






"""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  :						Any					       =							"platform"

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None			)    ->Union[str, Any]:


 if attention_mask is None:
  UpperCAmelCase__ 						=				np.where(input_ids != config.pad_token_id       ,	1       ,	0			)
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
 }


@require_flax
class 	_UpperCamelCase :



    '''simple docstring'''


    def __init__(      self   ,			__lowercase   ,			__lowercase=13   ,			__lowercase=7   ,			__lowercase=True   ,			__lowercase=False   ,			__lowercase=99   ,			__lowercase=16   ,			__lowercase=2   ,			__lowercase=4   ,			__lowercase=4   ,			__lowercase="gelu"   ,			__lowercase=0.1   ,			__lowercase=0.1   ,			__lowercase=20   ,			__lowercase=2   ,			__lowercase=1   ,			__lowercase=0   ,			__lowercase=16   ,			__lowercase=16   ,			__lowercase=0.02   ,			):
     UpperCAmelCase__ 						=				parent
     UpperCAmelCase__ 						=				batch_size
     UpperCAmelCase__ 						=				seq_length
     UpperCAmelCase__ 						=				is_training
     UpperCAmelCase__ 						=				use_labels
     UpperCAmelCase__ 						=				vocab_size
     UpperCAmelCase__ 						=				hidden_size
     UpperCAmelCase__ 						=				num_hidden_layers
     UpperCAmelCase__ 						=				num_attention_heads
     UpperCAmelCase__ 						=				intermediate_size
     UpperCAmelCase__ 						=				hidden_act
     UpperCAmelCase__ 						=				hidden_dropout_prob
     UpperCAmelCase__ 						=				attention_probs_dropout_prob
     UpperCAmelCase__ 						=				max_position_embeddings
     UpperCAmelCase__ 						=				eos_token_id
     UpperCAmelCase__ 						=				pad_token_id
     UpperCAmelCase__ 						=				bos_token_id
     UpperCAmelCase__ 						=				embed_dim
     UpperCAmelCase__ 						=				word_embed_proj_dim
     UpperCAmelCase__ 						=				initializer_range
     UpperCAmelCase__ 						=				False


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

     UpperCAmelCase__ 						=				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=__lowercase   ,			word_embed_proj_dim=self.word_embed_proj_dim   ,			initializer_range=self.initializer_range   ,			use_cache=__lowercase   ,			)
     UpperCAmelCase__ 						=				prepare_opt_inputs_dict(__lowercase   ,			__lowercase			)
     return config, inputs_dict


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.prepare_config_and_inputs()
     return config, inputs_dict


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				20
     UpperCAmelCase__ 						=				model_class_name(__lowercase			)

     UpperCAmelCase__ 						=				inputs_dict["input_ids"]
     UpperCAmelCase__ 						=				inputs_dict["attention_mask"]

     UpperCAmelCase__ 						=				model.init_cache(input_ids.shape[0]   ,			__lowercase			)
     UpperCAmelCase__ 						=				jnp.ones((input_ids.shape[0], max_length)   ,			dtype="""i4"""			)

     UpperCAmelCase__ 						=				jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1			)[None, :]   ,			(input_ids.shape[0], input_ids.shape[-1] - 1)   ,			)
     UpperCAmelCase__ 						=				model(
         input_ids[:, :-1]   ,			attention_mask=__lowercase   ,			past_key_values=__lowercase   ,			position_ids=__lowercase   ,			)

     UpperCAmelCase__ 						=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,			dtype="""i4"""			)
     UpperCAmelCase__ 						=				model(
         input_ids[:, -1:]   ,			attention_mask=__lowercase   ,			past_key_values=outputs_cache.past_key_values   ,			position_ids=__lowercase   ,			)

     UpperCAmelCase__ 						=				model(__lowercase			)

     UpperCAmelCase__ 						=				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 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				20
     UpperCAmelCase__ 						=				model_class_name(__lowercase			)

     UpperCAmelCase__ 						=				(
         inputs_dict["input_ids"],
         inputs_dict["attention_mask"],
     )

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

     UpperCAmelCase__ 						=				model.init_cache(input_ids.shape[0]   ,			__lowercase			)
     UpperCAmelCase__ 						=				jnp.broadcast_to(
         jnp.arange(input_ids.shape[-1] - 1			)[None, :]   ,			(input_ids.shape[0], input_ids.shape[-1] - 1)   ,			)

