

'''simple docstring'''


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      int=   RoCBertTokenizer
							_lowerCamelCase		:      Optional[Any]=   None
							_lowerCamelCase		:      Optional[Any]=   False
							_lowerCamelCase		:      List[str]=   True
							_lowerCamelCase		:      Tuple=   filter_non_english


							def 						_snake_case							(			self)							->				int:
								super().setUp()

								UpperCAmelCase_		:      str       	=					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
								UpperCAmelCase_		:      Optional[int]       	=					{}
								UpperCAmelCase_		:      Dict       	=					{}
								for i, value in enumerate(_snake_case):
									UpperCAmelCase_		:      Dict       	=					i
									UpperCAmelCase_		:      Any       	=					i
								UpperCAmelCase_		:      List[str]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      List[Any]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['word_shape_file'])
								UpperCAmelCase_		:      Optional[Any]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['word_pronunciation_file'])
								with open(self.vocab_file    ,			'w'    ,			encoding='utf-8') as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
								with open(self.word_shape_file    ,			'w'    ,			encoding='utf-8') as word_shape_writer:
									json.dump(_snake_case    ,			_snake_case    ,			ensure_ascii=_snake_case)
								with open(self.word_pronunciation_file    ,			'w'    ,			encoding='utf-8') as word_pronunciation_writer:
									json.dump(_snake_case    ,			_snake_case    ,			ensure_ascii=_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					self.tokenizer_class(self.vocab_file    ,			self.word_shape_file    ,			self.word_pronunciation_file)

								UpperCAmelCase_		:      Optional[Any]       	=					tokenizer.tokenize('你好[SEP]你是谁')
								self.assertListEqual(_snake_case    ,			['你', '好', '[SEP]', '你', '是', '谁'])
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case)    ,			[5, 6, 2, 5, 7, 8])
								self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_snake_case)    ,			[5, 6, 2, 5, 7, 8])
								self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_snake_case)    ,			[5, 6, 2, 5, 7, 8])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      str       	=					RoCBertBasicTokenizer()

								self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz')    ,			['ah', '\u535A', '\u63A8', 'zz'])


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Tuple       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')    ,			['hello', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Union[str, Any]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hällo', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['h\u00E9llo'])


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      int       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hallo', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      List[str]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['hallo', '!', 'how', 'are', 'you', '?'])
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo')    ,			['hello'])


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      int       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  ')    ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?'])


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      int       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['HäLLo', '!', 'how', 'Are', 'yoU', '?'])


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			strip_accents=_snake_case)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  ')    ,			['HaLLo', '!', 'how', 'Are', 'yoU', '?'])


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Optional[Any]       	=					RoCBertBasicTokenizer(do_lower_case=_snake_case    ,			never_split=['[UNK]'])

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]')    ,			['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'])


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      Tuple       	=					['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

								UpperCAmelCase_		:      str       	=					{}
								for i, token in enumerate(_snake_case):
									UpperCAmelCase_		:      Optional[int]       	=					i
								UpperCAmelCase_		:      Optional[int]       	=					RoCBertWordpieceTokenizer(vocab=_snake_case    ,			unk_token='[UNK]')

								self.assertListEqual(tokenizer.tokenize('')    ,			[])

								self.assertListEqual(tokenizer.tokenize('unwanted running')    ,			['un', '##want', '##ed', 'runn', '##ing'])

								self.assertListEqual(tokenizer.tokenize('unwantedX running')    ,			['[UNK]', 'runn', '##ing'])


							def 						_snake_case							(			self)							->				List[str]:
								self.assertTrue(_is_whitespace(' '))
								self.assertTrue(_is_whitespace('\t'))
								self.assertTrue(_is_whitespace('\r'))
								self.assertTrue(_is_whitespace('\n'))
								self.assertTrue(_is_whitespace('\u00A0'))

								self.assertFalse(_is_whitespace('A'))
								self.assertFalse(_is_whitespace('-'))


							def 						_snake_case							(			self)							->				Union[str, Any]:
								self.assertTrue(_is_control('\u0005'))

								self.assertFalse(_is_control('A'))
								self.assertFalse(_is_control(' '))
								self.assertFalse(_is_control('\t'))
								self.assertFalse(_is_control('\r'))


							def 						_snake_case							(			self)							->				Optional[Any]:
								self.assertTrue(_is_punctuation('-'))
								self.assertTrue(_is_punctuation('$'))
								self.assertTrue(_is_punctuation('`'))
								self.assertTrue(_is_punctuation('.'))

