
'''simple docstring'''


import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor
def 					__magic_name__(       ):
						UpperCamelCase_     :	Tuple					     =     set()
						# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
						for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
												UpperCamelCase_     :	Optional[int]					     =     getattr(transformers.models.auto							,				UpperCAmelCase__							,				UpperCAmelCase__	)
												if module is None:
																		continue
												# all mappings in a single auto modeling file
												UpperCamelCase_     :	Union[str, Any]					     =     [
												    x
												    for x in dir(UpperCAmelCase__	)
												    if x.endswith("""_MAPPING_NAMES"""	)
												    and (x.startswith("""MODEL_"""	) or x.startswith("""TF_MODEL_"""	) or x.startswith("""FLAX_MODEL_"""	))
												]
												for name in mapping_names:
																		UpperCamelCase_     :	Optional[int]					     =     getattr(UpperCAmelCase__							,				UpperCAmelCase__	)
																		if mapping is not None:
																								for v in mapping.values():
																														if isinstance(UpperCAmelCase__							,				(list, tuple)	):
																																				model_names.update(UpperCAmelCase__	)
																														elif isinstance(UpperCAmelCase__							,				UpperCAmelCase__	):
																																				model_names.add(UpperCAmelCase__	)

						return sorted(UpperCAmelCase__	)
def 					__magic_name__(       ):
						# All model names defined in auto mappings
						UpperCamelCase_     :	Any					     =     set(get_all_model_names()	)

						with open("""tests/utils/tiny_model_summary.json"""	) as fp:
												UpperCamelCase_     :	List[Any]					     =     json.load(UpperCAmelCase__	)
						UpperCamelCase_     :	Any					     =     set()
						for model_base_name in tiny_model_info:
												tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]	)

						# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
						UpperCamelCase_     :	int					     =     model_names.difference(UpperCAmelCase__	)
						for model_name in copy.copy(UpperCAmelCase__	):
												if not model_name.startswith("""TF"""	) and f"""TF{model_name}""" in not_on_hub:
																		tiny_models_names.remove(UpperCAmelCase__	)
												elif model_name.startswith("""TF"""	) and model_name[2:] in not_on_hub:
																		tiny_models_names.remove(UpperCAmelCase__	)

						return sorted(UpperCAmelCase__	)
def 					__magic_name__(       UpperCAmelCase__ :       Dict	):
						UpperCamelCase_     :	Optional[int]					     =     COMPOSITE_MODELS.values()

						# All tiny model base names on Hub
						UpperCamelCase_     :	str					     =     get_all_model_names()
						UpperCamelCase_     :	List[Any]					     =     hf_api.list_models(
						    filter=ModelFilter(
						        author="""hf-internal-testing"""							,				)	)
						UpperCamelCase_     :	Optional[Any]					     =     set()
						for x in models:
												UpperCamelCase_     :	Tuple					     =     x.modelId
												UpperCamelCase_     ,UpperCamelCase_     :	Any					     =     model.split("""/"""	)
												if not model.startswith("""tiny-random-"""	):
																		continue
												UpperCamelCase_     :	Optional[Any]					     =     model.replace("""tiny-random-"""							,				""""""	)
												if not model[0].isupper():
																		continue
												if model not in model_names and model not in special_models:
																		continue
												_models.add(UpperCAmelCase__	)

						UpperCamelCase_     :	Optional[Any]					     =     sorted(_models	)
						# All tiny model names on Hub
						UpperCamelCase_     :	List[Any]					     =     {}
						for model in models:
												UpperCamelCase_     :	List[Any]					     =     f"""hf-internal-testing/tiny-random-{model}"""
												UpperCamelCase_     :	Tuple					     =     model.split("""-"""	)[0]
												try:
																		UpperCamelCase_     :	List[str]					     =     hf_api.repo_info(UpperCAmelCase__	)
																		UpperCamelCase_     :	Tuple					     =     {
																		    """tokenizer_classes""": set(),
																		    """processor_classes""": set(),
																		    """model_classes""": set(),
																		    """sha""": repo_info.sha,
																		}
												except Exception:
																		continue
												try:
																		time.sleep(1	)
																		UpperCamelCase_     :	List[str]					     =     AutoTokenizer.from_pretrained(UpperCAmelCase__	)
																		content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__	)
												except Exception:
																		pass
												try:
																		time.sleep(1	)
																		UpperCamelCase_     :	Optional[Any]					     =     AutoTokenizer.from_pretrained(UpperCAmelCase__							,				use_fast=UpperCAmelCase__	)
																		content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__	)
												except Exception:
																		pass
												try:
																		time.sleep(1	)
																		UpperCamelCase_     :	str					     =     AutoImageProcessor.from_pretrained(UpperCAmelCase__	)
																		content["processor_classes"].add(img_p.__class__.__name__	)
												except Exception:
																		pass
												try:
																		time.sleep(1	)
																		UpperCamelCase_     :	Union[str, Any]					     =     AutoFeatureExtractor.from_pretrained(UpperCAmelCase__	)
																		if not isinstance(UpperCAmelCase__							,				UpperCAmelCase__	):
																								content["processor_classes"].add(feat_p.__class__.__name__	)
												except Exception:
																		pass
												try:
																		time.sleep(1	)
																		UpperCamelCase_     :	List[Any]					     =     getattr(UpperCAmelCase__							,				UpperCAmelCase__	)
																		UpperCamelCase_     :	Any					     =     model_class.from_pretrained(UpperCAmelCase__	)
																		content["model_classes"].add(m.__class__.__name__	)
												except Exception:
																		pass
												try:
																		time.sleep(1	)
																		UpperCamelCase_     :	List[Any]					     =     getattr(UpperCAmelCase__							,				f"""TF{model}"""	)
																		UpperCamelCase_     :	List[str]					     =     model_class.from_pretrained(UpperCAmelCase__	)
																		content["model_classes"].add(m.__class__.__name__	)
												except Exception:
																		pass