     UpperCAmelCase__ 						=				model(
         input_ids[:, :-1]   ,			attention_mask=__lowercase   ,			past_key_values=__lowercase   ,			position_ids=__lowercase   ,			)
     UpperCAmelCase__ 						=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,			dtype="""i4"""			)
     UpperCAmelCase__ 						=				model(
         input_ids[:, -1:]   ,			past_key_values=outputs_cache.past_key_values   ,			attention_mask=__lowercase   ,			position_ids=__lowercase   ,			)

     UpperCAmelCase__ 						=				model(__lowercase   ,			attention_mask=__lowercase			)

     UpperCAmelCase__ 						=				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 	_UpperCamelCase ( lowercase__		,     unittest.TestCase		,     lowercase__							):



    '''simple docstring'''


    __lowercase    :   int											=						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
    __lowercase    :   List[Any]											=						() if is_flax_available() else ()


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				FlaxOPTModelTester(self			)


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
      self.model_tester.check_use_cache_forward(__lowercase   ,			__lowercase   ,			__lowercase			)


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				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(__lowercase   ,			__lowercase   ,			__lowercase			)






    @slow
    def 		A__      (      self			):
     for model_class_name in self.all_model_classes:
      UpperCAmelCase__ 						=				model_class_name.from_pretrained("""facebook/opt-125m"""			)
      UpperCAmelCase__ 						=				np.ones((1, 1)			) * model.config.eos_token_id
      UpperCAmelCase__ 						=				model(__lowercase			)
      self.assertIsNotNone(__lowercase			)



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



    '''simple docstring'''


    @slow
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				FlaxOPTModel.from_pretrained("""facebook/opt-350m"""			)
     UpperCAmelCase__ 						=				jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]			)
     UpperCAmelCase__ 						=				model(input_ids=__lowercase			).last_hidden_state
     UpperCAmelCase__ 						=				(1, 11, 512)
     self.assertEqual(output.shape   ,			__lowercase			)
     UpperCAmelCase__ 						=				jnp.array(
         [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]			)
     self.assertTrue(jnp.allclose(output[:, :3, :3]   ,			__lowercase   ,			atol=4e-2			)			)



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



    '''simple docstring'''


    def 		A__      (      self			):
     super().setUp()
     UpperCAmelCase__ 						=				"facebook/opt-350m"






    def 		A__      (      self			):
     UpperCAmelCase__ 						=				FlaxOPTForCausalLM.from_pretrained(self.path_model			)
     UpperCAmelCase__ 						=				GPTaTokenizer.from_pretrained(self.path_model			)

     UpperCAmelCase__ 						=				[
         "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
     UpperCAmelCase__ 						=				tokenizer(__lowercase   ,			return_tensors="""jax"""   ,			padding=__lowercase   ,			add_special_tokens=__lowercase			)
     UpperCAmelCase__ 						=				model(inputs.input_ids   ,			attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
     UpperCAmelCase__ 						=				jnp.array(
         [
             [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
             [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
             [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
             [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
         ]			)
     self.assertTrue(jnp.allclose(__lowercase   ,			__lowercase   ,			atol=4e-2			)			)

     UpperCAmelCase__ 						=				jax.jit(__lowercase			)
     UpperCAmelCase__ 						=				model(inputs.input_ids   ,			attention_mask=inputs.attention_mask			)[0].mean(axis=-1			)
     self.assertTrue(jnp.allclose(__lowercase   ,			__lowercase   ,			atol=4e-2			)			)



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



    '''simple docstring'''


    @property
    def 		A__      (      self			):
     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 		A__      (      self			):
     UpperCAmelCase__ 						=				"facebook/opt-125m"

     UpperCAmelCase__ 						=				[
         "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",
     ]

     UpperCAmelCase__ 						=				[]

     UpperCAmelCase__ 						=				FlaxOPTForCausalLM.from_pretrained(__lowercase			)
     UpperCAmelCase__ 						=				GPTaTokenizer.from_pretrained(__lowercase			)

     for prompt in self.prompts:
      UpperCAmelCase__ 						=				tokenizer(__lowercase   ,			return_tensors="""jax"""			).input_ids

