

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


A											=     get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
		from transformers.models.mbart.modeling_mbart import shift_tokens_right

A											=     25_0004
A											=     25_0020


@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE  (		_snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A     :   str						  =  MBartaaTokenizer
			__A     :   List[Any]						  =  MBartaaTokenizerFast
			__A     :   Dict						  =  True
			__A     :   int						  =  True


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				super().setUp()

				# We have a SentencePiece fixture for testing
				snake_case_      		= MBartaaTokenizer(snake_case_ ,				src_lang='en_XX' ,				tgt_lang='ro_RO' ,				keep_accents=snake_case_							)
				tokenizer.save_pretrained(self.tmpdirname							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= "<s>"
				snake_case_      		= 0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_							) ,				snake_case_							)
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_							) ,				snake_case_							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= list(self.get_tokenizer().get_vocab().keys()							)

				self.assertEqual(vocab_keys[0] ,				'<s>'							)
				self.assertEqual(vocab_keys[1] ,				'<pad>'							)
				self.assertEqual(vocab_keys[-1] ,				'<mask>'							)
				self.assertEqual(len(snake_case_							) ,				10_54							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.assertEqual(self.get_tokenizer().vocab_size ,				10_54							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= MBartaaTokenizer(snake_case_ ,				src_lang='en_XX' ,				tgt_lang='ro_RO' ,				keep_accents=snake_case_							)

				snake_case_      		= tokenizer.tokenize('This is a test'							)
				self.assertListEqual(snake_case_ ,				['▁This', '▁is', '▁a', '▁t', 'est']							)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(snake_case_							) ,				[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,				)

				snake_case_      		= tokenizer.tokenize('I was born in 92000, and this is falsé.'							)
				self.assertListEqual(
				    snake_case_ ,				[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.'] ,				)
				snake_case_      		= tokenizer.convert_tokens_to_ids(snake_case_							)
				self.assertListEqual(
				    snake_case_ ,				[
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				    ] ,				)

				snake_case_      		= tokenizer.convert_ids_to_tokens(snake_case_							)
				self.assertListEqual(
				    snake_case_ ,				[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.'] ,				)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {"input_ids": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=snake_case_ ,				model_name='facebook/mbart-large-50' ,				revision='d3913889c59cd5c9e456b269c376325eabad57e2' ,				)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if not self.test_slow_tokenizer:
					# as we don't have a slow version, we can't compare the outputs between slow and fast versions
					return

				snake_case_      		= (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
						snake_case_      		= self.rust_tokenizer_class.from_pretrained(snake_case_ ,				**snake_case_							)
						snake_case_      		= self.tokenizer_class.from_pretrained(snake_case_ ,				**snake_case_							)

						snake_case_      		= tempfile.mkdtemp()

						snake_case_      		= tokenizer_r.save_pretrained(snake_case_							)
						snake_case_      		= tokenizer_p.save_pretrained(snake_case_							)

						# Checks it save with the same files + the tokenizer.json file for the fast one
						self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files							)							)
						snake_case_      		= tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f							)
						self.assertSequenceEqual(snake_case_ ,				snake_case_							)

						# Checks everything loads correctly in the same way
						snake_case_      		= tokenizer_r.from_pretrained(snake_case_							)
						snake_case_      		= tokenizer_p.from_pretrained(snake_case_							)

						# Check special tokens are set accordingly on Rust and Python
						for key in tokenizer_pp.special_tokens_map:
							self.assertTrue(hasattr(snake_case_ ,				snake_case_							)							)
							# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
							# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

						shutil.rmtree(snake_case_							)

						# Save tokenizer rust, legacy_format=True
						snake_case_      		= tempfile.mkdtemp()

						snake_case_      		= tokenizer_r.save_pretrained(snake_case_ ,				legacy_format=snake_case_							)
						snake_case_      		= tokenizer_p.save_pretrained(snake_case_							)

						# Checks it save with the same files
						self.assertSequenceEqual(snake_case_ ,				snake_case_							)

						# Checks everything loads correctly in the same way
						snake_case_      		= tokenizer_r.from_pretrained(snake_case_							)
						snake_case_      		= tokenizer_p.from_pretrained(snake_case_							)

						# Check special tokens are set accordingly on Rust and Python
						for key in tokenizer_pp.special_tokens_map:
							self.assertTrue(hasattr(snake_case_ ,				snake_case_							)							)

						shutil.rmtree(snake_case_							)

						# Save tokenizer rust, legacy_format=False
						snake_case_      		= tempfile.mkdtemp()

						snake_case_      		= tokenizer_r.save_pretrained(snake_case_ ,				legacy_format=snake_case_							)
						snake_case_      		= tokenizer_p.save_pretrained(snake_case_							)

						# Checks it saved the tokenizer.json file
						self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files							)							)

						# Checks everything loads correctly in the same way
						snake_case_      		= tokenizer_r.from_pretrained(snake_case_							)
						snake_case_      		= tokenizer_p.from_pretrained(snake_case_							)

