

"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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     :    Union[str, Any]    =		'platform'

	import jax
	import jax.numpy as jnp

	from transformers import AutoTokenizer
	from transformers.models.mbart.modeling_flax_mbart import (
	    FlaxMBartForConditionalGeneration,
	    FlaxMBartForQuestionAnswering,
	    FlaxMBartForSequenceClassification,
	    FlaxMBartModel,
	    shift_tokens_right,
	)
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__=None			,							lowercase__=None			,							lowercase__=None			,							lowercase__=None			,							lowercase__=None			,							lowercase__=None			,							):
					"""simple docstring"""
					if attention_mask is None:
										A			=					np.where(input_ids != config.pad_token_id			,							1			,							0      )
					if decoder_attention_mask is None:
										A			=					np.where(decoder_input_ids != config.pad_token_id			,							1			,							0      )
					if head_mask is None:
										A			=					np.ones((config.encoder_layers, config.encoder_attention_heads)      )
					if decoder_head_mask is None:
										A			=					np.ones((config.decoder_layers, config.decoder_attention_heads)      )
					if cross_attn_head_mask is None:
										A			=					np.ones((config.decoder_layers, config.decoder_attention_heads)      )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					}





class 			__UpperCamelCase (   unittest.TestCase						):


		def __init__(self :		str     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=1_3     ,     __SCREAMING_SNAKE_CASE :		Dict=7     ,     __SCREAMING_SNAKE_CASE :		List[Any]=True     ,     __SCREAMING_SNAKE_CASE :		int=False     ,     __SCREAMING_SNAKE_CASE :		int=9_9     ,     __SCREAMING_SNAKE_CASE :		str=1_6     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=2     ,     __SCREAMING_SNAKE_CASE :		int=4     ,     __SCREAMING_SNAKE_CASE :		List[str]=4     ,     __SCREAMING_SNAKE_CASE :		Optional[int]="gelu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=0.1     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0.1     ,     __SCREAMING_SNAKE_CASE :		List[Any]=3_2     ,     __SCREAMING_SNAKE_CASE :		Dict=2     ,     __SCREAMING_SNAKE_CASE :		List[str]=1     ,     __SCREAMING_SNAKE_CASE :		List[Any]=0     ,     __SCREAMING_SNAKE_CASE :		str=2     ,     __SCREAMING_SNAKE_CASE :		Tuple=0.0_2     ,     ):
							A			=					parent
							A			=					batch_size
							A			=					seq_length
							A			=					is_training
							A			=					use_labels
							A			=					vocab_size
							A			=					hidden_size
							A			=					num_hidden_layers
							A			=					num_attention_heads
							A			=					intermediate_size
							A			=					hidden_act
							A			=					hidden_dropout_prob
							A			=					attention_probs_dropout_prob
							A			=					max_position_embeddings
							A			=					eos_token_id
							A			=					pad_token_id
							A			=					bos_token_id
							A			=					decoder_start_token_id
							A			=					initializer_range


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

							A			=					shift_tokens_right(__SCREAMING_SNAKE_CASE     ,     1)

							A			=					MBartConfig(
							    vocab_size=self.vocab_size     ,     d_model=self.hidden_size     ,     encoder_layers=self.num_hidden_layers     ,     decoder_layers=self.num_hidden_layers     ,     encoder_attention_heads=self.num_attention_heads     ,     decoder_attention_heads=self.num_attention_heads     ,     encoder_ffn_dim=self.intermediate_size     ,     decoder_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     ,     decoder_start_token_id=self.decoder_start_token_id     ,     initializer_range=self.initializer_range     ,     use_cache=__SCREAMING_SNAKE_CASE     ,     )
							A			=					prepare_mbart_inputs_dict(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							return config, inputs_dict


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			,    A			=					self.prepare_config_and_inputs()
							return config, inputs_dict


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Dict     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		Dict):
							A			=					2_0
							A			=					model_class_name(__SCREAMING_SNAKE_CASE)

							A			=					model.encode(inputs_dict["input_ids"])