								self.assertFalse(_is_punctuation('A'))
								self.assertFalse(_is_punctuation(' '))


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					self.get_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(_snake_case) for t in ['Test', '\xad', 'test']]    ,			[['[UNK]'], [], ['[UNK]']])

								if self.test_rust_tokenizer:
									UpperCAmelCase_		:      int       	=					self.get_rust_tokenizer()
									self.assertListEqual(
									    [rust_tokenizer.tokenize(_snake_case) for t in ['Test', '\xad', 'test']]    ,			[['[UNK]'], [], ['[UNK]']])


							def 						_snake_case							(			self)							->				Dict:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										UpperCAmelCase_		:      Optional[int]       	=					self.rust_tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)

										UpperCAmelCase_		:      Any       	=					F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer_r.encode_plus(
										    _snake_case    ,			return_attention_mask=_snake_case    ,			return_token_type_ids=_snake_case    ,			return_offsets_mapping=_snake_case    ,			add_special_tokens=_snake_case    ,			)

										UpperCAmelCase_		:      List[Any]       	=					tokenizer_r.do_lower_case if hasattr(_snake_case    ,			'do_lower_case') else False
										UpperCAmelCase_		:      List[str]       	=					(
										    [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), 'A'),
										        ((1, 2), ','),
										        ((3, 5), 'na'),
										        ((5, 6), '##ï'),
										        ((6, 8), '##ve'),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), 'Allen'),
										        ((21, 23), '##NL'),
										        ((23, 24), '##P'),
										        ((25, 33), 'sentence'),
										        ((33, 34), '.'),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										    if not do_lower_case
										    else [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), 'a'),
										        ((1, 2), ','),
										        ((3, 8), 'naive'),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), 'allen'),
										        ((21, 23), '##nl'),
										        ((23, 24), '##p'),
										        ((25, 33), 'sentence'),
										        ((33, 34), '.'),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										)

										self.assertEqual(
										    [e[1] for e in expected_results]    ,			tokenizer_r.convert_ids_to_tokens(tokens['input_ids']))
										self.assertEqual([e[0] for e in expected_results]    ,			tokens['offset_mapping'])


							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_		:      List[Any]       	=					['的', '人', '有']
								UpperCAmelCase_		:      int       	=					''.join(_snake_case)
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										UpperCAmelCase_		:      Any       	=					True
										UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)
										UpperCAmelCase_		:      Tuple       	=					self.rust_tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)

										UpperCAmelCase_		:      Tuple       	=					tokenizer_p.encode(_snake_case    ,			add_special_tokens=_snake_case)
										UpperCAmelCase_		:      Optional[int]       	=					tokenizer_r.encode(_snake_case    ,			add_special_tokens=_snake_case)

										UpperCAmelCase_		:      Dict       	=					tokenizer_r.convert_ids_to_tokens(_snake_case)
										UpperCAmelCase_		:      str       	=					tokenizer_p.convert_ids_to_tokens(_snake_case)

										# it is expected that each Chinese character is not preceded by "##"
										self.assertListEqual(_snake_case    ,			_snake_case)
										self.assertListEqual(_snake_case    ,			_snake_case)

										UpperCAmelCase_		:      Optional[Any]       	=					False
										UpperCAmelCase_		:      Any       	=					self.rust_tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)
										UpperCAmelCase_		:      List[str]       	=					self.tokenizer_class.from_pretrained(_snake_case    ,			**_snake_case)

										UpperCAmelCase_		:      int       	=					tokenizer_r.encode(_snake_case    ,			add_special_tokens=_snake_case)
										UpperCAmelCase_		:      int       	=					tokenizer_p.encode(_snake_case    ,			add_special_tokens=_snake_case)

										UpperCAmelCase_		:      int       	=					tokenizer_r.convert_ids_to_tokens(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer_p.convert_ids_to_tokens(_snake_case)

										# it is expected that only the first Chinese character is not preceded by "##".
										UpperCAmelCase_		:      Dict       	=					[
										    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_snake_case)
										]
										self.assertListEqual(_snake_case    ,			_snake_case)
										self.assertListEqual(_snake_case    ,			_snake_case)


							@slow
							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      List[Any]       	=					self.tokenizer_class(self.vocab_file    ,			self.word_shape_file    ,			self.word_pronunciation_file)

