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 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __call__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					**__SCREAMING_SNAKE_CASE    :				Any   ):






					"""simple docstring"""
					# 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(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ) and table.get("query"   ) is not None and table.get("table"   ) is not None:
											_snake_case              =      [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:
														_snake_case              =      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:
								_snake_case              =      [{"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."   )

											_snake_case              =      pd.DataFrame(tqa_pipeline_input["table"]   )

					return tqa_pipeline_inputs

@add_end_docstrings(_lowercase							)
class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      '''table,query'''



		def __init__(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				str=TableQuestionAnsweringArgumentHandler()  ,					*__SCREAMING_SNAKE_CASE    :				int  ,					**__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					super().__init__(*__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )
					_snake_case              =      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()   )   )

					_snake_case              =      bool(getattr(self.model.config  ,					"aggregation_labels"  ,					__SCREAMING_SNAKE_CASE   )   ) and bool(
					    getattr(self.model.config  ,					"num_aggregation_labels"  ,					__SCREAMING_SNAKE_CASE   )   )
					_snake_case              =      "tapas" if hasattr(self.model.config  ,					"aggregation_labels"   ) else None



		def 				_a				(			self    :				str  ,					**__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return self.model(**__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				List[Any]  ,					**__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					if self.framework == "pt":
								_snake_case              =      []
								_snake_case              =      []
								_snake_case              =      None
								_snake_case              =      inputs["input_ids"].shape[0]

								_snake_case              =      inputs["input_ids"].to(self.device   )
								_snake_case              =      inputs["attention_mask"].to(self.device   )
								_snake_case              =      inputs["token_type_ids"].to(self.device   )
								_snake_case              =      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:
														_snake_case              =      token_type_ids_example[:, 3]  # shape (seq_len,)
														_snake_case              =      np.zeros_like(prev_labels_example.cpu().numpy()   )  # shape (seq_len,)

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

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

														_snake_case              =      torch.from_numpy(__SCREAMING_SNAKE_CASE   ).type(torch.long   ).to(self.device   )

											_snake_case              =      input_ids[index]
											_snake_case              =      attention_mask[index]  # shape (seq_len,)
											_snake_case              =      token_type_ids[index]  # shape (seq_len, 7)
											_snake_case              =      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   )  ,					)
											_snake_case              =      outputs.logits

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

											all_logits.append(__SCREAMING_SNAKE_CASE   )

											_snake_case              =      torch.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE   )
											_snake_case              =      dist_per_token.probs * attention_mask_example.type(torch.floataa   ).to(
											    dist_per_token.probs.device   )

											_snake_case              =      collections.defaultdict(__SCREAMING_SNAKE_CASE   )
											for i, p in enumerate(probabilities.squeeze().tolist()   ):
														_snake_case              =      token_type_ids_example[:, 0].tolist()[i]
														_snake_case              =      token_type_ids_example[:, 1].tolist()[i] - 1
														_snake_case              =      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   )

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

								_snake_case              =      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:
								_snake_case              =      []
								_snake_case              =      []
								_snake_case              =      None
								_snake_case              =      inputs["input_ids"].shape[0]

								_snake_case              =      inputs["input_ids"]
								_snake_case              =      inputs["attention_mask"]
								_snake_case              =      inputs["token_type_ids"].numpy()
								_snake_case              =      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:
														_snake_case              =      token_type_ids_example[:, 3]  # shape (seq_len,)
														_snake_case              =      np.zeros_like(__SCREAMING_SNAKE_CASE  ,					dtype=np.intaa   )  # shape (seq_len,)

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

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

														_snake_case              =      model_labels

											_snake_case              =      input_ids[index]
											_snake_case              =      attention_mask[index]  # shape (seq_len,)
											_snake_case              =      token_type_ids[index]  # shape (seq_len, 7)
											_snake_case              =      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   )  ,					)
											_snake_case              =      outputs.logits

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

											all_logits.append(__SCREAMING_SNAKE_CASE   )

											_snake_case              =      tfp.distributions.Bernoulli(logits=__SCREAMING_SNAKE_CASE   )
											_snake_case              =      dist_per_token.probs_parameter() * tf.cast(__SCREAMING_SNAKE_CASE  ,					tf.floataa   )

