

import string
from math import logaa





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= document.translate(
	    str.maketrans(''			, ''			, string.punctuation	)	).replace('\n'			, ''	)
	snake_case_      		= document_without_punctuation.split(' '	)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]	)





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= corpus.lower().translate(
	    str.maketrans(''			, ''			, string.punctuation	)	)  # strip all punctuation and replace it with ''
	snake_case_      		= corpus_without_punctuation.split('\n'	)
	snake_case_      		= term.lower()
	return (len([doc for doc in docs if term in doc]	), len(lowercase__	))





def a(lowercase__			, lowercase__			, lowercase__=False	):





	'''simple docstring'''
	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(lowercase__			, lowercase__	):





	'''simple docstring'''
	return round(tf * idf			, 3	)



from __future__ import annotations

import os
import tempfile
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow

from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    AutoTokenizer,
		    TFAutoModelForCausalLM,
		    TFAutoModelForSeqaSeqLM,
		    TFAutoModelForSpeechSeqaSeq,
		    TFAutoModelForVisionaSeq,
		    TFBartForConditionalGeneration,
		    TFLogitsProcessorList,
		    TFMinLengthLogitsProcessor,
		    tf_top_k_top_p_filtering,
		)

if is_tensorflow_text_available():
		import tensorflow_text as text


@require_tf
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= tf.convert_to_tensor(
				    [
				        [
				            8.222_0991,  # 3rd highest value; idx. 0
				            -0.562_0044,
				            5.2322_9752,
				            4.038_6393,
				            -6.879_8378,
				            -0.5478_5802,
				            -3.201_2153,
				            2.9277_7176,
				            1.8817_1953,
				            7.3534_1276,  # 5th highest value; idx. 9
				            8.4320_7833,  # 2nd highest value; idx. 10
				            -9.8571_1836,
				            -5.9620_9236,
				            -1.1303_9161,
				            -7.111_5294,
				            -0.836_9633,
				            -5.318_6408,
				            7.0642_7407,
				            0.8136_9344,
				            -0.8202_3817,
				            -5.917_9796,
				            0.5881_3443,
				            -6.9977_8438,
				            4.7155_1189,
				            -0.1877_1637,
				            7.4402_0759,  # 4th highest value; idx. 25
				            9.3845_0987,  # 1st highest value; idx. 26
				            2.1266_2941,
				            -9.3256_2038,
				            2.3565_2522,
				        ],  # cummulative prob of 5 highest values <= 0.6
				        [
				            0.5842_5518,
				            4.5313_9238,
				            -5.5751_0464,
				            -6.2803_0699,
				            -7.1952_9503,
				            -4.0212_2551,
				            1.3933_7037,
				            -6.0670_7057,
				            1.5948_0517,
				            -9.64_3119,
				            0.0390_7799,
				            0.6723_1762,
				            -8.8820_6726,
				            6.2711_5922,  # 4th highest value; idx. 13
				            2.2852_0723,
				            4.8276_7506,
				            4.3042_1368,
				            8.827_5313,  # 2nd highest value; idx. 17
				            5.4402_9958,  # 5th highest value; idx. 18
				            -4.473_5794,
				            7.3857_9536,  # 3rd highest value; idx. 20
				            -2.9105_1663,
				            2.6194_6077,
				            -2.567_4762,
				            -9.4895_9302,
				            -4.0292_2645,
				            -1.3541_6918,
				            9.6770_2323,  # 1st highest value; idx. 27
				            -5.8947_8553,
				            1.8537_0467,
				        ],  # cummulative prob of 5 highest values <= 0.6
				    ] ,				dtype=tf.floataa ,				)

				snake_case_      		= tf.convert_to_tensor(
				    [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] ,				dtype=tf.intaa ,				)  # expected non filtered idx as noted above

				snake_case_      		= tf.convert_to_tensor(
				    [8.22_2099, 7.353_4126, 8.43_2078, 7.440_2075, 9.3_8451, 6.27_1159, 8.82_7531, 5.440_2995, 7.385_7956, 9.67_7023] ,				dtype=tf.floataa ,				)  # expected non filtered values as noted above

				snake_case_      		= tf_top_k_top_p_filtering(__UpperCamelCase ,				top_k=10 ,				top_p=0.6 ,				min_tokens_to_keep=4							)

				snake_case_      		= output[output != -float('inf'							)]
				snake_case_      		= tf.cast(
				    tf.where(tf.not_equal(__UpperCamelCase ,				tf.constant(-float('inf'							) ,				dtype=tf.floataa							)							)							) ,				dtype=tf.intaa ,				)

				tf.debugging.assert_near(__UpperCamelCase ,				__UpperCamelCase ,				rtol=1E-12							)
				tf.debugging.assert_equal(__UpperCamelCase ,				__UpperCamelCase							)