												UpperCamelCase_     :	Dict					     =     sorted(content["""tokenizer_classes"""]	)
												UpperCamelCase_     :	str					     =     sorted(content["""processor_classes"""]	)
												UpperCamelCase_     :	int					     =     sorted(content["""model_classes"""]	)

												UpperCamelCase_     :	Union[str, Any]					     =     content
												with open(os.path.join(UpperCAmelCase__							,				"""hub_tiny_model_summary.json"""	)							,				"""w"""	) as fp:
																		json.dump(UpperCAmelCase__							,				UpperCAmelCase__							,				ensure_ascii=UpperCAmelCase__							,				indent=4	)


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      argparse.ArgumentParser()
	parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
	__SCREAMING_SNAKE_CASE						=      parser.parse_args()

	# This has to be `spawn` to avoid hanging forever!
	multiprocessing.set_start_method("spawn")

	__SCREAMING_SNAKE_CASE						=      "tiny_models"
	__SCREAMING_SNAKE_CASE						=      True
	__SCREAMING_SNAKE_CASE						=      None
	__SCREAMING_SNAKE_CASE						=      get_tiny_model_names_from_repo()
	__SCREAMING_SNAKE_CASE						=      True
	__SCREAMING_SNAKE_CASE						=      True
	__SCREAMING_SNAKE_CASE						=      "hf-internal-testing"

	create_tiny_models(
	    output_path,
	    all,
	    model_types,
	    models_to_skip,
	    no_check,
	    upload,
	    organization,
	    token=os.environ.get("TOKEN", None),
	    num_workers=args.num_workers,
	)






'''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     __snake_case			(    _a	):




		def __call__(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:List[Any]=None			,    _SCREAMING_SNAKE_CASE					:Tuple=None			,    **_SCREAMING_SNAKE_CASE					:int)					->			str:






								'''simple docstring'''






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

																				if table[0].get("""query""") is not None and table[0].get("""table""") is not None:
																										UpperCamelCase_     :	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(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) or isinstance(_SCREAMING_SNAKE_CASE			,    types.GeneratorType):
																				return table
														else:
																				raise ValueError(
																				    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
																				    F"""is {type(_SCREAMING_SNAKE_CASE)})""")
								else:
														UpperCamelCase_     :	int					     =     [{"""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.""")

																				UpperCamelCase_     :	Any					     =     pd.DataFrame(tqa_pipeline_input["""table"""])

								return tqa_pipeline_inputs







@add_end_docstrings(_a	)
class     __snake_case			(    _a	):
		a_          =					"table,query"




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=TableQuestionAnsweringArgumentHandler()			,    *_SCREAMING_SNAKE_CASE					:Any			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[Any]:






								'''simple docstring'''






								super().__init__(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Optional[Any]					     =     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()))

								UpperCamelCase_     :	List[str]					     =     bool(getattr(self.model.config			,    """aggregation_labels"""			,    _SCREAMING_SNAKE_CASE)) and bool(
								    getattr(self.model.config			,    """num_aggregation_labels"""			,    _SCREAMING_SNAKE_CASE))
								UpperCamelCase_     :	List[Any]					     =     """tapas""" if hasattr(self.model.config			,    """aggregation_labels""") else None




		def 	_snake_case	(  self					:List[Any]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								return self.model(**_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[int]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								if self.framework == "pt":
														UpperCamelCase_     :	Optional[int]					     =     []
														UpperCamelCase_     :	Union[str, Any]					     =     []
														UpperCamelCase_     :	int					     =     None
														UpperCamelCase_     :	Dict					     =     inputs["""input_ids"""].shape[0]

														UpperCamelCase_     :	List[str]					     =     inputs["""input_ids"""].to(self.device)
														UpperCamelCase_     :	int					     =     inputs["""attention_mask"""].to(self.device)
														UpperCamelCase_     :	List[Any]					     =     inputs["""token_type_ids"""].to(self.device)
														UpperCamelCase_     :	List[str]					     =     None

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

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

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

																										UpperCamelCase_     :	Union[str, Any]					     =     torch.from_numpy(_SCREAMING_SNAKE_CASE).type(torch.long).to(self.device)

