



'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A			:   Optional[Any]			=      """platform"""

      import jax
      import jax.numpy as jnp

      from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_=None					,						lowerCamelCase_=None							):
   if attention_mask is None:
      snake_case   :							Any						  =np.where(input_ids != config.pad_token_id					,						1					,						0							)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
   }





@require_flax
class 							lowerCAmelCase_		:





    def __init__(    self    :    List[Any],    _snake_case    :    Optional[int],    _snake_case    :    int=13,    _snake_case    :    List[Any]=7,    _snake_case    :    Optional[Any]=True,    _snake_case    :    List[Any]=False,    _snake_case    :    Optional[Any]=99,    _snake_case    :    Any=16,    _snake_case    :    List[Any]=2,    _snake_case    :    int=4,    _snake_case    :    Any=4,    _snake_case    :    str="gelu",    _snake_case    :    str=0.1,    _snake_case    :    Any=0.1,    _snake_case    :    Dict=20,    _snake_case    :    Dict=2,    _snake_case    :    Tuple=1,    _snake_case    :    List[Any]=0,    _snake_case    :    str=16,    _snake_case    :    Any=16,    _snake_case    :    Union[str, Any]=0.02,    ):



       '''simple docstring'''







       snake_case   :							Union[str, Any]						  =parent
       snake_case   :							Tuple						  =batch_size
       snake_case   :							int						  =seq_length
       snake_case   :							Any						  =is_training
       snake_case   :							Union[str, Any]						  =use_labels
       snake_case   :							Tuple						  =vocab_size
       snake_case   :							Union[str, Any]						  =hidden_size
       snake_case   :							Union[str, Any]						  =num_hidden_layers
       snake_case   :							str						  =num_attention_heads
       snake_case   :							Union[str, Any]						  =intermediate_size
       snake_case   :							List[str]						  =hidden_act
       snake_case   :							Dict						  =hidden_dropout_prob
       snake_case   :							Any						  =attention_probs_dropout_prob
       snake_case   :							Optional[Any]						  =max_position_embeddings
       snake_case   :							Any						  =eos_token_id
       snake_case   :							List[str]						  =pad_token_id
       snake_case   :							int						  =bos_token_id
       snake_case   :							Any						  =embed_dim
       snake_case   :							Any						  =word_embed_proj_dim
       snake_case   :							str						  =initializer_range
       snake_case   :							str						  =False





    def 					__snake_case       (    self    :    Any     ):



       '''simple docstring'''







       snake_case   :							int						  =np.clip(ids_tensor([self.batch_size, self.seq_length - 1],    self.vocab_size     ),    3,    self.vocab_size     )
       snake_case   :							int						  =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),    dtype=np.intaa     )),    -1     )

       snake_case   :							int						  =OPTConfig(
           vocab_size=self.vocab_size,    hidden_size=self.hidden_size,    num_hidden_layers=self.num_hidden_layers,    num_attention_heads=self.num_attention_heads,    ffn_dim=self.intermediate_size,    dropout=self.hidden_dropout_prob,    attention_dropout=self.attention_probs_dropout_prob,    max_position_embeddings=self.max_position_embeddings,    eos_token_id=self.eos_token_id,    bos_token_id=self.bos_token_id,    pad_token_id=self.pad_token_id,    embed_dim=self.embed_dim,    is_encoder_decoder=_snake_case,    word_embed_proj_dim=self.word_embed_proj_dim,    initializer_range=self.initializer_range,    use_cache=_snake_case,    )
       snake_case   :							Union[str, Any]						  =prepare_opt_inputs_dict(_snake_case,    _snake_case     )
       return config, inputs_dict





    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







       snake_case  ,    snake_case   :							Union[str, Any]						  =self.prepare_config_and_inputs()
       return config, inputs_dict





    def 					__snake_case       (    self    :    Any,    _snake_case    :    int,    _snake_case    :    Optional[int],    _snake_case    :    Dict     ):