						# Check special tokens are set accordingly on Rust and Python
						for key in tokenizer_pp.special_tokens_map:
							self.assertTrue(hasattr(snake_case_ ,				snake_case_							)							)

						shutil.rmtree(snake_case_							)







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






			"""simple docstring"""




			__A     :   int						  =  """facebook/mbart-large-50-one-to-many-mmt"""
			__A     :   List[str]						  =  [
			    """ UN Chief Says There Is No Military Solution in Syria""",
			    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
			]
			__A     :   Any						  =  [
			    """Şeful ONU declară că nu există o soluţie militară în Siria""",
			    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
			    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
			    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
			]
			__A     :   List[Any]						  =  [EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]


			@classmethod
			def 			__lowerCAmelCase (    cls							):

				"""simple docstring"""


				snake_case_      		= MBartaaTokenizer.from_pretrained(
				    cls.checkpoint_name ,				src_lang='en_XX' ,				tgt_lang='ro_RO'							)
				snake_case_      		= 1
				return cls


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,				25_00_01							)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,				25_00_04							)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,				25_00_20							)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN'] ,				25_00_38							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
				self.assertListEqual(self.expected_src_tokens ,				snake_case_							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.assertIn(snake_case_ ,				self.tokenizer.all_special_ids							)
				snake_case_      		= [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
				snake_case_      		= self.tokenizer.decode(snake_case_ ,				skip_special_tokens=snake_case_							)
				snake_case_      		= self.tokenizer.decode(generated_ids[1:] ,				skip_special_tokens=snake_case_							)
				self.assertEqual(snake_case_ ,				snake_case_							)
				self.assertNotIn(self.tokenizer.eos_token ,				snake_case_							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= ["this is gunna be a long sentence " * 20]
				assert isinstance(src_text[0] ,				snake_case_							)
				snake_case_      		= 10
				snake_case_      		= self.tokenizer(snake_case_ ,				max_length=snake_case_ ,				truncation=snake_case_							).input_ids[0]
				self.assertEqual(ids[0] ,				snake_case_							)
				self.assertEqual(ids[-1] ,				2							)
				self.assertEqual(len(snake_case_							) ,				snake_case_							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']							) ,				[25_00_53, 25_00_01]							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= tempfile.mkdtemp()
				snake_case_      		= self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(snake_case_							)
				snake_case_      		= MBartaaTokenizer.from_pretrained(snake_case_							)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,				snake_case_							)


			@require_torch
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.tokenizer(self.src_text ,				text_target=self.tgt_text ,				padding=snake_case_ ,				return_tensors='pt'							)
				snake_case_      		= shift_tokens_right(batch['labels'] ,				self.tokenizer.pad_token_id							)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				assert batch.input_ids[1][0] == EN_CODE
				assert batch.input_ids[1][-1] == 2
				assert batch.labels[1][0] == RO_CODE
				assert batch.labels[1][-1] == 2
				assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]


			@require_torch
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.tokenizer(
				    self.src_text ,				text_target=self.tgt_text ,				padding=snake_case_ ,				truncation=snake_case_ ,				max_length=len(self.expected_src_tokens							) ,				return_tensors='pt' ,				)
				snake_case_      		= shift_tokens_right(batch['labels'] ,				self.tokenizer.pad_token_id							)

				self.assertIsInstance(snake_case_ ,				snake_case_							)

				self.assertEqual((2, 14) ,				batch.input_ids.shape							)
				self.assertEqual((2, 14) ,				batch.attention_mask.shape							)
				snake_case_      		= batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens ,				snake_case_							)
				self.assertEqual(2 ,				batch.decoder_input_ids[0, 0]							)  # decoder_start_token_id
				# Test that special tokens are reset
				self.assertEqual(self.tokenizer.prefix_tokens ,				[EN_CODE]							)
				self.assertEqual(self.tokenizer.suffix_tokens ,				[self.tokenizer.eos_token_id]							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.tokenizer(self.src_text ,				padding=snake_case_ ,				truncation=snake_case_ ,				max_length=3 ,				return_tensors='pt'							)
				snake_case_      		= self.tokenizer(
				    text_target=self.tgt_text ,				padding=snake_case_ ,				truncation=snake_case_ ,				max_length=10 ,				return_tensors='pt'							)
				snake_case_      		= targets["input_ids"]
				snake_case_      		= shift_tokens_right(snake_case_ ,				self.tokenizer.pad_token_id							)

				self.assertEqual(batch.input_ids.shape[1] ,				3							)
				self.assertEqual(batch.decoder_input_ids.shape[1] ,				10							)







			@require_torch
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.tokenizer._build_translation_inputs(
				    'A test' ,				return_tensors='pt' ,				src_lang='en_XX' ,				tgt_lang='ar_AR'							)

				self.assertEqual(
				    nested_simplify(snake_case_							) ,				{
				        # en_XX, A, test, EOS
				        'input_ids': [[25_00_04, 62, 30_34, 2]],
				        'attention_mask': [[1, 1, 1, 1]],
				        # ar_AR
				        'forced_bos_token_id': 25_00_01,
				    } ,				)



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'							)
