
"""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 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			Union[str, Any]							,A :			str							,A :			Dict=99							,A :			int=13							,A :			Union[str, Any]=7							,A :			Tuple=9							,A :			List[str]=True							,A :			List[Any]=True							,A :			int=False							,A :			str=32							,A :			Union[str, Any]=5							,A :			Optional[int]=4							,A :			Tuple=37							,A :			Union[str, Any]=8							,A :			Union[str, Any]=0.1							,A :			List[Any]=0.0_02							,A :			int=1							,A :			Any=0							,A :			int=0							,A :			Optional[int]=None							,A :			Any=None							,):
					__A             =			parent
					__A             =			batch_size
					__A             =			encoder_seq_length
					__A             =			decoder_seq_length
					# For common tests
					__A             =			self.decoder_seq_length
					__A             =			is_training
					__A             =			use_attention_mask
					__A             =			use_labels
					__A             =			vocab_size
					__A             =			hidden_size
					__A             =			num_hidden_layers
					__A             =			num_attention_heads
					__A             =			d_ff
					__A             =			relative_attention_num_buckets
					__A             =			dropout_rate
					__A             =			initializer_factor
					__A             =			eos_token_id
					__A             =			pad_token_id
					__A             =			decoder_start_token_id
					__A             =			None
					__A             =			decoder_layers






	def     UpperCamelCase_  (	self :			str			):
					return TaConfig.from_pretrained("google/umt5-base"			)






	def     UpperCamelCase_  (	self :			List[Any]							,A :			List[str]							,A :			Optional[int]							,A :			Optional[Any]							,A :			Any=None							,A :			Any=None							,A :			str=None							,A :			Dict=None							,A :			int=None							,):
					if attention_mask is None:
									__A             =			input_ids.ne(config.pad_token_id			)
					if decoder_attention_mask is None:
									__A             =			decoder_input_ids.ne(config.pad_token_id			)
					if head_mask is None:
									__A             =			torch.ones(config.num_hidden_layers							,config.num_attention_heads							,device=A			)
					if decoder_head_mask is None:
									__A             =			torch.ones(config.num_decoder_layers							,config.num_attention_heads							,device=A			)
					if cross_attn_head_mask is None:
									__A             =			torch.ones(
									    config.num_decoder_layers							,config.num_attention_heads							,device=A			)
					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     UpperCamelCase_  (	self :			int			):
					__A             =			ids_tensor([self.batch_size, self.encoder_seq_length]							,self.vocab_size			)
					__A             =			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
					__A             =			input_ids.clamp(self.pad_token_id + 1			)
					__A             =			decoder_input_ids.clamp(self.pad_token_id + 1			)

					__A             =			self.get_config()
					__A             =			config.num_attention_heads
					__A             =			self.prepare_inputs_dict(A							,A							,A			)
					return config, input_dict






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






	def     UpperCamelCase_  (	self :			List[str]			):
					return TaConfig(
					    vocab_size=1_66							,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     UpperCamelCase_  (	self :			Optional[int]			):
					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     UpperCamelCase_  (	self :			Tuple							,A :			Dict							,A :			Union[str, Any]							,A :			Dict							,A :			str							,A :			List[Any]							,A :			Tuple							,):
					__A             =			UMTaModel(config=A			)
					model.to(A			)
					model.eval()
					__A             =			model(
					    input_ids=A							,decoder_input_ids=A							,attention_mask=A							,decoder_attention_mask=A							,)
					__A             =			model(input_ids=A							,decoder_input_ids=A			)
					__A             =			result.last_hidden_state
					__A             =			result.past_key_values
					__A             =			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(A			)							,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     UpperCamelCase_  (	self :			Any							,A :			Any							,A :			Optional[Any]							,A :			Union[str, Any]							,A :			Optional[int]							,A :			Union[str, Any]							,A :			Optional[int]							,):
					__A             =			UMTaModel(config=A			).get_decoder().to(A			).eval()
					# first forward pass
					__A             =			model(A							,use_cache=A			)
					__A             =			model(A			)
					__A             =			model(A							,use_cache=A			)

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

					__A      ,							__A             =			outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__A             =			ids_tensor((self.batch_size, 1)							,config.vocab_size			)

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

					__A             =			model(A			)["last_hidden_state"]
					__A             =			model(A							,past_key_values=A			)["last_hidden_state"]

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

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







	def     UpperCamelCase_  (	self :			Optional[int]							,A :			List[str]							,A :			Optional[int]							,):
					__A             =			UMTaModel(config=A			).to(A			).half().eval()
					__A             =			model(**A			)["last_hidden_state"]
					self.parent.assertFalse(torch.isnan(A			).any().item()			)