																				UpperCamelCase_     :	Optional[int]					     =     input_ids[index]
																				UpperCamelCase_     :	int					     =     attention_mask[index]  # shape (seq_len,)
																				UpperCamelCase_     :	Any					     =     token_type_ids[index]  # shape (seq_len, 7)
																				UpperCamelCase_     :	Union[str, Any]					     =     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)			,    )
																				UpperCamelCase_     :	int					     =     outputs.logits

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

																				all_logits.append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	Any					     =     torch.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	List[Any]					     =     dist_per_token.probs * attention_mask_example.type(torch.floataa).to(
																				    dist_per_token.probs.device)

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

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

														UpperCamelCase_     :	str					     =     torch.cat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

														return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_SCREAMING_SNAKE_CASE)			,    0))
								else:
														UpperCamelCase_     :	Any					     =     []
														UpperCamelCase_     :	Dict					     =     []
														UpperCamelCase_     :	Union[str, Any]					     =     None
														UpperCamelCase_     :	List[Any]					     =     inputs["""input_ids"""].shape[0]

														UpperCamelCase_     :	List[str]					     =     inputs["""input_ids"""]
														UpperCamelCase_     :	int					     =     inputs["""attention_mask"""]
														UpperCamelCase_     :	Optional[Any]					     =     inputs["""token_type_ids"""].numpy()
														UpperCamelCase_     :	Dict					     =     None

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

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

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

																										UpperCamelCase_     :	Optional[int]					     =     model_labels

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

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

																				all_logits.append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	List[Any]					     =     tfp.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	str					     =     dist_per_token.probs_parameter() * tf.cast(_SCREAMING_SNAKE_CASE			,    tf.floataa)

																				UpperCamelCase_     :	str					     =     collections.defaultdict(_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Any					     =     token_type_ids_example
																				for i, p in enumerate(tf.squeeze(_SCREAMING_SNAKE_CASE).numpy().tolist()):
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 0].tolist()[i]
																										UpperCamelCase_     :	List[str]					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																										UpperCamelCase_     :	Optional[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(_SCREAMING_SNAKE_CASE)

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

														UpperCamelCase_     :	Optional[Any]					     =     tf.concat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

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




		def __call__(  self					:Union[str, Any]			,    *_SCREAMING_SNAKE_CASE					:str			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self._args_parser(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[str]					     =     super().__call__(_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								if len(_SCREAMING_SNAKE_CASE) == 1:
														return results[0]
								return results




		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=None			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    **_SCREAMING_SNAKE_CASE					:List[str])					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     {}
								if padding is not None:
														UpperCamelCase_     :	Tuple					     =     padding
								if truncation is not None:
														UpperCamelCase_     :	Tuple					     =     truncation

								UpperCamelCase_     :	Optional[int]					     =     {}
								if sequential is not None:
														UpperCamelCase_     :	Tuple					     =     sequential
								return preprocess_params, forward_params, {}




		def 	_snake_case	(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:str=None			,    _SCREAMING_SNAKE_CASE					:Tuple=True			,    _SCREAMING_SNAKE_CASE					:str=None)					->			int:






								'''simple docstring'''






								if truncation is None:
														if self.type == "tapas":
																				UpperCamelCase_     :	List[Any]					     =     """drop_rows_to_fit"""
														else:
																				UpperCamelCase_     :	Union[str, Any]					     =     """do_not_truncate"""

								UpperCamelCase_     ,UpperCamelCase_     :	Any					     =     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""")
								UpperCamelCase_     :	Optional[Any]					     =     self.tokenizer(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    return_tensors=self.framework			,    truncation=_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	List[Any]					     =     table
								return inputs




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=False)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     model_inputs.pop("""table""")

								if self.type == "tapas":
														if sequential:
																				UpperCamelCase_     :	List[Any]					     =     self.sequential_inference(**_SCREAMING_SNAKE_CASE)
														else:
																				UpperCamelCase_     :	str					     =     self.batch_inference(**_SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_     :	List[Any]					     =     self.model.generate(**_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
								return model_outputs





		def 	_snake_case	(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:Optional[int])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     model_outputs["""model_inputs"""]
								UpperCamelCase_     :	int					     =     model_outputs["""table"""]
								UpperCamelCase_     :	Tuple					     =     model_outputs["""outputs"""]
								if self.type == "tapas":
														if self.aggregate:
																				UpperCamelCase_     ,UpperCamelCase_     :	int					     =     outputs[:2]
																				UpperCamelCase_     :	Any					     =     self.tokenizer.convert_logits_to_predictions(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     ,UpperCamelCase_     :	Dict					     =     predictions
																				UpperCamelCase_     :	str					     =     {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_SCREAMING_SNAKE_CASE)}

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

																				answers.append(_SCREAMING_SNAKE_CASE)
														if len(_SCREAMING_SNAKE_CASE) == 0:
																				raise PipelineException("""Empty answer""")
								else:
														UpperCamelCase_     :	Optional[int]					     =     [{"""answer""": answer} for answer in self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)]

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