							A			,    A			=					(
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							A			=					model.init_cache(decoder_input_ids.shape[0]     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,     dtype="i4")

							A			=					jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]     ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,     )
							A			=					model.decode(
							    decoder_input_ids[:, :-1]     ,     __SCREAMING_SNAKE_CASE     ,     decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,     past_key_values=__SCREAMING_SNAKE_CASE     ,     decoder_position_ids=__SCREAMING_SNAKE_CASE     ,     )

							A			=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,     dtype="i4")
							A			=					model.decode(
							    decoder_input_ids[:, -1:]     ,     __SCREAMING_SNAKE_CASE     ,     decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,     past_key_values=outputs_cache.past_key_values     ,     decoder_position_ids=__SCREAMING_SNAKE_CASE     ,     )

							A			=					model.decode(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							A			=					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 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		List[Any]):
							A			=					2_0
							A			=					model_class_name(__SCREAMING_SNAKE_CASE)

							A			=					model.encode(inputs_dict["input_ids"])

							A			,    A			=					(
							    inputs_dict["decoder_input_ids"],
							    inputs_dict["decoder_attention_mask"],
							)

							A			=					jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
							    ]     ,     axis=-1     ,     )

							A			=					model.init_cache(decoder_input_ids.shape[0]     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]     ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,     )

							A			=					model.decode(
							    decoder_input_ids[:, :-1]     ,     __SCREAMING_SNAKE_CASE     ,     decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,     past_key_values=__SCREAMING_SNAKE_CASE     ,     decoder_position_ids=__SCREAMING_SNAKE_CASE     ,     )
							A			=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,     dtype="i4")
							A			=					model.decode(
							    decoder_input_ids[:, -1:]     ,     __SCREAMING_SNAKE_CASE     ,     past_key_values=outputs_cache.past_key_values     ,     decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,     decoder_position_ids=__SCREAMING_SNAKE_CASE     ,     )

							A			=					model.decode(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     decoder_attention_mask=__SCREAMING_SNAKE_CASE)

							A			=					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 (   unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    99


		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							A			=					np.array(
							    [
							        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
							        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
							        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
							        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
							        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
							        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
							        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
							        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
							        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
							        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
							        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
							        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
							        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
							    ]     ,     dtype=np.intaa     ,     )

							A			=					input_ids.shape[0]
							A			=					MBartConfig(
							    vocab_size=self.vocab_size     ,     d_model=2_4     ,     encoder_layers=2     ,     decoder_layers=2     ,     encoder_attention_heads=2     ,     decoder_attention_heads=2     ,     encoder_ffn_dim=3_2     ,     decoder_ffn_dim=3_2     ,     max_position_embeddings=4_8     ,     eos_token_id=2     ,     pad_token_id=1     ,     bos_token_id=0     ,     )
							return config, input_ids, batch_size


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			,    A			,    A			=					self._get_config_and_data()
							A			=					FlaxMBartForSequenceClassification(__SCREAMING_SNAKE_CASE)
							A			=					model(input_ids=__SCREAMING_SNAKE_CASE     ,     decoder_input_ids=__SCREAMING_SNAKE_CASE)
							A			=					(batch_size, config.num_labels)
							self.assertEqual(outputs["logits"].shape     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			,    A			,    A			=					self._get_config_and_data()
							A			=					FlaxMBartForQuestionAnswering(__SCREAMING_SNAKE_CASE)
							A			=					model(input_ids=__SCREAMING_SNAKE_CASE)

							self.assertEqual(outputs["start_logits"].shape     ,     input_ids.shape)
							self.assertEqual(outputs["end_logits"].shape     ,     input_ids.shape)