											_snake_case              =      collections.defaultdict(__SCREAMING_SNAKE_CASE   )
											_snake_case              =      token_type_ids_example
											for i, p in enumerate(tf.squeeze(__SCREAMING_SNAKE_CASE   ).numpy().tolist()   ):
														_snake_case              =      token_type_ids_example[:, 0].tolist()[i]
														_snake_case              =      token_type_ids_example[:, 1].tolist()[i] - 1
														_snake_case              =      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   )

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

								_snake_case              =      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    :				List[str]  ,					*__SCREAMING_SNAKE_CASE    :				List[Any]  ,					**__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      self._args_parser(*__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )

					_snake_case              =      super().__call__(__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )
					if len(__SCREAMING_SNAKE_CASE   ) == 1:
								return results[0]
					return results



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Dict=None  ,					__SCREAMING_SNAKE_CASE    :				Any=None  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					**__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      {}
					if padding is not None:
								_snake_case              =      padding
					if truncation is not None:
								_snake_case              =      truncation

					_snake_case              =      {}
					if sequential is not None:
								_snake_case              =      sequential
					return preprocess_params, forward_params, {}



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				str=True  ,					__SCREAMING_SNAKE_CASE    :				str=None   ):






					"""simple docstring"""
					if truncation is None:
								if self.type == "tapas":
											_snake_case              =      "drop_rows_to_fit"
								else:
											_snake_case              =      "do_not_truncate"

					_snake_case       , _snake_case              =      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"   )
					_snake_case              =      self.tokenizer(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					return_tensors=self.framework  ,					truncation=__SCREAMING_SNAKE_CASE  ,					padding=__SCREAMING_SNAKE_CASE   )
					_snake_case              =      table
					return inputs



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Tuple=False   ):






					"""simple docstring"""
					_snake_case              =      model_inputs.pop("table"   )

					if self.type == "tapas":
								if sequential:
											_snake_case              =      self.sequential_inference(**__SCREAMING_SNAKE_CASE   )
								else:
											_snake_case              =      self.batch_inference(**__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      self.model.generate(**__SCREAMING_SNAKE_CASE   )
					_snake_case              =      {"model_inputs": model_inputs, "table": table, "outputs": outputs}
					return model_outputs







		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]   ):






					"""simple docstring"""
					_snake_case              =      model_outputs["model_inputs"]
					_snake_case              =      model_outputs["table"]
					_snake_case              =      model_outputs["outputs"]
					if self.type == "tapas":
								if self.aggregate:
											_snake_case       , _snake_case              =      outputs[:2]
											_snake_case              =      self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
											_snake_case       , _snake_case              =      predictions
											_snake_case              =      {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__SCREAMING_SNAKE_CASE   )}

											_snake_case              =      self.model.config.no_aggregation_label_index
											_snake_case              =      {
											    i: aggregators[i] + " > " for i, pred in enumerate(__SCREAMING_SNAKE_CASE   ) if pred != no_agg_label_index
											}
								else:
											_snake_case              =      outputs[0]
											_snake_case              =      self.tokenizer.convert_logits_to_predictions(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
											_snake_case              =      predictions[0]
											_snake_case              =      {}
											_snake_case              =      {}
								_snake_case              =      []
								for index, coordinates in enumerate(__SCREAMING_SNAKE_CASE   ):
											_snake_case              =      [table.iat[coordinate] for coordinate in coordinates]
											_snake_case              =      aggregators.get(__SCREAMING_SNAKE_CASE  ,					""   )
											_snake_case              =      aggregators_prefix.get(__SCREAMING_SNAKE_CASE  ,					""   )
											_snake_case              =      {
											    "answer": aggregator_prefix + ", ".join(__SCREAMING_SNAKE_CASE   ),
											    "coordinates": coordinates,
											    "cells": [table.iat[coordinate] for coordinate in coordinates],
											}
											if aggregator:
														_snake_case              =      aggregator

											answers.append(__SCREAMING_SNAKE_CASE   )
								if len(__SCREAMING_SNAKE_CASE   ) == 0:
											raise PipelineException("Empty answer"   )
					else:
								_snake_case              =      [{"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]


import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )



		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      self.rust_tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					assert self.tokenizer_ab.add_prefix_space
					assert self.tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]







		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					assert self.rust_tokenizer_ab.add_prefix_space
					assert self.rust_tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]