@require_tf
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							,						__snake_case							):






			"""simple docstring"""




			if is_tf_available():
						__A						  =  {
						    """AutoModelForCausalLM""": TFAutoModelForCausalLM,
						    """AutoModelForSpeechSeq2Seq""": TFAutoModelForSpeechSeqaSeq,
						    """AutoModelForSeq2SeqLM""": TFAutoModelForSeqaSeqLM,
						    """AutoModelForVision2Seq""": TFAutoModelForVisionaSeq,
						    """LogitsProcessorList""": TFLogitsProcessorList,
						    """MinLengthLogitsProcessor""": TFMinLengthLogitsProcessor,
						    """create_tensor_fn""": tf.convert_to_tensor,
						    """floats_tensor""": floats_tensor,
						    """return_tensors""": """tf""",
						}


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
				snake_case_      		= 2
				snake_case_      		= 2


				class SCREAMING_SNAKE_CASE  (		tf.Module							):






							"""simple docstring"""




							def __init__(    self ,				__UpperCamelCase							):

								"""simple docstring"""


								super(__UpperCamelCase ,				self							).__init__()
								snake_case_      		= model







							@tf.function(
							    input_signature=(
							        tf.TensorSpec((None, input_length) ,				tf.intaa ,				name='input_ids'							),
							        tf.TensorSpec((None, input_length) ,				tf.intaa ,				name='attention_mask'							),
							    ) ,				jit_compile=__UpperCamelCase ,				)
							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

								"""simple docstring"""


								snake_case_      		= self.model.generate(
								    input_ids=__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				max_new_tokens=__UpperCamelCase ,				return_dict_in_generate=__UpperCamelCase ,				)
								return {"sequences": outputs["sequences"]}







				snake_case_      		= [[2, 0], [1_02, 1_03]]
				snake_case_      		= [[1, 0], [1, 1]]
				snake_case_      		= DummyModel(model=__UpperCamelCase							)
				with tempfile.TemporaryDirectory() as tmp_dir:
					tf.saved_model.save(__UpperCamelCase ,				__UpperCamelCase ,				signatures={'serving_default': dummy_model.serving}							)
					snake_case_      		= tf.saved_model.load(__UpperCamelCase							).signatures['serving_default']
					for batch_size in range(1 ,				len(__UpperCamelCase							) + 1							):
						snake_case_      		= {
						    'input_ids': tf.constant(dummy_input_ids[:batch_size]							),
						    'attention_mask': tf.constant(dummy_attention_masks[:batch_size]							),
						}
						snake_case_      		= serving_func(**__UpperCamelCase							)['sequences']
						snake_case_      		= test_model.generate(**__UpperCamelCase ,				max_new_tokens=__UpperCamelCase							)
						tf.debugging.assert_equal(__UpperCamelCase ,				__UpperCamelCase							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
				snake_case_      		= 1
				snake_case_      		= 2


				class SCREAMING_SNAKE_CASE  (		tf.Module							):






							"""simple docstring"""




							def __init__(    self ,				__UpperCamelCase							):

								"""simple docstring"""


								super(__UpperCamelCase ,				self							).__init__()
								snake_case_      		= model







							@tf.function(
							    input_signature=(
							        tf.TensorSpec((batch_size, None) ,				tf.intaa ,				name='input_ids'							),
							        tf.TensorSpec((batch_size, None) ,				tf.intaa ,				name='attention_mask'							),
							    ) ,				jit_compile=__UpperCamelCase ,				)
							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

								"""simple docstring"""


								snake_case_      		= self.model.generate(
								    input_ids=__UpperCamelCase ,				attention_mask=__UpperCamelCase ,				max_new_tokens=__UpperCamelCase ,				return_dict_in_generate=__UpperCamelCase ,				)
								return {"sequences": outputs["sequences"]}







				snake_case_      		= [[2], [1_02, 1_03]]
				snake_case_      		= [[1], [1, 1]]
				snake_case_      		= DummyModel(model=__UpperCamelCase							)
				with tempfile.TemporaryDirectory() as tmp_dir:
					tf.saved_model.save(__UpperCamelCase ,				__UpperCamelCase ,				signatures={'serving_default': dummy_model.serving}							)
					snake_case_      		= tf.saved_model.load(__UpperCamelCase							).signatures['serving_default']
					for input_row in range(len(__UpperCamelCase							)							):
						snake_case_      		= {
						    'input_ids': tf.constant([dummy_input_ids[input_row]]							),
						    'attention_mask': tf.constant([dummy_attention_masks[input_row]]							),
						}
						snake_case_      		= serving_func(**__UpperCamelCase							)['sequences']
						snake_case_      		= test_model.generate(**__UpperCamelCase ,				max_new_tokens=__UpperCamelCase							)
						tf.debugging.assert_equal(__UpperCamelCase ,				__UpperCamelCase							)