      UpperCAmelCase__ 						=				model.generate(__lowercase   ,			max_length=10			)
      UpperCAmelCase__ 						=				generated_ids[0]

      UpperCAmelCase__ 						=				tokenizer.batch_decode(__lowercase   ,			skip_special_tokens=__lowercase			)
      predicted_outputs += generated_string

     self.assertListEqual(__lowercase   ,			__lowercase			)


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"facebook/opt-350m"

     UpperCAmelCase__ 						=				[
         "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",
     ]

     UpperCAmelCase__ 						=				[]
     UpperCAmelCase__ 						=				FlaxOPTForCausalLM.from_pretrained(__lowercase			)
     UpperCAmelCase__ 						=				GPTaTokenizer.from_pretrained(__lowercase			)

     for prompt in self.prompts:
      UpperCAmelCase__ 						=				tokenizer(__lowercase   ,			return_tensors="""jax"""			).input_ids

      UpperCAmelCase__ 						=				model.generate(__lowercase   ,			max_length=10			)
      UpperCAmelCase__ 						=				generated_ids[0]

      UpperCAmelCase__ 						=				tokenizer.batch_decode(__lowercase   ,			skip_special_tokens=__lowercase			)
      predicted_outputs += generated_string

     self.assertListEqual(__lowercase   ,			__lowercase			)


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"facebook/opt-125m"
     UpperCAmelCase__ 						=				[
         "Today is a beautiful day and I want to thank",
         "In the city of Rome Canaver Canaver Canaver Canaver",
     ]
     UpperCAmelCase__ 						=				FlaxOPTForCausalLM.from_pretrained(__lowercase			)
     UpperCAmelCase__ 						=				GPTaTokenizer.from_pretrained(__lowercase			)
     UpperCAmelCase__ 						=				tokenizer(
         [
             """Today is a beautiful day and I want to""",
             """In the city of""",
         ]   ,			return_tensors="""jax"""   ,			padding=__lowercase   ,			)

     UpperCAmelCase__ 						=				jax.jit(model.generate			)

     UpperCAmelCase__ 						=				jit_generate(inputs["""input_ids"""]   ,			attention_mask=inputs["""attention_mask"""]			).sequences

     UpperCAmelCase__ 						=				tokenizer.batch_decode(__lowercase   ,			skip_special_tokens=__lowercase			)

     self.assertIsNotNone(__lowercase   ,			__lowercase			)






    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"facebook/opt-350m"

     UpperCAmelCase__ 						=				GPTaTokenizer.from_pretrained(__lowercase			)
     UpperCAmelCase__ 						=				FlaxOPTForCausalLM.from_pretrained(__lowercase			)

     UpperCAmelCase__ 						=				"left"

     # use different length sentences to test batching
     UpperCAmelCase__ 						=				[
         "Hello, my dog is a little",
         "Today, I",
     ]

     UpperCAmelCase__ 						=				tokenizer(__lowercase   ,			return_tensors="""jax"""   ,			padding=__lowercase			)
     UpperCAmelCase__ 						=				inputs["input_ids"]

     UpperCAmelCase__ 						=				model.generate(input_ids=__lowercase   ,			attention_mask=inputs["""attention_mask"""]   ,			trace=__lowercase			)

     UpperCAmelCase__ 						=				tokenizer(sentences[0]   ,			return_tensors="""jax"""			).input_ids
     UpperCAmelCase__ 						=				model.generate(input_ids=__lowercase			)

     UpperCAmelCase__ 						=				inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
     UpperCAmelCase__ 						=				tokenizer(sentences[1]   ,			return_tensors="""jax"""			).input_ids
     UpperCAmelCase__ 						=				model.generate(input_ids=__lowercase   ,			max_length=model.config.max_length - num_paddings			)

     UpperCAmelCase__ 						=				tokenizer.batch_decode(outputs[0]   ,			skip_special_tokens=__lowercase			)
     UpperCAmelCase__ 						=				tokenizer.decode(output_non_padded[0][0]   ,			skip_special_tokens=__lowercase			)
     UpperCAmelCase__ 						=				tokenizer.decode(output_padded[0][0]   ,			skip_special_tokens=__lowercase			)