       '''simple docstring'''







       snake_case   :							int						  =20
       snake_case   :							List[str]						  =model_class_name(_snake_case     )

       snake_case   :							Tuple						  =inputs_dict['''input_ids''']
       snake_case   :							List[Any]						  =inputs_dict['''attention_mask''']

       snake_case   :							Tuple						  =model.init_cache(input_ids.shape[0],    _snake_case     )
       snake_case   :							List[str]						  =jnp.ones((input_ids.shape[0], max_length),    dtype='''i4'''     )

       snake_case   :							str						  =jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1     )[None, :],    (input_ids.shape[0], input_ids.shape[-1] - 1),    )
       snake_case   :							Dict						  =model(
           input_ids[:, :-1],    attention_mask=_snake_case,    past_key_values=_snake_case,    position_ids=_snake_case,    )

       snake_case   :							Any						  =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],    dtype='''i4'''     )
       snake_case   :							Optional[Any]						  =model(
           input_ids[:, -1:],    attention_mask=_snake_case,    past_key_values=outputs_cache.past_key_values,    position_ids=_snake_case,    )

       snake_case   :							List[Any]						  =model(_snake_case     )

       snake_case   :							str						  =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
       self.parent.assertTrue(diff < 1E-3,    msg=f'''Max diff is {diff}'''     )






    def 					__snake_case       (    self    :    Tuple,    _snake_case    :    Any,    _snake_case    :    Dict,    _snake_case    :    Optional[Any]     ):



       '''simple docstring'''







       snake_case   :							List[str]						  =20
       snake_case   :							List[Any]						  =model_class_name(_snake_case     )

       snake_case  ,    snake_case   :							Dict						  =(
           inputs_dict['''input_ids'''],
           inputs_dict['''attention_mask'''],
       )

       snake_case   :							int						  =jnp.concatenate(
           [
               attention_mask,
               jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
           ],    axis=-1,    )

       snake_case   :							Union[str, Any]						  =model.init_cache(input_ids.shape[0],    _snake_case     )
       snake_case   :							List[str]						  =jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1     )[None, :],    (input_ids.shape[0], input_ids.shape[-1] - 1),    )

       snake_case   :							Optional[Any]						  =model(
           input_ids[:, :-1],    attention_mask=_snake_case,    past_key_values=_snake_case,    position_ids=_snake_case,    )
       snake_case   :							Any						  =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],    dtype='''i4'''     )
       snake_case   :							Any						  =model(
           input_ids[:, -1:],    past_key_values=outputs_cache.past_key_values,    attention_mask=_snake_case,    position_ids=_snake_case,    )

       snake_case   :							Optional[int]						  =model(_snake_case,    attention_mask=_snake_case     )

       snake_case   :							Optional[int]						  =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
       self.parent.assertTrue(diff < 1E-3,    msg=f'''Max diff is {diff}'''     )





@require_flax
class 							lowerCAmelCase_		(	a_  ,      unittest.TestCase  ,      a_						):
    __UpperCAmelCase							=	(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    __UpperCAmelCase							=	() if is_flax_available() else ()





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =FlaxOPTModelTester(self     )





    def 					__snake_case       (    self    :    Dict     ):



       '''simple docstring'''







       snake_case  ,    snake_case   :							str						  =self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward(_snake_case,    _snake_case,    _snake_case     )





    def 					__snake_case       (    self    :    Dict     ):



       '''simple docstring'''







       snake_case  ,    snake_case   :							Optional[Any]						  =self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
          self.model_tester.check_use_cache_forward_with_attn_mask(_snake_case,    _snake_case,    _snake_case     )






    @slow
    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       for model_class_name in self.all_model_classes:
          snake_case   :							Union[str, Any]						  =model_class_name.from_pretrained('''facebook/opt-125m'''     )
          snake_case   :							Tuple						  =np.ones((1, 1)     ) * model.config.eos_token_id
          snake_case   :							List[str]						  =model(_snake_case     )
          self.assertIsNotNone(_snake_case     )





@require_sentencepiece
@require_flax
class 							lowerCAmelCase_		(	unittest.TestCase						):





    @slow
    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =FlaxOPTModel.from_pretrained('''facebook/opt-350m'''     )
       snake_case   :							Tuple						  =jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     )
       snake_case   :							List[Any]						  =model(input_ids=_snake_case     ).last_hidden_state
       snake_case   :							Optional[int]						  =(1, 11, 512)
       self.assertEqual(output.shape,    _snake_case     )
       snake_case   :							Tuple						  =jnp.array(
           [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]     )
       self.assertTrue(jnp.allclose(output[:, :3, :3],    _snake_case,    atol=4E-2     )     )





@require_flax
@slow
class 							lowerCAmelCase_		(	unittest.TestCase						):





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       super().setUp()
       snake_case   :							List[Any]						  ='''facebook/opt-350m'''






    def 					__snake_case       (    self    :    Dict     ):