								UpperCAmelCase_		:      Dict       	=					tokenizer.encode('你好'    ,			add_special_tokens=_snake_case)
								UpperCAmelCase_		:      int       	=					tokenizer.encode('你是谁'    ,			add_special_tokens=_snake_case)

								UpperCAmelCase_		:      int       	=					tokenizer.build_inputs_with_special_tokens(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					tokenizer.build_inputs_with_special_tokens(_snake_case    ,			_snake_case)

								assert encoded_sentence == [1] + text + [2]
								assert encoded_pair == [1] + text + [2] + text_a + [2]





							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					self.get_tokenizers(do_lower_case=_snake_case)
								for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										UpperCAmelCase_		:      Dict       	=					'你好，你是谁'
										UpperCAmelCase_		:      List[str]       	=					tokenizer.tokenize(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.convert_tokens_to_ids(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.convert_tokens_to_shape_ids(_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer.convert_tokens_to_pronunciation_ids(_snake_case)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.prepare_for_model(
										    _snake_case    ,			_snake_case    ,			_snake_case    ,			add_special_tokens=_snake_case)

										UpperCAmelCase_		:      Dict       	=					tokenizer.encode_plus(_snake_case    ,			add_special_tokens=_snake_case)

										self.assertEqual(_snake_case    ,			_snake_case)









'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						lowerCAmelCase__										= "platform"

						import jax
						import jax.numpy as jnp

						from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
						    FlaxBlenderbotSmallForConditionalGeneration,
						    FlaxBlenderbotSmallModel,
						    shift_tokens_right,
						)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,)    ->    List[Any]:
	if attention_mask is None:
		UpperCAmelCase_		:      Tuple       	=					np.where(input_ids != config.pad_token_id   ,1   ,0  )
	if decoder_attention_mask is None:
		UpperCAmelCase_		:      List[str]       	=					np.where(decoder_input_ids != config.pad_token_id   ,1   ,0  )
	if head_mask is None:
		UpperCAmelCase_		:      Optional[Any]       	=					np.ones((config.encoder_layers, config.encoder_attention_heads)  )
	if decoder_head_mask is None:
		UpperCAmelCase_		:      Optional[int]       	=					np.ones((config.decoder_layers, config.decoder_attention_heads)  )
	if cross_attn_head_mask is None:
		UpperCAmelCase_		:      List[str]       	=					np.ones((config.decoder_layers, config.decoder_attention_heads)  )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=7    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=99    ,			_snake_case=16    ,			_snake_case=2    ,			_snake_case=4    ,			_snake_case=4    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=32    ,			_snake_case=2    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=0.02    ,			)							->				int:
								UpperCAmelCase_		:      List[Any]       	=					parent
								UpperCAmelCase_		:      Optional[int]       	=					batch_size
								UpperCAmelCase_		:      Tuple       	=					seq_length
								UpperCAmelCase_		:      List[Any]       	=					is_training
								UpperCAmelCase_		:      int       	=					use_labels
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      Dict       	=					hidden_size
								UpperCAmelCase_		:      int       	=					num_hidden_layers
								UpperCAmelCase_		:      int       	=					num_attention_heads
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Dict       	=					hidden_act
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Any       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      int       	=					max_position_embeddings
								UpperCAmelCase_		:      Optional[Any]       	=					eos_token_id
								UpperCAmelCase_		:      Tuple       	=					pad_token_id
								UpperCAmelCase_		:      List[Any]       	=					bos_token_id
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      int       	=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size)    ,			3    ,			self.vocab_size)
								UpperCAmelCase_		:      List[str]       	=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,			dtype=np.intaa))    ,			-1)

								UpperCAmelCase_		:      Optional[int]       	=					shift_tokens_right(_snake_case    ,			1    ,			2)

								UpperCAmelCase_		:      Dict       	=					BlenderbotSmallConfig(
								    vocab_size=self.vocab_size    ,			d_model=self.hidden_size    ,			encoder_layers=self.num_hidden_layers    ,			decoder_layers=self.num_hidden_layers    ,			encoder_attention_heads=self.num_attention_heads    ,			decoder_attention_heads=self.num_attention_heads    ,			encoder_ffn_dim=self.intermediate_size    ,			decoder_ffn_dim=self.intermediate_size    ,			dropout=self.hidden_dropout_prob    ,			attention_dropout=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			eos_token_id=self.eos_token_id    ,			bos_token_id=self.bos_token_id    ,			pad_token_id=self.pad_token_id    ,			initializer_range=self.initializer_range    ,			use_cache=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					prepare_blenderbot_inputs_dict(_snake_case    ,			_snake_case    ,			_snake_case)
								return config, inputs_dict


