





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 		__a							(	__UpperCamelCase      ):



       def __call__(     self	:   Dict					,			UpperCAmelCase	:   Optional[Any]=None					,			UpperCAmelCase	:   Dict=None					,			**UpperCAmelCase	:   Tuple			):
           # 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_					:	Dict						=   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[Any]						=   [{"""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_					:	List[str]						=   pd.DataFrame(tqa_pipeline_input["""table"""]			)

           return tqa_pipeline_inputs






@add_end_docstrings(__UpperCamelCase      )
class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   int      =		"""table,query"""



       def __init__(     self	:   Tuple					,			UpperCAmelCase	:   List[str]=TableQuestionAnsweringArgumentHandler()					,			*UpperCAmelCase	:   Tuple					,			**UpperCAmelCase	:   Union[str, Any]			):
           super().__init__(*UpperCAmelCase					,			**UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   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_					:	int						=   bool(getattr(self.model.config					,			"""aggregation_labels"""					,			UpperCAmelCase			)			) and bool(
               getattr(self.model.config					,			"""num_aggregation_labels"""					,			UpperCAmelCase			)			)
           lowerCAmelCase_					:	List[Any]						=   """tapas""" if hasattr(self.model.config					,			"""aggregation_labels"""			) else None



       def  A							(     self	:   Any					,			**UpperCAmelCase	:   List[str]			):
           return self.model(**UpperCAmelCase			)



       def  A							(     self	:   Tuple					,			**UpperCAmelCase	:   Any			):
           if self.framework == "pt":
               lowerCAmelCase_					:	List[str]						=   []
               lowerCAmelCase_					:	int						=   []
               lowerCAmelCase_					:	int						=   None
               lowerCAmelCase_					:	List[Any]						=   inputs["""input_ids"""].shape[0]

               lowerCAmelCase_					:	str						=   inputs["""input_ids"""].to(self.device			)
               lowerCAmelCase_					:	Optional[Any]						=   inputs["""attention_mask"""].to(self.device			)
               lowerCAmelCase_					:	Tuple						=   inputs["""token_type_ids"""].to(self.device			)
               lowerCAmelCase_					:	Union[str, 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_					:	int						=   token_type_ids_example[:, 3]  # shape (seq_len,)
                       lowerCAmelCase_					:	Tuple						=   np.zeros_like(prev_labels_example.cpu().numpy()			)  # shape (seq_len,)

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

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

                       lowerCAmelCase_					:	int						=   torch.from_numpy(UpperCAmelCase			).type(torch.long			).to(self.device			)

                   lowerCAmelCase_					:	Dict						=   input_ids[index]
                   lowerCAmelCase_					:	str						=   attention_mask[index]  # shape (seq_len,)
                   lowerCAmelCase_					:	Dict						=   token_type_ids[index]  # shape (seq_len, 7)
                   lowerCAmelCase_					:	List[str]						=   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_					:	Union[str, Any]						=   outputs.logits

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

                   all_logits.append(UpperCAmelCase			)

                   lowerCAmelCase_					:	Optional[Any]						=   torch.distributions.Bernoulli(logits=UpperCAmelCase			)
                   lowerCAmelCase_					:	Optional[int]						=   dist_per_token.probs * attention_mask_example.type(torch.floataa			).to(
                       dist_per_token.probs.device			)

                   lowerCAmelCase_					:	Optional[int]						=   collections.defaultdict(UpperCAmelCase			)
                   for i, p in enumerate(probabilities.squeeze().tolist()			):
                       lowerCAmelCase_					:	Union[str, Any]						=   token_type_ids_example[:, 0].tolist()[i]
                       lowerCAmelCase_					:	List[str]						=   token_type_ids_example[:, 1].tolist()[i] - 1
                       lowerCAmelCase_					:	Union[str, 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_					:	int						=   {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_					:	Tuple						=   []
               lowerCAmelCase_					:	Optional[Any]						=   []
               lowerCAmelCase_					:	List[str]						=   None
               lowerCAmelCase_					:	str						=   inputs["""input_ids"""].shape[0]