       '''simple docstring'''







       snake_case   :							str						  =FlaxOPTForCausalLM.from_pretrained(self.path_model     )
       snake_case   :							Any						  =GPTaTokenizer.from_pretrained(self.path_model     )

       snake_case   :							Tuple						  =[
           '''Today is a beautiful day and I want to''',
           '''In the city of''',
           '''Paris is the capital of France and''',
           '''Computers and mobile phones have taken''',
       ]
       # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
       snake_case   :							Optional[int]						  =tokenizer(_snake_case,    return_tensors='''jax''',    padding=_snake_case,    add_special_tokens=_snake_case     )
       snake_case   :							Optional[int]						  =model(inputs.input_ids,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
       snake_case   :							Optional[Any]						  =jnp.array(
           [
               [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
               [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
               [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
               [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
           ]     )
       self.assertTrue(jnp.allclose(_snake_case,    _snake_case,    atol=4E-2     )     )

       snake_case   :							Union[str, Any]						  =jax.jit(_snake_case     )
       snake_case   :							Union[str, Any]						  =model(inputs.input_ids,    attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
       self.assertTrue(jnp.allclose(_snake_case,    _snake_case,    atol=4E-2     )     )





@require_flax
@slow
class 							lowerCAmelCase_		(	unittest.TestCase						):





    @property
    def 					__snake_case       (    self    :    str     ):



       '''simple docstring'''







       return [
           "Today is a beautiful day and I want",
           "In the city of",
           "Paris is the capital of France and",
           "Computers and mobile phones have taken",
       ]





    def 					__snake_case       (    self    :    int     ):



       '''simple docstring'''







       snake_case   :							List[str]						  ='''facebook/opt-125m'''

       snake_case   :							List[Any]						  =[
           '''Today is a beautiful day and I want to''',
           '''In the city of New York, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       snake_case   :							Optional[Any]						  =[]

       snake_case   :							Tuple						  =FlaxOPTForCausalLM.from_pretrained(_snake_case     )
       snake_case   :							Tuple						  =GPTaTokenizer.from_pretrained(_snake_case     )

       for prompt in self.prompts:
          snake_case   :							Any						  =tokenizer(_snake_case,    return_tensors='''jax'''     ).input_ids

          snake_case   :							Optional[int]						  =model.generate(_snake_case,    max_length=10     )
          snake_case   :							Optional[Any]						  =generated_ids[0]

          snake_case   :							Union[str, Any]						  =tokenizer.batch_decode(_snake_case,    skip_special_tokens=_snake_case     )
          predicted_outputs += generated_string

       self.assertListEqual(_snake_case,    _snake_case     )





    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							Union[str, Any]						  ='''facebook/opt-350m'''

       snake_case   :							Any						  =[
           '''Today is a beautiful day and I want to''',
           '''In the city of San Francisco, the city''',
           '''Paris is the capital of France and the capital''',
           '''Computers and mobile phones have taken over the''',
       ]

       snake_case   :							Union[str, Any]						  =[]
       snake_case   :							str						  =FlaxOPTForCausalLM.from_pretrained(_snake_case     )
       snake_case   :							int						  =GPTaTokenizer.from_pretrained(_snake_case     )

       for prompt in self.prompts:
          snake_case   :							Union[str, Any]						  =tokenizer(_snake_case,    return_tensors='''jax'''     ).input_ids

          snake_case   :							Optional[int]						  =model.generate(_snake_case,    max_length=10     )
          snake_case   :							Optional[int]						  =generated_ids[0]

          snake_case   :							List[str]						  =tokenizer.batch_decode(_snake_case,    skip_special_tokens=_snake_case     )
          predicted_outputs += generated_string

       self.assertListEqual(_snake_case,    _snake_case     )





    def 					__snake_case       (    self    :    Tuple     ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  ='''facebook/opt-125m'''
       snake_case   :							Optional[int]						  =[
           '''Today is a beautiful day and I want to thank''',
           '''In the city of Rome Canaver Canaver Canaver Canaver''',
       ]
       snake_case   :							str						  =FlaxOPTForCausalLM.from_pretrained(_snake_case     )
       snake_case   :							Optional[int]						  =GPTaTokenizer.from_pretrained(_snake_case     )
       snake_case   :							List[str]						  =tokenizer(
           [
               '''Today is a beautiful day and I want to''',
               '''In the city of''',
           ],    return_tensors='''jax''',    padding=_snake_case,    )

       snake_case   :							Dict						  =jax.jit(model.generate     )

       snake_case   :							Any						  =jit_generate(inputs['''input_ids'''],    attention_mask=inputs['''attention_mask''']     ).sequences

       snake_case   :							Any						  =tokenizer.batch_decode(_snake_case,    skip_special_tokens=_snake_case     )

       self.assertIsNotNone(_snake_case,    _snake_case     )






