probabilities.squeeze().tolist()   ):
									lowerCamelCase	:     Tuple            =       token_type_ids_example[:, 0].tolist()[i]
									lowerCamelCase	:     Dict            =       token_type_ids_example[:, 1].tolist()[i] - 1
									lowerCamelCase	:     List[Any]            =       token_type_ids_example[:, 2].tolist()[i] - 1
									if col >= 0 and row >= 0 and segment_id == 1:
											coords_to_probs[(col, row)].append(UpperCAmelCase_   )

							lowerCamelCase	:     List[str]            =       {key: np.array(coords_to_probs[key]   ).mean() > 0.5 for key in coords_to_probs}

					lowerCamelCase	:     int            =       torch.cat(tuple(UpperCAmelCase_   )						,					0   )

					return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCAmelCase_   )						,					0   ))
			else:
					lowerCamelCase	:     Union[str, Any]            =       []
					lowerCamelCase	:     Union[str, Any]            =       []
					lowerCamelCase	:     Union[str, Any]            =       None
					lowerCamelCase	:     str            =       inputs['input_ids'].shape[0]

					lowerCamelCase	:     List[Any]            =       inputs['input_ids']
					lowerCamelCase	:     str            =       inputs['attention_mask']
					lowerCamelCase	:     Optional[Any]            =       inputs['token_type_ids'].numpy()
					lowerCamelCase	:     Optional[Any]            =       None

					for index in range(UpperCAmelCase_   ):
							# If sequences have already been processed, the token type IDs will be created according to the previous
							# answer.
							if prev_answers is not None:
									lowerCamelCase	:     Union[str, Any]            =       token_type_ids_example[:, 3]  # shape (seq_len,)
									lowerCamelCase	:     List[Any]            =       np.zeros_like(UpperCAmelCase_						,					dtype=np.intaa   )  # shape (seq_len,)

									lowerCamelCase	:     str            =       token_type_ids[index]  # shape (seq_len, 7)
									for i in range(model_labels.shape[0]   ):
											lowerCamelCase	:     int            =       token_type_ids_example[:, 0].tolist()[i]
											lowerCamelCase	:     Optional[int]            =       token_type_ids_example[:, 1].tolist()[i] - 1
											lowerCamelCase	:     Tuple            =       token_type_ids_example[:, 2].tolist()[i] - 1

											if row_id >= 0 and col_id >= 0 and segment_id == 1:
													lowerCamelCase	:     Optional[int]            =       int(prev_answers[(col_id, row_id)]   )

									lowerCamelCase	:     Optional[int]            =       model_labels

							lowerCamelCase	:     str            =       input_ids[index]
							lowerCamelCase	:     Tuple            =       attention_mask[index]  # shape (seq_len,)
							lowerCamelCase	:     Tuple            =       token_type_ids[index]  # shape (seq_len, 7)
							lowerCamelCase	:     Union[str, Any]            =       self.model(
							    input_ids=np.expand_dims(UpperCAmelCase_						,					axis=0   )						,					attention_mask=np.expand_dims(UpperCAmelCase_						,					axis=0   )						,					token_type_ids=np.expand_dims(UpperCAmelCase_						,					axis=0   )						,					)
							lowerCamelCase	:     int            =       outputs.logits

							if self.aggregate:
									all_aggregations.append(outputs.logits_aggregation   )

							all_logits.append(UpperCAmelCase_   )

							lowerCamelCase	:     int            =       tfp.distributions.Bernoulli(logits=UpperCAmelCase_   )
							lowerCamelCase	:     Dict            =       dist_per_token.probs_parameter() * tf.cast(UpperCAmelCase_						,					tf.floataa   )

							lowerCamelCase	:     Union[str, Any]            =       collections.defaultdict(UpperCAmelCase_   )
							lowerCamelCase	:     List[str]            =       token_type_ids_example
							for i, p in enumerate(tf.squeeze(UpperCAmelCase_   ).numpy().tolist()   ):
									lowerCamelCase	:     List[str]            =       token_type_ids_example[:, 0].tolist()[i]
									lowerCamelCase	:     Optional[int]            =       token_type_ids_example[:, 1].tolist()[i] - 1
									lowerCamelCase	:     int            =       token_type_ids_example[:, 2].tolist()[i] - 1
									if col >= 0 and row >= 0 and segment_id == 1:
											coords_to_probs[(col, row)].append(UpperCAmelCase_   )

							lowerCamelCase	:     Any            =       {key: np.array(coords_to_probs[key]   ).mean() > 0.5 for key in coords_to_probs}

					lowerCamelCase	:     Union[str, Any]            =       tf.concat(tuple(UpperCAmelCase_   )						,					0   )

					return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(UpperCAmelCase_   )						,					0   ))




	def __call__(					self						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase	:     Optional[Any]            =       self._args_parser(*UpperCAmelCase_						,					**UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       super().__call__(UpperCAmelCase_						,					**UpperCAmelCase_   )
			if len(UpperCAmelCase_   ) == 1:
					return results[0]
			return results