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							A			,    A			,    A			=					self._get_config_and_data()
							A			=					FlaxMBartForConditionalGeneration(__SCREAMING_SNAKE_CASE)
							A			=					lm_model(input_ids=__SCREAMING_SNAKE_CASE)
							A			=					(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["logits"].shape     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					MBartConfig(
							    vocab_size=self.vocab_size     ,     d_model=1_4     ,     encoder_layers=2     ,     decoder_layers=2     ,     encoder_attention_heads=2     ,     decoder_attention_heads=2     ,     encoder_ffn_dim=8     ,     decoder_ffn_dim=8     ,     max_position_embeddings=4_8     ,     )
							A			=					FlaxMBartForConditionalGeneration(__SCREAMING_SNAKE_CASE)
							A			=					np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]     ,     dtype=np.intaa)
							A			=					np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]     ,     dtype=np.intaa)
							A			=					lm_model(input_ids=__SCREAMING_SNAKE_CASE     ,     decoder_input_ids=__SCREAMING_SNAKE_CASE)
							A			=					(*summary.shape, config.vocab_size)
							self.assertEqual(outputs["logits"].shape     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]     ,     dtype=np.intaa)
							A			=					shift_tokens_right(__SCREAMING_SNAKE_CASE     ,     1)
							A			=					np.equal(__SCREAMING_SNAKE_CASE     ,     1).astype(np.floataa).sum()
							A			=					np.equal(__SCREAMING_SNAKE_CASE     ,     1).astype(np.floataa).sum()
							self.assertEqual(shifted.shape     ,     input_ids.shape)
							self.assertEqual(__SCREAMING_SNAKE_CASE     ,     n_pad_before - 1)
							self.assertTrue(np.equal(shifted[:, 0]     ,     2).all())





@require_flax
class 			__UpperCamelCase (   _A     ,		unittest.TestCase     ,		_A						):
		SCREAMING_SNAKE_CASE								=    True
		SCREAMING_SNAKE_CASE								=    (
		    (
		        FlaxMBartModel,
		        FlaxMBartForConditionalGeneration,
		        FlaxMBartForSequenceClassification,
		        FlaxMBartForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		SCREAMING_SNAKE_CASE								=    (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					FlaxMBartModelTester(self)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			,    A			=					self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			,    A			=					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(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__):
																	A			=					self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
																	A			=					model_class(__SCREAMING_SNAKE_CASE)

																	@jax.jit
																	def encode_jitted(__SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		int=None     ,     **__SCREAMING_SNAKE_CASE :		List[Any]):
																						return model.encode(input_ids=__SCREAMING_SNAKE_CASE     ,     attention_mask=__SCREAMING_SNAKE_CASE)

																	with self.subTest("JIT Enabled"):
																						A			=					encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

																	with self.subTest("JIT Disabled"):
																						with jax.disable_jit():
																											A			=					encode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     len(__SCREAMING_SNAKE_CASE))
																	for jitted_output, output in zip(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																						self.assertEqual(jitted_output.shape     ,     output.shape)


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__):
																	A			=					model_class(__SCREAMING_SNAKE_CASE)
																	A			=					model.encode(inputs_dict["input_ids"]     ,     inputs_dict["attention_mask"])

																	A			=					{
																	    "decoder_input_ids": inputs_dict["decoder_input_ids"],
																	    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
																	    "encoder_outputs": encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(__SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		Tuple     ,     __SCREAMING_SNAKE_CASE :		str):
																						return model.decode(
																						    decoder_input_ids=__SCREAMING_SNAKE_CASE     ,     decoder_attention_mask=__SCREAMING_SNAKE_CASE     ,     encoder_outputs=__SCREAMING_SNAKE_CASE     ,     )

																	with self.subTest("JIT Enabled"):
																						A			=					decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

																	with self.subTest("JIT Disabled"):
																						with jax.disable_jit():
																											A			=					decode_jitted(**__SCREAMING_SNAKE_CASE).to_tuple()

																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,     len(__SCREAMING_SNAKE_CASE))
																	for jitted_output, output in zip(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
																						self.assertEqual(jitted_output.shape     ,     output.shape)


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							for model_class_name in self.all_model_classes:
												A			=					model_class_name.from_pretrained("facebook/mbart-large-cc25"     ,     from_pt=__SCREAMING_SNAKE_CASE)
												# FlaxMBartForSequenceClassification expects eos token in input_ids
												A			=					np.ones((1, 1)) * model.config.eos_token_id
												A			=					model(__SCREAMING_SNAKE_CASE)
												self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