     UpperCAmelCase__ 						=				[
         "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(__lowercase   ,			__lowercase			)
     self.assertListEqual(__lowercase   ,			[non_padded_sentence, padded_sentence]			)










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  (					lowercase__				):






							"""simple docstring"""

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

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

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

																									SCREAMING_SNAKE_CASE__  :					Dict =       pd.DataFrame(tqa_pipeline_input["table"]     )

													return tqa_pipeline_inputs







@add_end_docstrings(lowercase__				)
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    "table,query"

							def __init__(   self					:  List[str]				,						a					:  Optional[int]=TableQuestionAnsweringArgumentHandler()				,						*a					:  List[str]				,						**a					:  Dict     )						->Any:
													super().__init__(*a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       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()     )     )

													SCREAMING_SNAKE_CASE__  :					str =       bool(getattr(self.model.config				,						"aggregation_labels"				,						a     )     ) and bool(
													    getattr(self.model.config				,						"num_aggregation_labels"				,						a     )     )
													SCREAMING_SNAKE_CASE__  :					int =       "tapas" if hasattr(self.model.config				,						"aggregation_labels"     ) else None

							def   A_	(   self					:  List[str]				,						**a					:  Any     )						->Optional[Any]:
													return self.model(**a     )

							def   A_	(   self					:  Any				,						**a					:  Any     )						->Dict:
													if self.framework == "pt":
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					int =       []
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

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

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

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

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

																															SCREAMING_SNAKE_CASE__  :					Dict =       torch.from_numpy(a     ).type(torch.long     ).to(self.device     )

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

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

																									all_logits.append(a     )

																									SCREAMING_SNAKE_CASE__  :					Any =       torch.distributions.Bernoulli(logits=a     )
																									SCREAMING_SNAKE_CASE__  :					Tuple =       dist_per_token.probs * attention_mask_example.type(torch.floataa     ).to(
																									    dist_per_token.probs.device     )

																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       collections.defaultdict(a     )
																									for i, p in enumerate(probabilities.squeeze().tolist()     ):
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       token_type_ids_example[:, 0].tolist()[i]
																															SCREAMING_SNAKE_CASE__  :					Dict =       token_type_ids_example[:, 1].tolist()[i] - 1
																															SCREAMING_SNAKE_CASE__  :					Optional[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(a     )

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

																			SCREAMING_SNAKE_CASE__  :					List[str] =       torch.cat(tuple(a     )				,						0     )

																			return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(a     )				,						0     ))
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

																			SCREAMING_SNAKE_CASE__  :					Tuple =       inputs["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["attention_mask"]
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       inputs["token_type_ids"].numpy()
																			SCREAMING_SNAKE_CASE__  :					Tuple =       None

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

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

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

																															SCREAMING_SNAKE_CASE__  :					Any =       model_labels

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

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

																									all_logits.append(a     )

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

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

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

																			SCREAMING_SNAKE_CASE__  :					List[Any] =       tf.concat(tuple(a     )				,						0     )

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

							def __call__(   self					:  Optional[Any]				,						*a					:  Any				,						**a					:  Optional[int]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					Tuple =       self._args_parser(*a				,						**a     )

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

							def   A_	(   self					:  Any				,						a					:  Dict=None				,						a					:  Dict=None				,						a					:  Union[str, Any]=None				,						**a					:  int     )						->Any:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       {}
													if padding is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padding
													if truncation is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       truncation

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

							def   A_	(   self					:  Any				,						a					:  Any				,						a					:  Union[str, Any]=None				,						a					:  Union[str, Any]=True				,						a					:  Dict=None     )						->int:
													if truncation is None:
																			if self.type == "tapas":
																									SCREAMING_SNAKE_CASE__  :					Any =       "drop_rows_to_fit"
																			else:
																									SCREAMING_SNAKE_CASE__  :					List[str] =       "do_not_truncate"

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[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"     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.tokenizer(a				,						a				,						return_tensors=self.framework				,						truncation=a				,						padding=a     )
													SCREAMING_SNAKE_CASE__  :					str =       table
													return inputs

							def   A_	(   self					:  Any				,						a					:  Tuple				,						a					:  Union[str, Any]=False     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model_inputs.pop("table"     )

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






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

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

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

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