

"""simple docstring"""


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class    snake_case     :

				def __init__(     self							:    Tuple					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    int=9_9					,  UpperCamelCase__							:    Dict=1_3					,  UpperCamelCase__							:    List[str]=7					,  UpperCamelCase__							:    Optional[int]=9					,  UpperCamelCase__							:    int=True					,  UpperCamelCase__							:    str=True					,  UpperCamelCase__							:    Optional[int]=False					,  UpperCamelCase__							:    Any=3_2					,  UpperCamelCase__							:    str=5					,  UpperCamelCase__							:    int=4					,  UpperCamelCase__							:    List[Any]=3_7					,  UpperCamelCase__							:    Any=8					,  UpperCamelCase__							:    str=0.1					,  UpperCamelCase__							:    Any=0.002					,  UpperCamelCase__							:    Any=1					,  UpperCamelCase__							:    Tuple=0					,  UpperCamelCase__							:    Tuple=0					,  UpperCamelCase__							:    Tuple=None					,  UpperCamelCase__							:    Union[str, Any]=None					,  )->       str:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      parent
						__lowerCAmelCase:	Optional[Any]   				=      batch_size
						__lowerCAmelCase:	Union[str, Any]   				=      encoder_seq_length
						__lowerCAmelCase:	Union[str, Any]   				=      decoder_seq_length
						# For common tests
						__lowerCAmelCase:	int   				=      self.decoder_seq_length
						__lowerCAmelCase:	Union[str, Any]   				=      is_training
						__lowerCAmelCase:	Tuple   				=      use_attention_mask
						__lowerCAmelCase:	Tuple   				=      use_labels
						__lowerCAmelCase:	List[Any]   				=      vocab_size
						__lowerCAmelCase:	str   				=      hidden_size
						__lowerCAmelCase:	Dict   				=      num_hidden_layers
						__lowerCAmelCase:	List[Any]   				=      num_attention_heads
						__lowerCAmelCase:	Tuple   				=      d_ff
						__lowerCAmelCase:	Union[str, Any]   				=      relative_attention_num_buckets
						__lowerCAmelCase:	Optional[Any]   				=      dropout_rate
						__lowerCAmelCase:	List[str]   				=      initializer_factor
						__lowerCAmelCase:	List[str]   				=      eos_token_id
						__lowerCAmelCase:	List[Any]   				=      pad_token_id
						__lowerCAmelCase:	Union[str, Any]   				=      decoder_start_token_id
						__lowerCAmelCase:	Tuple   				=      None
						__lowerCAmelCase:	int   				=      decoder_layers

				def 							lowercase_					(     self							:    Optional[int])->       Optional[int]:



						'''simple docstring'''


						return TaConfig.from_pretrained("google/umt5-base")

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    int=None					,  UpperCamelCase__							:    str=None					,  UpperCamelCase__							:    Dict=None					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    int=None					,  )->       Union[str, Any]:



						'''simple docstring'''


						if attention_mask is None:
								__lowerCAmelCase:	Dict   				=      input_ids.ne(config.pad_token_id)
						if decoder_attention_mask is None:
								__lowerCAmelCase:	int   				=      decoder_input_ids.ne(config.pad_token_id)
						if head_mask is None:
								__lowerCAmelCase:	Optional[Any]   				=      torch.ones(config.num_hidden_layers					,  config.num_attention_heads					,  device=UpperCamelCase__)
						if decoder_head_mask is None:
								__lowerCAmelCase:	Optional[Any]   				=      torch.ones(config.num_decoder_layers					,  config.num_attention_heads					,  device=UpperCamelCase__)
						if cross_attn_head_mask is None:
								__lowerCAmelCase:	int   				=      torch.ones(
								    config.num_decoder_layers					,  config.num_attention_heads					,  device=UpperCamelCase__)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}

				def 							lowercase_					(     self							:    Any)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      ids_tensor([self.batch_size, self.encoder_seq_length]					,  self.vocab_size)
						__lowerCAmelCase:	Union[str, Any]   				=      ids_tensor([self.batch_size, self.decoder_seq_length]					,  self.vocab_size)

						# we need to clamp the input ids here to avoid having pad token in between
						# this is because for NllbMoe the position_ids are prepared such that
						# all pad tokens have pos id = 2 and rest are between 2..seq_length
						# and the seq_length here is seq_length - num_pad_tokens
						# but when using past, there is no way of knowing if the past input ids had
						# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
						# position_ids being off by num_pad_tokens in past input
						__lowerCAmelCase:	Tuple   				=      input_ids.clamp(self.pad_token_id + 1)
						__lowerCAmelCase:	Tuple   				=      decoder_input_ids.clamp(self.pad_token_id + 1)

						__lowerCAmelCase:	List[str]   				=      self.get_config()
						__lowerCAmelCase:	Optional[int]   				=      config.num_attention_heads
						__lowerCAmelCase:	List[str]   				=      self.prepare_inputs_dict(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__)
						return config, input_dict

				def 							lowercase_					(     self							:    Optional[Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.prepare_config_and_inputs()
						return config, inputs_dict

				def 							lowercase_					(     self							:    Tuple)->       Optional[int]:



						'''simple docstring'''


						return TaConfig(
						    vocab_size=1_6_6					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )

				def 							lowercase_					(     self							:    List[str])->       str:



						'''simple docstring'''


						return TaConfig(
						    vocab_size=self.vocab_size					,  d_model=self.hidden_size					,  d_ff=self.d_ff					,  d_kv=self.hidden_size // self.num_attention_heads					,  num_layers=self.num_hidden_layers					,  num_decoder_layers=self.decoder_layers					,  num_heads=self.num_attention_heads					,  relative_attention_num_buckets=self.relative_attention_num_buckets					,  dropout_rate=self.dropout_rate					,  initializer_factor=self.initializer_factor					,  eos_token_id=self.eos_token_id					,  bos_token_id=self.pad_token_id					,  pad_token_id=self.pad_token_id					,  decoder_start_token_id=self.decoder_start_token_id					,  )

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    str					,  )->       int:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      UMTaModel(config=UpperCamelCase__)
						model.to(UpperCamelCase__)
						model.eval()
						__lowerCAmelCase:	Tuple   				=      model(
						    input_ids=UpperCamelCase__					,  decoder_input_ids=UpperCamelCase__					,  attention_mask=UpperCamelCase__					,  decoder_attention_mask=UpperCamelCase__					,  )
						__lowerCAmelCase:	int   				=      model(input_ids=UpperCamelCase__					,  decoder_input_ids=UpperCamelCase__)
						__lowerCAmelCase:	List[str]   				=      result.last_hidden_state
						__lowerCAmelCase:	int   				=      result.past_key_values
						__lowerCAmelCase:	Union[str, Any]   				=      result.encoder_last_hidden_state

						self.parent.assertEqual(encoder_output.size()					,  (self.batch_size, self.encoder_seq_length, self.hidden_size))
						self.parent.assertEqual(decoder_output.size()					,  (self.batch_size, self.decoder_seq_length, self.hidden_size))
						# There should be `num_layers` key value embeddings stored in decoder_past
						self.parent.assertEqual(len(UpperCamelCase__)					,  config.num_layers)
						# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
						self.parent.assertEqual(len(decoder_past[0])					,  4)

				def 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    List[Any]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    Optional[Any]					,  )->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      UMTaModel(config=UpperCamelCase__).get_decoder().to(UpperCamelCase__).eval()
						# first forward pass
						__lowerCAmelCase:	int   				=      model(UpperCamelCase__					,  use_cache=UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      model(UpperCamelCase__)
						__lowerCAmelCase:	str   				=      model(UpperCamelCase__					,  use_cache=UpperCamelCase__)

						self.parent.assertTrue(len(UpperCamelCase__) == len(UpperCamelCase__))
						self.parent.assertTrue(len(UpperCamelCase__) == len(UpperCamelCase__) + 1)

						__lowerCAmelCase:	Tuple   				=      outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						__lowerCAmelCase:	Optional[int]   				=      ids_tensor((self.batch_size, 1)					,  config.vocab_size)

						# append to next input_ids and
						__lowerCAmelCase:	Any   				=      torch.cat([input_ids, next_tokens]					,  dim=-1)

						__lowerCAmelCase:	int   				=      model(UpperCamelCase__)["last_hidden_state"]
						__lowerCAmelCase:	str   				=      model(UpperCamelCase__					,  past_key_values=UpperCamelCase__)["last_hidden_state"]

						# select random slice
						__lowerCAmelCase:	Optional[Any]   				=      ids_tensor((1,)					,  output_from_past.shape[-1]).item()
						__lowerCAmelCase:	int   				=      output_from_no_past[:, -1, random_slice_idx].detach()
						__lowerCAmelCase:	int   				=      output_from_past[:, 0, random_slice_idx].detach()

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(UpperCamelCase__					,  UpperCamelCase__					,  atol=1e-3))




				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    str					,  )->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      UMTaModel(config=UpperCamelCase__).to(UpperCamelCase__).half().eval()
						__lowerCAmelCase:	Any   				=      model(**UpperCamelCase__)["last_hidden_state"]
						self.parent.assertFalse(torch.isnan(UpperCamelCase__).any().item())





@require_torch
class    snake_case     (    __snake_case,							__snake_case,							__snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  (
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				SCREAMING_SNAKE_CASE_       :				Dict       				=  (UMTaForConditionalGeneration,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  (
				    {
				        """conversational""": UMTaForConditionalGeneration,
				        """feature-extraction""": UMTaModel,
				        """summarization""": UMTaForConditionalGeneration,
				        """text2text-generation""": UMTaForConditionalGeneration,
				        """translation""": UMTaForConditionalGeneration,
				        """question-answering""": UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  True
				SCREAMING_SNAKE_CASE_       :				int       				=  False
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  False
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  True
				SCREAMING_SNAKE_CASE_       :				str       				=  True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  [0.8, 0.9]

				def 							lowercase_					(     self							:    List[str])->       str:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      UMTaModelTester(self)