@require_flax
@require_sentencepiece
@require_tokenizers
class 			__UpperCamelCase (   unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    [
		    " UN Chief Says There Is No Military Solution in Syria",
		]
		SCREAMING_SNAKE_CASE								=    [
		    "Şeful ONU declară că nu există o soluţie militară în Siria",
		]
		SCREAMING_SNAKE_CASE								=    "facebook/mbart-large-en-ro"


		@cached_property
		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							return AutoTokenizer.from_pretrained(self.model_name)


		@cached_property
		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							A			=					FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     ,     from_pt=__SCREAMING_SNAKE_CASE)
							return model


		def 					SCREAMING_SNAKE_CASE__   (self :		str     ,     **__SCREAMING_SNAKE_CASE :		int):
							A			=					self.translate_src_text(**__SCREAMING_SNAKE_CASE)
							self.assertListEqual(self.expected_text     ,     __SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple     ,     **__SCREAMING_SNAKE_CASE :		str):
							A			=					self.tokenizer(self.src_text     ,     **__SCREAMING_SNAKE_CASE     ,     return_tensors="np")
							A			=					self.model.generate(
							    model_inputs.input_ids     ,     attention_mask=model_inputs.attention_mask     ,     decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]     ,     early_stopping=__SCREAMING_SNAKE_CASE     ,     num_beams=2     ,     ).sequences
							A			=					self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,     skip_special_tokens=__SCREAMING_SNAKE_CASE)
							return generated_words


		@slow
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							self._assert_generated_batch_equal_expected()








"""simple docstring"""




import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


__A     :    int    =		'none'
__A     :    List[Any]    =		'none'
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					value.split(lowercase__      )
					return len(lowercase__      ) > 1 and any((len(lowercase__      ) == 3 for x in split)      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if isinstance(lowercase__			,							lowercase__      ):
										return value
					if isinstance(lowercase__			,							lowercase__      ):
										return float(lowercase__      )
					if not isinstance(lowercase__			,							lowercase__      ):
										raise ValueError("Argument value is not a string. Can't parse it as float"      )
					A			=					value

					try:
										# Example: 1,000.7
										if "." in sanitized and "," in sanitized:
															return float(sanitized.replace(","			,							""      )      )
										# 1,000
										if "," in sanitized and _split_thousands(","			,							lowercase__      ):
															return float(sanitized.replace(","			,							""      )      )
										# 5,5556
										if "," in sanitized and sanitized.count(","      ) == 1 and not _split_thousands(","			,							lowercase__      ):
															return float(sanitized.replace(","			,							"."      )      )
										# 0.0.0.1
										if sanitized.count("."      ) > 1:
															return float(sanitized.replace("."			,							""      )      )
										# 0,0,0,1
										if sanitized.count(","      ) > 1:
															return float(sanitized.replace(","			,							""      )      )
										return float(lowercase__      )
					except ValueError:
										# Avoid adding the sanitized value in the error message.
										raise ValueError("Unable to convert value to float"      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					if answer is None:
										return None
					try:
										A			=					convert_to_float(lowercase__      )
										if isinstance(lowercase__			,							lowercase__      ) and math.isnan(lowercase__      ):
															return None
										return value
					except ValueError:
										return answer.lower()


__A     :    List[str]    =		{
    'text': lambda x: x,
    'real': convert_to_float,
}





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2
		SCREAMING_SNAKE_CASE								=    3
		SCREAMING_SNAKE_CASE								=    4
		SCREAMING_SNAKE_CASE								=    5





class 			__UpperCamelCase (   enum.Enum						):
		SCREAMING_SNAKE_CASE								=    0
		SCREAMING_SNAKE_CASE								=    1
		SCREAMING_SNAKE_CASE								=    2





@dataclasses.dataclass
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42
		SCREAMING_SNAKE_CASE								=    42