    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							str						  ='''facebook/opt-350m'''

       snake_case   :							str						  =GPTaTokenizer.from_pretrained(_snake_case     )
       snake_case   :							Dict						  =FlaxOPTForCausalLM.from_pretrained(_snake_case     )

       snake_case   :							int						  ='''left'''

       # use different length sentences to test batching
       snake_case   :							Optional[int]						  =[
           '''Hello, my dog is a little''',
           '''Today, I''',
       ]

       snake_case   :							Dict						  =tokenizer(_snake_case,    return_tensors='''jax''',    padding=_snake_case     )
       snake_case   :							Any						  =inputs['''input_ids''']

       snake_case   :							Dict						  =model.generate(input_ids=_snake_case,    attention_mask=inputs['''attention_mask'''],    trace=_snake_case     )

       snake_case   :							Any						  =tokenizer(sentences[0],    return_tensors='''jax'''     ).input_ids
       snake_case   :							Optional[Any]						  =model.generate(input_ids=_snake_case     )

       snake_case   :							List[Any]						  =inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
       snake_case   :							Any						  =tokenizer(sentences[1],    return_tensors='''jax'''     ).input_ids
       snake_case   :							Dict						  =model.generate(input_ids=_snake_case,    max_length=model.config.max_length - num_paddings     )

       snake_case   :							Dict						  =tokenizer.batch_decode(outputs[0],    skip_special_tokens=_snake_case     )
       snake_case   :							List[Any]						  =tokenizer.decode(output_non_padded[0][0],    skip_special_tokens=_snake_case     )
       snake_case   :							Union[str, Any]						  =tokenizer.decode(output_padded[0][0],    skip_special_tokens=_snake_case     )

       snake_case   :							Tuple						  =[
           '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
           '''Today, I was in the middle of a conversation with a friend about the''',
       ]
       self.assertListEqual(_snake_case,    _snake_case     )
       self.assertListEqual(_snake_case,    [non_padded_sentence, padded_sentence]     )










'''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 							lowerCAmelCase_		(	a_						):





    def __call__(    self    :    str,    _snake_case    :    Dict=None,    _snake_case    :    str=None,    **_snake_case    :    Any     ):



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

             if table[0].get('''query'''     ) is not None and table[0].get('''table'''     ) is not None:
                snake_case   :							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(_snake_case,    _snake_case     ) or isinstance(_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(_snake_case     )})'''     )
       else:
          snake_case   :							Union[str, 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.'''     )

             snake_case   :							Any						  =pd.DataFrame(tqa_pipeline_input['''table''']     )

       return tqa_pipeline_inputs





@add_end_docstrings(a_						)
class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	'table,query'





    def __init__(    self    :    Any,    _snake_case    :    Any=TableQuestionAnsweringArgumentHandler(),    *_snake_case    :    List[Any],    **_snake_case    :    Dict     ):



       '''simple docstring'''







       super().__init__(*_snake_case,    **_snake_case     )
       snake_case   :							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()     )     )

       snake_case   :							Dict						  =bool(getattr(self.model.config,    '''aggregation_labels''',    _snake_case     )     ) and bool(
           getattr(self.model.config,    '''num_aggregation_labels''',    _snake_case     )     )
       snake_case   :							List[str]						  ='''tapas''' if hasattr(self.model.config,    '''aggregation_labels'''     ) else None





    def 					__snake_case       (    self    :    Dict,    **_snake_case    :    Tuple     ):



       '''simple docstring'''







       return self.model(**_snake_case     )





    def 					__snake_case       (    self    :    List[Any],    **_snake_case    :    List[str]     ):



       '''simple docstring'''







       if self.framework == "pt":
          snake_case   :							str						  =[]
          snake_case   :							str						  =[]
          snake_case   :							Optional[int]						  =None
          snake_case   :							List[str]						  =inputs['''input_ids'''].shape[0]

