


'''simple docstring'''
import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
							import optax
							from flax.training.common_utils import onehot

							from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
							from transformers.models.ta.modeling_flax_ta import shift_tokens_right






@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						@slow
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"	)
													A_			    =		AutoTokenizer.from_pretrained("google/mt5-small"	)

													A_			    =		tokenizer("Hello there" ,  return_tensors="np"	).input_ids
													A_			    =		tokenizer("Hi I am" ,  return_tensors="np"	).input_ids

													A_			    =		shift_tokens_right(lowerCAmelCase ,  model.config.pad_token_id ,  model.config.decoder_start_token_id	)

													A_			    =		model(lowerCAmelCase ,  decoder_input_ids=lowerCAmelCase	).logits
													A_			    =		optax.softmax_cross_entropy(lowerCAmelCase ,  onehot(lowerCAmelCase ,  logits.shape[-1]	)	).mean()

													A_			    =		-(labels.shape[-1] * loss.item())

													A_			    =		-8_4.9_1_2_7
													self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)









'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