			@slow
			@require_tensorflow_text
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				with tempfile.TemporaryDirectory() as tmp_dir:
					# file needed to load the TF tokenizer
					hf_hub_download(repo_id='google/flan-t5-small' ,				filename='spiece.model' ,				local_dir=__UpperCamelCase							)


					class SCREAMING_SNAKE_CASE  (		tf.keras.layers.Layer							):






								"""simple docstring"""




								def __init__(    self							):

									"""simple docstring"""


									super().__init__()
									snake_case_      		= text.SentencepieceTokenizer(
									    model=tf.io.gfile.GFile(os.path.join(__UpperCamelCase ,				'spiece.model'							) ,				'rb'							).read()							)
									snake_case_      		= TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5'							)







								def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				*__UpperCamelCase ,				**__UpperCamelCase							):

									"""simple docstring"""


									snake_case_      		= self.tokenizer.tokenize(__UpperCamelCase							)
									snake_case_		,       snake_case_      		= text.pad_model_inputs(
									    __UpperCamelCase ,				max_seq_length=64 ,				pad_value=self.model.config.pad_token_id							)
									snake_case_      		= self.model.generate(input_ids=__UpperCamelCase ,				attention_mask=__UpperCamelCase							)
									return self.tokenizer.detokenize(__UpperCamelCase							)







					snake_case_      		= CompleteSentenceTransformer()
					snake_case_      		= tf.keras.layers.Input(shape=(1,) ,				dtype=tf.string ,				name='inputs'							)
					snake_case_      		= complete_model(__UpperCamelCase							)
					snake_case_      		= tf.keras.Model(__UpperCamelCase ,				__UpperCamelCase							)
					keras_model.save(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {
				    'do_sample': True,
				    'num_beams': 1,
				    'top_p': 0.7,
				    'top_k': 10,
				    'temperature': 0.7,
				}
				snake_case_      		= 14

				snake_case_      		= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)
				snake_case_      		= 'Hello, my dog is cute and'
				snake_case_      		= tokenizer(__UpperCamelCase ,				return_tensors='tf'							)
				snake_case_      		= TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'							)

				snake_case_      		= 6_38
				# forces the generation to happen on CPU, to avoid GPU-related quirks
				with tf.device(':/CPU:0'							):
					tf.random.set_seed(0							)
					snake_case_      		= model.generate(**__UpperCamelCase ,				eos_token_id=__UpperCamelCase ,				**__UpperCamelCase							)
				self.assertTrue(expectation == len(generated_tokens[0]							)							)

				snake_case_      		= [6_38, 1_98]
				with tf.device(':/CPU:0'							):
					tf.random.set_seed(0							)
					snake_case_      		= model.generate(**__UpperCamelCase ,				eos_token_id=__UpperCamelCase ,				**__UpperCamelCase							)
				self.assertTrue(expectation == len(generated_tokens[0]							)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart'							)
				snake_case_      		= 'Hugging Face is a technology company based in New York and Paris.'
				snake_case_      		= bart_tokenizer(__UpperCamelCase ,				return_tensors='tf'							).input_ids
				snake_case_      		= TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart'							)
				snake_case_      		= bart_model.generate(__UpperCamelCase							).numpy()


				class SCREAMING_SNAKE_CASE  (		__snake_case							):






							"""simple docstring"""




							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

								"""simple docstring"""


								return super().call(__UpperCamelCase ,				**__UpperCamelCase							)







				snake_case_      		= FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart'							)
				snake_case_      		= bart_model.generate(__UpperCamelCase ,				foo='bar'							).numpy()
				self.assertTrue(np.array_equal(__UpperCamelCase ,				__UpperCamelCase							)							)


				class SCREAMING_SNAKE_CASE  (		bart_model.model.encoder.__class__							):






							"""simple docstring"""




							def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				**__UpperCamelCase							):

								"""simple docstring"""


								return super().call(__UpperCamelCase ,				**__UpperCamelCase							)







				snake_case_      		= FakeEncoder(bart_model.config ,				bart_model.model.shared							)
				snake_case_      		= fake_encoder

				# Normal generation still works (the output will be different because the encoder weights are different)
				snake_case_      		= bart_model.generate(__UpperCamelCase							).numpy()
				with self.assertRaises(__UpperCamelCase							):
					# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
					bart_model.generate(__UpperCamelCase ,				foo='bar'							)