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					self.prepare_config_and_inputs()
								return config, inputs_dict


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Optional[Any]       	=					20
								UpperCAmelCase_		:      str       	=					model_class_name(_snake_case)

								UpperCAmelCase_		:      Dict       	=					model.encode(inputs_dict['input_ids'])

								UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					(
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								UpperCAmelCase_		:      Optional[int]       	=					model.init_cache(decoder_input_ids.shape[0]    ,			_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,			dtype='i4')

								UpperCAmelCase_		:      Dict       	=					jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]    ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,			)
								UpperCAmelCase_		:      Optional[int]       	=					model.decode(
								    decoder_input_ids[:, :-1]    ,			_snake_case    ,			decoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			decoder_position_ids=_snake_case    ,			)

								UpperCAmelCase_		:      int       	=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,			dtype='i4')
								UpperCAmelCase_		:      Any       	=					model.decode(
								    decoder_input_ids[:, -1:]    ,			_snake_case    ,			decoder_attention_mask=_snake_case    ,			past_key_values=outputs_cache.past_key_values    ,			decoder_position_ids=_snake_case    ,			)

								UpperCAmelCase_		:      Optional[int]       	=					model.decode(_snake_case    ,			_snake_case)

								UpperCAmelCase_		:      List[str]       	=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1e-3    ,			msg=F"""Max diff is {diff}""")





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      int       	=					20
								UpperCAmelCase_		:      Optional[int]       	=					model_class_name(_snake_case)

								UpperCAmelCase_		:      Optional[int]       	=					model.encode(inputs_dict['input_ids'])

								UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					(
								    inputs_dict['decoder_input_ids'],
								    inputs_dict['decoder_attention_mask'],
								)

								UpperCAmelCase_		:      int       	=					jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
								    ]    ,			axis=-1    ,			)

								UpperCAmelCase_		:      Any       	=					model.init_cache(decoder_input_ids.shape[0]    ,			_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]    ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,			)

								UpperCAmelCase_		:      Optional[int]       	=					model.decode(
								    decoder_input_ids[:, :-1]    ,			_snake_case    ,			decoder_attention_mask=_snake_case    ,			past_key_values=_snake_case    ,			decoder_position_ids=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,			dtype='i4')
								UpperCAmelCase_		:      Dict       	=					model.decode(
								    decoder_input_ids[:, -1:]    ,			_snake_case    ,			past_key_values=outputs_cache.past_key_values    ,			decoder_attention_mask=_snake_case    ,			decoder_position_ids=_snake_case    ,			)

								UpperCAmelCase_		:      Any       	=					model.decode(_snake_case    ,			_snake_case    ,			decoder_attention_mask=_snake_case)

								UpperCAmelCase_		:      Dict       	=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
								self.parent.assertTrue(diff < 1e-3    ,			msg=F"""Max diff is {diff}""")



@require_flax
class 					lowercase						(  unittest.TestCase ):
							_lowerCamelCase		:      Union[str, Any]=   99


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      str       	=					np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ]    ,			dtype=np.intaa    ,			)

								UpperCAmelCase_		:      Optional[int]       	=					input_ids.shape[0]
								UpperCAmelCase_		:      Any       	=					BlenderbotSmallConfig(
								    vocab_size=self.vocab_size    ,			d_model=24    ,			encoder_layers=2    ,			decoder_layers=2    ,			encoder_attention_heads=2    ,			decoder_attention_heads=2    ,			encoder_ffn_dim=32    ,			decoder_ffn_dim=32    ,			max_position_embeddings=48    ,			eos_token_id=2    ,			pad_token_id=1    ,			bos_token_id=0    ,			)
								return config, input_ids, batch_size