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					**UpperCAmelCase_   )						->				Union[str, Any]:
			lowerCamelCase	:     List[Any]            =       {}
			if padding is not None:
					lowerCamelCase	:     Union[str, Any]            =       padding
			if truncation is not None:
					lowerCamelCase	:     Dict            =       truncation

			lowerCamelCase	:     Optional[Any]            =       {}
			if sequential is not None:
					lowerCamelCase	:     Any            =       sequential
			return preprocess_params, forward_params, {}




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=None						,					UpperCAmelCase_=True						,					UpperCAmelCase_=None   )						->				str:
			if truncation is None:
					if self.type == "tapas":
							lowerCamelCase	:     Any            =       'drop_rows_to_fit'
					else:
							lowerCamelCase	:     str            =       'do_not_truncate'

			lowerCamelCase      ,       lowerCamelCase	:     List[str]            =       pipeline_input['table'], pipeline_input['query']
			if table.empty:
					raise ValueError('table is empty'   )
			if query is None or query == "":
					raise ValueError('query is empty'   )
			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer(UpperCAmelCase_						,					UpperCAmelCase_						,					return_tensors=self.framework						,					truncation=UpperCAmelCase_						,					padding=UpperCAmelCase_   )
			lowerCamelCase	:     int            =       table
			return inputs




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=False   )						->				Optional[Any]:
			lowerCamelCase	:     Optional[Any]            =       model_inputs.pop('table'   )

			if self.type == "tapas":
					if sequential:
							lowerCamelCase	:     List[Any]            =       self.sequential_inference(**UpperCAmelCase_   )
					else:
							lowerCamelCase	:     int            =       self.batch_inference(**UpperCAmelCase_   )
			else:
					lowerCamelCase	:     Tuple            =       self.model.generate(**UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       {'model_inputs': model_inputs, 'table': table, 'outputs': outputs}
			return model_outputs




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				str:
			lowerCamelCase	:     str            =       model_outputs['model_inputs']
			lowerCamelCase	:     int            =       model_outputs['table']
			lowerCamelCase	:     Optional[Any]            =       model_outputs['outputs']
			if self.type == "tapas":
					if self.aggregate:
							lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       outputs[:2]
							lowerCamelCase	:     List[str]            =       self.tokenizer.convert_logits_to_predictions(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       predictions
							lowerCamelCase	:     Dict            =       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(UpperCAmelCase_   )}

							lowerCamelCase	:     Union[str, Any]            =       self.model.config.no_aggregation_label_index
							lowerCamelCase	:     int            =       {
							    i: aggregators[i] + ' > ' for i, pred in enumerate(UpperCAmelCase_   ) if pred != no_agg_label_index
							}
					else:
							lowerCamelCase	:     int            =       outputs[0]
							lowerCamelCase	:     Any            =       self.tokenizer.convert_logits_to_predictions(UpperCAmelCase_						,					UpperCAmelCase_   )
							lowerCamelCase	:     List[str]            =       predictions[0]
							lowerCamelCase	:     Optional[int]            =       {}
							lowerCamelCase	:     List[str]            =       {}
					lowerCamelCase	:     Any            =       []
					for index, coordinates in enumerate(UpperCAmelCase_   ):
							lowerCamelCase	:     Optional[int]            =       [table.iat[coordinate] for coordinate in coordinates]
							lowerCamelCase	:     Dict            =       aggregators.get(UpperCAmelCase_						,					''   )
							lowerCamelCase	:     Optional[Any]            =       aggregators_prefix.get(UpperCAmelCase_						,					''   )
							lowerCamelCase	:     int            =       {
							    'answer': aggregator_prefix + ', '.join(UpperCAmelCase_   ),
							    'coordinates': coordinates,
							    'cells': [table.iat[coordinate] for coordinate in coordinates],
							}
							if aggregator:
									lowerCamelCase	:     Optional[Any]            =       aggregator

							answers.append(UpperCAmelCase_   )
					if len(UpperCAmelCase_   ) == 0:
							raise PipelineException('Empty answer'   )
			else:
					lowerCamelCase	:     List[Any]            =       [{'answer': answer} for answer in self.tokenizer.batch_decode(UpperCAmelCase_						,					skip_special_tokens=UpperCAmelCase_   )]

			return answers if len(UpperCAmelCase_   ) > 1 else answers[0]