               lowerCAmelCase_					:	Optional[int]						=   inputs["""input_ids"""]
               lowerCAmelCase_					:	Optional[Any]						=   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_					:	Optional[int]						=   token_type_ids_example[:, 3]  # shape (seq_len,)
                       lowerCAmelCase_					:	Tuple						=   np.zeros_like(UpperCAmelCase					,			dtype=np.intaa			)  # shape (seq_len,)

                       lowerCAmelCase_					:	List[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_					:	Tuple						=   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_					:	Optional[Any]						=   int(prev_answers[(col_id, row_id)]			)

                       lowerCAmelCase_					:	Tuple						=   model_labels

                   lowerCAmelCase_					:	Optional[int]						=   input_ids[index]
                   lowerCAmelCase_					:	List[str]						=   attention_mask[index]  # shape (seq_len,)
                   lowerCAmelCase_					:	Dict						=   token_type_ids[index]  # shape (seq_len, 7)
                   lowerCAmelCase_					:	List[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_					:	Optional[Any]						=   outputs.logits

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

                   all_logits.append(UpperCAmelCase			)

                   lowerCAmelCase_					:	List[str]						=   tfp.distributions.Bernoulli(logits=UpperCAmelCase			)
                   lowerCAmelCase_					:	Tuple						=   dist_per_token.probs_parameter() * tf.cast(UpperCAmelCase					,			tf.floataa			)

                   lowerCAmelCase_					:	List[str]						=   collections.defaultdict(UpperCAmelCase			)
                   lowerCAmelCase_					:	str						=   token_type_ids_example
                   for i, p in enumerate(tf.squeeze(UpperCAmelCase			).numpy().tolist()			):
                       lowerCAmelCase_					:	Dict						=   token_type_ids_example[:, 0].tolist()[i]
                       lowerCAmelCase_					:	Optional[Any]						=   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_					:	Optional[int]						=   {key: np.array(coords_to_probs[key]			).mean() > 0.5 for key in coords_to_probs}

               lowerCAmelCase_					:	Dict						=   tf.concat(tuple(UpperCAmelCase			)					,			0			)

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



       def __call__(     self	:   List[Any]					,			*UpperCAmelCase	:   Dict					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   self._args_parser(*UpperCAmelCase					,			**UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[int]						=   super().__call__(UpperCAmelCase					,			**UpperCAmelCase			)
           if len(UpperCAmelCase			) == 1:
               return results[0]
           return results



       def  A							(     self	:   int					,			UpperCAmelCase	:   Optional[int]=None					,			UpperCAmelCase	:   List[Any]=None					,			UpperCAmelCase	:   int=None					,			**UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	Optional[Any]						=   {}
           if padding is not None:
               lowerCAmelCase_					:	Dict						=   padding
           if truncation is not None:
               lowerCAmelCase_					:	Dict						=   truncation

           lowerCAmelCase_					:	int						=   {}
           if sequential is not None:
               lowerCAmelCase_					:	Dict						=   sequential
           return preprocess_params, forward_params, {}



       def  A							(     self	:   int					,			UpperCAmelCase	:   List[Any]					,			UpperCAmelCase	:   List[Any]=None					,			UpperCAmelCase	:   Tuple=True					,			UpperCAmelCase	:   Union[str, Any]=None			):
           if truncation is None:
               if self.type == "tapas":
                   lowerCAmelCase_					:	Dict						=   """drop_rows_to_fit"""
               else:
                   lowerCAmelCase_					:	List[str]						=   """do_not_truncate"""

           lowerCAmelCase_		,		lowerCAmelCase_					:	Dict						=   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_					:	List[str]						=   self.tokenizer(UpperCAmelCase					,			UpperCAmelCase					,			return_tensors=self.framework					,			truncation=UpperCAmelCase					,			padding=UpperCAmelCase			)
           lowerCAmelCase_					:	List[Any]						=   table
           return inputs



       def  A							(     self	:   Optional[Any]					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   int=False			):
           lowerCAmelCase_					:	int						=   model_inputs.pop("""table"""			)

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



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

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

                   answers.append(UpperCAmelCase			)
               if len(UpperCAmelCase			) == 0:
                   raise PipelineException("""Empty answer"""			)
           else:
               lowerCAmelCase_					:	Tuple						=   [{"""answer""": answer} for answer in self.tokenizer.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase			)]

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









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