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

          for index in range(_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   :							Union[str, Any]						  =token_type_ids_example[:, 3]  # shape (seq_len,)
                snake_case   :							Any						  =np.zeros_like(prev_labels_example.cpu().numpy()     )  # shape (seq_len,)

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

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

                snake_case   :							List[Any]						  =torch.from_numpy(_snake_case     ).type(torch.long     ).to(self.device     )

             snake_case   :							Tuple						  =input_ids[index]
             snake_case   :							Optional[int]						  =attention_mask[index]  # shape (seq_len,)
             snake_case   :							List[Any]						  =token_type_ids[index]  # shape (seq_len, 7)
             snake_case   :							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     ),    )
             snake_case   :							int						  =outputs.logits

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

             all_logits.append(_snake_case     )

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

             snake_case   :							Dict						  =collections.defaultdict(_snake_case     )
             for i, p in enumerate(probabilities.squeeze().tolist()     ):
                snake_case   :							int						  =token_type_ids_example[:, 0].tolist()[i]
                snake_case   :							Optional[int]						  =token_type_ids_example[:, 1].tolist()[i] - 1
                snake_case   :							Dict						  =token_type_ids_example[:, 2].tolist()[i] - 1
                if col >= 0 and row >= 0 and segment_id == 1:
                   coords_to_probs[(col, row)].append(_snake_case     )

             snake_case   :							Union[str, Any]						  ={key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

          snake_case   :							Tuple						  =torch.cat(tuple(_snake_case     ),    0     )

          return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_snake_case     ),    0     ))
       else:
          snake_case   :							Tuple						  =[]
          snake_case   :							Optional[int]						  =[]
          snake_case   :							str						  =None
          snake_case   :							Dict						  =inputs['''input_ids'''].shape[0]

          snake_case   :							Optional[Any]						  =inputs['''input_ids''']
          snake_case   :							Optional[int]						  =inputs['''attention_mask''']
          snake_case   :							Union[str, Any]						  =inputs['''token_type_ids'''].numpy()
          snake_case   :							Tuple						  =None

          for index in range(_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   :							Any						  =token_type_ids_example[:, 3]  # shape (seq_len,)
                snake_case   :							str						  =np.zeros_like(_snake_case,    dtype=np.intaa     )  # shape (seq_len,)

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

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

                snake_case   :							Union[str, Any]						  =model_labels

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

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

             all_logits.append(_snake_case     )

             snake_case   :							int						  =tfp.distributions.Bernoulli(logits=_snake_case     )
             snake_case   :							Union[str, Any]						  =dist_per_token.probs_parameter() * tf.cast(_snake_case,    tf.floataa     )

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

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

          snake_case   :							Dict						  =tf.concat(tuple(_snake_case     ),    0     )

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





    def __call__(    self    :    List[str],    *_snake_case    :    Any,    **_snake_case    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							Tuple						  =self._args_parser(*_snake_case,    **_snake_case     )

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





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    Union[str, Any]=None,    _snake_case    :    Union[str, Any]=None,    _snake_case    :    List[Any]=None,    **_snake_case    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  ={}
       if padding is not None:
          snake_case   :							List[Any]						  =padding
       if truncation is not None:
          snake_case   :							int						  =truncation

       snake_case   :							Any						  ={}
       if sequential is not None:
          snake_case   :							int						  =sequential
       return preprocess_params, forward_params, {}





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    Dict,    _snake_case    :    List[Any]=None,    _snake_case    :    Optional[int]=True,    _snake_case    :    Dict=None     ):



       '''simple docstring'''







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

       snake_case  ,    snake_case   :							Optional[int]						  =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   :							Dict						  =self.tokenizer(_snake_case,    _snake_case,    return_tensors=self.framework,    truncation=_snake_case,    padding=_snake_case     )
       snake_case   :							List[Any]						  =table
       return inputs





    def 					__snake_case       (    self    :    List[str],    _snake_case    :    List[Any],    _snake_case    :    Any=False     ):



       '''simple docstring'''







       snake_case   :							Union[str, Any]						  =model_inputs.pop('''table'''     )

       if self.type == "tapas":
          if sequential:
             snake_case   :							Dict						  =self.sequential_inference(**_snake_case     )
          else:
             snake_case   :							Optional[int]						  =self.batch_inference(**_snake_case     )
       else:
          snake_case   :							str						  =self.model.generate(**_snake_case     )
       snake_case   :							int						  ={'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
       return model_outputs






    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    Optional[Any]     ):



       '''simple docstring'''







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

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

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

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