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					self._get_config_and_data()
								UpperCAmelCase_		:      str       	=					FlaxBlenderbotSmallForConditionalGeneration(_snake_case)
								UpperCAmelCase_		:      Union[str, Any]       	=					lm_model(input_ids=_snake_case)
								UpperCAmelCase_		:      str       	=					(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['logits'].shape    ,			_snake_case)


							def 						_snake_case							(			self)							->				Tuple:
								UpperCAmelCase_		:      Tuple       	=					BlenderbotSmallConfig(
								    vocab_size=self.vocab_size    ,			d_model=14    ,			encoder_layers=2    ,			decoder_layers=2    ,			encoder_attention_heads=2    ,			decoder_attention_heads=2    ,			encoder_ffn_dim=8    ,			decoder_ffn_dim=8    ,			max_position_embeddings=48    ,			)
								UpperCAmelCase_		:      Union[str, Any]       	=					FlaxBlenderbotSmallForConditionalGeneration(_snake_case)
								UpperCAmelCase_		:      Dict       	=					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,			dtype=np.intaa)
								UpperCAmelCase_		:      Union[str, Any]       	=					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,			dtype=np.intaa)
								UpperCAmelCase_		:      List[str]       	=					lm_model(input_ids=_snake_case    ,			decoder_input_ids=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['logits'].shape    ,			_snake_case)





							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      int       	=					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,			dtype=np.intaa)
								UpperCAmelCase_		:      List[str]       	=					shift_tokens_right(_snake_case    ,			1    ,			2)
								UpperCAmelCase_		:      Dict       	=					np.equal(_snake_case    ,			1).astype(np.floataa).sum()
								UpperCAmelCase_		:      Union[str, Any]       	=					np.equal(_snake_case    ,			1).astype(np.floataa).sum()
								self.assertEqual(shifted.shape    ,			input_ids.shape)
								self.assertEqual(_snake_case    ,			n_pad_before - 1)
								self.assertTrue(np.equal(shifted[:, 0]    ,			2).all())



@require_flax
class 					lowercase						(  a_,					unittest.TestCase,					a_ ):
							_lowerCamelCase		:      str=   True
							_lowerCamelCase		:      Union[str, Any]=   (
							    (
							        FlaxBlenderbotSmallModel,
							        FlaxBlenderbotSmallForConditionalGeneration,
							    )
							    if is_flax_available()
							    else ()
							)
							_lowerCamelCase		:      Optional[int]=   (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      List[str]       	=					FlaxBlenderbotSmallModelTester(self)


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(_snake_case    ,			_snake_case    ,			_snake_case)


							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(_snake_case    ,			_snake_case    ,			_snake_case)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[Any]       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:      Dict       	=					self._prepare_for_class(_snake_case    ,			_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					model_class(_snake_case)

										@jax.jit
										def encode_jitted(_snake_case    ,			_snake_case=None    ,			**_snake_case):
											return model.encode(input_ids=_snake_case    ,			attention_mask=_snake_case)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:      Optional[int]       	=					encode_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:      Optional[int]       	=					encode_jitted(**_snake_case).to_tuple()

										self.assertEqual(len(_snake_case)    ,			len(_snake_case))
										for jitted_output, output in zip(_snake_case    ,			_snake_case):
											self.assertEqual(jitted_output.shape    ,			output.shape)


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
										UpperCAmelCase_		:      Tuple       	=					model_class(_snake_case)
										UpperCAmelCase_		:      Union[str, Any]       	=					model.encode(inputs_dict['input_ids']    ,			inputs_dict['attention_mask'])

										UpperCAmelCase_		:      str       	=					{
										    'decoder_input_ids': inputs_dict['decoder_input_ids'],
										    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
										    'encoder_outputs': encoder_outputs,
										}

										@jax.jit
										def decode_jitted(_snake_case    ,			_snake_case    ,			_snake_case):
											return model.decode(
											    decoder_input_ids=_snake_case    ,			decoder_attention_mask=_snake_case    ,			encoder_outputs=_snake_case    ,			)

										with self.subTest('JIT Enabled'):
											UpperCAmelCase_		:      Tuple       	=					decode_jitted(**_snake_case).to_tuple()

										with self.subTest('JIT Disabled'):
											with jax.disable_jit():
												UpperCAmelCase_		:      List[Any]       	=					decode_jitted(**_snake_case).to_tuple()

										self.assertEqual(len(_snake_case)    ,			len(_snake_case))
										for jitted_output, output in zip(_snake_case    ,			_snake_case):
											self.assertEqual(jitted_output.shape    ,			output.shape)





							@slow
							def 						_snake_case							(			self)							->				Any:
								for model_class_name in self.all_model_classes:
									UpperCAmelCase_		:      Any       	=					model_class_name.from_pretrained('facebook/blenderbot_small-90M')
									# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
									UpperCAmelCase_		:      List[Any]       	=					np.ones((1, 1)) * model.config.eos_token_id
									UpperCAmelCase_		:      Dict       	=					model(_snake_case)
									self.assertIsNotNone(_snake_case)