__A     :    int    =		re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return list(_TOKENIZER.findall(x.lower()      )      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if operator == _Operator.EQUALS:
										return src == tgt
					elif operator == _Operator.GREATER:
										return src > tgt
					elif operator == _Operator.LESSER:
										return src < tgt
					raise ValueError(F"""Unknown operator: {operator}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					table["types"]
					return _TYPE_CONVERTER[types[column]](lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					return isinstance(lowercase__			,							lowercase__      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					for cond in conditions:
										A			=					row[cond.column]

										A			=					_parse_value(lowercase__			,							cond.column			,							cond.cmp_value      )

										if _is_string(lowercase__      ) and _is_string(lowercase__      ):
															A			=					_normalize_for_match(lowercase__      )
															A			=					_normalize_for_match(lowercase__      )

										if not isinstance(lowercase__			,							type(lowercase__      )      ):
															raise ValueError("Type difference {} != {}".format(type(lowercase__      )			,							type(lowercase__      )      )      )

										if not _compare(cond.operator			,							lowercase__			,							lowercase__      ):
															return False
					return True
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if not answer_coordinates:
										if aggregation_op == _Aggregation.COUNT:
															return 0.0
										else:
															return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
					if aggregation_op == _Aggregation.COUNT:
										return float(len(lowercase__      )      )

					# If we have just one answer, if float returns it or try a conversion.
					A			=					[table["rows"][i][j] for (i, j) in answer_coordinates]
					if len(lowercase__      ) == 1:
										try:
															return convert_to_float(values[0]      )
										except ValueError as e:
															if aggregation_op != _Aggregation.NONE:
																				raise e

					if aggregation_op == _Aggregation.NONE:
										return None

					# Other aggregation only support numeric values. Bail out if we have strings.
					if not all((isinstance(lowercase__			,							(int, float)      ) for v in values)      ):
										return None

					if aggregation_op == _Aggregation.SUM:
										return float(sum(lowercase__      )      )
					elif aggregation_op == _Aggregation.AVERAGE:
										return sum(lowercase__      ) / len(lowercase__      )
					else:
										raise ValueError(F"""Unknown aggregation: {aggregation_op}"""      )
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			=					sql_query["agg"]
					if aggregation_op_index >= 3:
										A			=					_Aggregation(lowercase__      )
					else:
										A			=					_Aggregation.NONE

					A			=					sql_query["sel"]
					A			=					[
					    _Condition(lowercase__			,							_Operator(lowercase__      )			,							lowercase__      )
					    for column, operator, cmp_value in zip(
					        sql_query["conds"]["column_index"]			,							sql_query["conds"]["operator_index"]			,							sql_query["conds"]["condition"]      )
					]

					A			=					[]
					for row in range(len(table["rows"]      )      ):
										if _respect_conditions(lowercase__			,							table["rows"][row]			,							lowercase__      ):
															indices.append((row, target_column)      )

					if not indices:
										return [], aggregation_op

					if len(lowercase__      ) == 1:
										return indices, aggregation_op

					# Parsing of MIN/MAX.
					if aggregation_op_index in (1, 2):
										A			=					{2: min, 1: max}
										A			=					[(table["rows"][i][j], index) for index, (i, j) in enumerate(lowercase__      )]
										A			=					functools.reduce(operators[sql_query["agg"]]			,							lowercase__      )

										A			=					[indices[reduced[1]]]
										return ret, _Aggregation.NONE

					return indices, aggregation_op
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__			,							lowercase__      ):
					"""simple docstring"""
					if float_answer is not None:
										return [str(lowercase__      )]
					return [str(table["real_rows"][r][c]      ) for r, c in answer_coordinates]
def    __SCREAMING_SNAKE_CASE						(						lowercase__			,							lowercase__      ):
					"""simple docstring"""
					A			,    A			=					_get_answer_coordinates(lowercase__			,							lowercase__      )
					A			=					_get_float_answer(lowercase__			,							lowercase__			,							lowercase__      )
					A			=					_get_answer_text(lowercase__			,							lowercase__			,							lowercase__      )
					# keep the original data the same with TaPas
					if len(lowercase__      ) == 0:
										A			=					[EMPTY_ANSWER]
					return answer_text