				@unittest.skip("Test has a segmentation fault on torch 1.8.0")
				def 							lowercase_					(     self							:    Union[str, Any])->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.model_tester.prepare_config_and_inputs()
						__lowerCAmelCase:	Dict   				=      UMTaModel(config_and_inputs[0]).to(UpperCamelCase__)
						with tempfile.TemporaryDirectory() as tmpdirname:
								torch.onnx.export(
								    UpperCamelCase__					,  (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])					,  f"{tmpdirname}/t5_test.onnx"					,  export_params=UpperCamelCase__					,  opset_version=9					,  input_names=["input_ids", "decoder_input_ids"]					,  )

				@unittest.skipIf(torch_device == "cpu"					,  "Cant do half precision")
				def 							lowercase_					(     self							:    int)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model_fpaa_forward(*UpperCamelCase__)

				def 							lowercase_					(     self							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      ["encoder_attentions", "decoder_attentions", "cross_attentions"]
						__lowerCAmelCase:	Tuple   				=      self.model_tester.prepare_config_and_inputs()
						__lowerCAmelCase:	Dict   				=      config_and_inputs[0]
						__lowerCAmelCase:	Optional[int]   				=      UMTaForConditionalGeneration(UpperCamelCase__).eval()
						model.to(UpperCamelCase__)

						__lowerCAmelCase:	Dict   				=      {
						    "head_mask": torch.zeros(config.num_layers					,  config.num_heads					,  device=UpperCamelCase__),
						    "decoder_head_mask": torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=UpperCamelCase__),
						    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers					,  config.num_heads					,  device=UpperCamelCase__),
						}

						for attn_name, (name, mask) in zip(UpperCamelCase__					,  head_masking.items()):
								__lowerCAmelCase:	Union[str, Any]   				=      {name: mask}
								# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
								if name == "head_mask":
										__lowerCAmelCase:	Optional[int]   				=      torch.ones(
										    config.num_decoder_layers					,  config.num_heads					,  device=UpperCamelCase__)

								__lowerCAmelCase:	str   				=      model.generate(
								    config_and_inputs[1]["input_ids"]					,  num_beams=1					,  max_length=3					,  output_attentions=UpperCamelCase__					,  return_dict_in_generate=UpperCamelCase__					,  **UpperCamelCase__					,  )
								# We check the state of decoder_attentions and cross_attentions just from the last step
								__lowerCAmelCase:	Union[str, Any]   				=      out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
								self.assertEqual(sum([w.sum().item() for w in attn_weights])					,  0.0)




				@unittest.skip("Does not work on the tiny model as we keep hitting edge cases.")
				def 							lowercase_					(     self							:    List[str])->       int:



						'''simple docstring'''


						pass





@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case     (    unittest.TestCase  ):

				@slow
				@unittest.skip(
				    "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged")
				def 							lowercase_					(     self							:    List[str])->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      UMTaForConditionalGeneration.from_pretrained("google/umt5-small"					,  return_dict=UpperCamelCase__).to(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      AutoTokenizer.from_pretrained("google/umt5-small"					,  use_fast=UpperCamelCase__					,  legacy=UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      [
						    "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
						    "No se como puedo <extra_id_0>.",
						    "This is the reason why we <extra_id_0> them.",
						    "The <extra_id_0> walks in <extra_id_1>, seats",
						    "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
						]
						__lowerCAmelCase:	Dict   				=      tokenizer(UpperCamelCase__					,  return_tensors="pt"					,  padding=UpperCamelCase__).input_ids
						# fmt: off
						__lowerCAmelCase:	Any   				=      torch.tensor(
						    [
						        [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
						        [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
						        [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
						    ])
						# fmt: on
						torch.testing.assert_allclose(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Any   				=      model.generate(input_ids.to(UpperCamelCase__))
						__lowerCAmelCase:	Union[str, Any]   				=      [
						    "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
						    "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
						    "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
						    "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
						    "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
						]
						__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.batch_decode(UpperCamelCase__)
						self.assertEqual(UpperCamelCase__					,  UpperCamelCase__)






"""simple docstring"""


from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1_0**-1_0			)     ->     float:
		__lowerCAmelCase:	Union[str, Any]   				=      a
		while True:
				__lowerCAmelCase:	Optional[int]   				=      Decimal(__SCREAMING_SNAKE_CASE			) - (
				    Decimal(eval(__SCREAMING_SNAKE_CASE			)			) / Decimal(eval(str(diff(__SCREAMING_SNAKE_CASE			)			)			)			)  # noqa: S307
				)
				# This number dictates the accuracy of the answer
				if abs(eval(__SCREAMING_SNAKE_CASE			)			) < precision:  # noqa: S307
						return float(__SCREAMING_SNAKE_CASE			)


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
			# Find root of polynomial
			print(F'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
			# Find Square Root of 5
			print(F'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
			# Exponential Roots
			print(F'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')