@require_torch
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					(
	    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
	)
	snake_case_    =					(UMTaForConditionalGeneration,) if is_torch_available() else ()
	snake_case_    =					(
	    {
	        "conversational": UMTaForConditionalGeneration,
	        "feature-extraction": UMTaModel,
	        "summarization": UMTaForConditionalGeneration,
	        "text2text-generation": UMTaForConditionalGeneration,
	        "translation": UMTaForConditionalGeneration,
	        "question-answering": UMTaForQuestionAnswering,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case_    =					True
	snake_case_    =					False
	snake_case_    =					False
	snake_case_    =					True
	snake_case_    =					True
	# The small UMT5 model needs higher percentages for CPU/MP tests
	snake_case_    =					[0.8, 0.9]






	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			UMTaModelTester(self			)






	@unittest.skip("Test has a segmentation fault on torch 1.8.0"			)
	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					__A             =			UMTaModel(config_and_inputs[0]			).to(A			)
					with tempfile.TemporaryDirectory() as tmpdirname:
									torch.onnx.export(
									    A							,(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])							,f'''{tmpdirname}/t5_test.onnx'''							,export_params=A							,opset_version=9							,input_names=["input_ids", "decoder_input_ids"]							,)






	@unittest.skipIf(torch_device == "cpu"							,"Cant do half precision"			)
	def     UpperCamelCase_  (	self :			List[str]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model_fpaa_forward(*A			)






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			["encoder_attentions", "decoder_attentions", "cross_attentions"]
					__A             =			self.model_tester.prepare_config_and_inputs()
					__A             =			config_and_inputs[0]
					__A             =			UMTaForConditionalGeneration(A			).eval()
					model.to(A			)

					__A             =			{
					    "head_mask": torch.zeros(config.num_layers							,config.num_heads							,device=A			),
					    "decoder_head_mask": torch.zeros(config.num_decoder_layers							,config.num_heads							,device=A			),
					    "cross_attn_head_mask": torch.zeros(config.num_decoder_layers							,config.num_heads							,device=A			),
					}

					for attn_name, (name, mask) in zip(A							,head_masking.items()			):
									__A             =			{name: mask}
									# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
									if name == "head_mask":
													__A             =			torch.ones(
													    config.num_decoder_layers							,config.num_heads							,device=A			)

									__A             =			model.generate(
									    config_and_inputs[1]["input_ids"]							,num_beams=1							,max_length=3							,output_attentions=A							,return_dict_in_generate=A							,**A							,)
									# We check the state of decoder_attentions and cross_attentions just from the last step
									__A             =			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     UpperCamelCase_  (	self :			Union[str, Any]			):
					pass





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




	'''simple docstring'''





	@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     UpperCamelCase_  (	self :			int			):
					__A             =			UMTaForConditionalGeneration.from_pretrained("google/umt5-small"							,return_dict=A			).to(A			)
					__A             =			AutoTokenizer.from_pretrained("google/umt5-small"							,use_fast=A							,legacy=A			)
					__A             =			[
					    "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>.",
					]
					__A             =			tokenizer(A							,return_tensors="pt"							,padding=A			).input_ids
					# fmt: off
					__A             =			torch.tensor(
					    [
					        [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
					        [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
					        [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
					    ]			)
					# fmt: on
					torch.testing.assert_allclose(A							,A			)

					__A             =			model.generate(input_ids.to(A			)			)
					__A             =			[
					    "<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>",
					]
					__A             =			tokenizer.batch_decode(A			)
					self.assertEqual(A							,A			)


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

				# one length string
				__A             =			0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__A             =			single_char_strings[ch]
												__A             =			my_str / all_sum
												my_fir_sum += prob * math.loga(a_						)  # entropy formula.

    # print entropy
				print(F'''{round(-1 * my_fir_sum						):.1f}'''						)

				# two len string
				__A             =			sum(two_char_strings.values()						)
				__A             =			0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__A             =			cha + cha
												if sequence in two_char_strings:
																__A             =			two_char_strings[sequence]
																__A             =			int(a_						) / all_sum
																my_sec_sum += prob * math.loga(a_						)

    # print second entropy
				print(F'''{round(-1 * my_sec_sum						):.1f}'''						)

				# print the difference between them
				print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)						):.1f}'''						)


def 						UpperCAmelCase      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0		,       len(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""simple docstring"""



				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()
