


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_    :					List[str]             =logging.get_logger(__name__)


A_    :					str             ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
A_    :					str             ={
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

A_    :					List[Any]             ={
    'RUCAIBox/mvp': 1024,
}




@lru_cache()
def   lowerCamelCase_       (					):



						"""simple docstring"""




						a_					=		(
						    list(range(ord("""!"""      )       ,    ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      )       ,    ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      )       ,    ord("""ÿ"""      ) + 1      )      )
						)
						a_					=		bs[:]
						a_					=		0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(UpperCamelCase__      )
																		cs.append(2**8 + n      )
																		n += 1
						a_					=		[chr(UpperCamelCase__      ) for n in cs]
						return dict(zip(UpperCamelCase__       ,    UpperCamelCase__      )      )




def   lowerCamelCase_       (					UpperCAmelCase__      ):



						"""simple docstring"""




						a_					=		set()
						a_					=		word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												a_					=		char
						return pairs





class        lowercase_				( UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		VOCAB_FILES_NAMES
							snake_case_        =		PRETRAINED_VOCAB_FILES_MAP
							snake_case_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_        =		['''input_ids''', '''attention_mask''']


							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase="replace"			,    _UpperCAmelCase="<s>"			,    _UpperCAmelCase="</s>"			,    _UpperCAmelCase="</s>"			,    _UpperCAmelCase="<s>"			,    _UpperCAmelCase="<unk>"			,    _UpperCAmelCase="<pad>"			,    _UpperCAmelCase="<mask>"			,    _UpperCAmelCase=False			,    **_UpperCAmelCase			,    ):







													"""simple docstring"""


													a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else bos_token
													a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else eos_token
													a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else sep_token
													a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else cls_token
													a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else unk_token
													a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else mask_token

													super().__init__(
													    errors=_UpperCAmelCase			,    bos_token=_UpperCAmelCase			,    eos_token=_UpperCAmelCase			,    unk_token=_UpperCAmelCase			,    sep_token=_UpperCAmelCase			,    cls_token=_UpperCAmelCase			,    pad_token=_UpperCAmelCase			,    mask_token=_UpperCAmelCase			,    add_prefix_space=_UpperCAmelCase			,    **_UpperCAmelCase			,    )
													with open(_UpperCAmelCase			,    encoding="""utf-8"""		) as vocab_handle:
																			a_					=		json.load(_UpperCAmelCase		)
													a_					=		{v: k for k, v in self.encoder.items()}
													a_					=		errors  # how to handle errors in decoding
													a_					=		bytes_to_unicode()
													a_					=		{v: k for k, v in self.byte_encoder.items()}
													with open(_UpperCAmelCase			,    encoding="""utf-8"""		) as merges_handle:
																			a_					=		merges_handle.read().split("""\n"""		)[1:-1]
													a_					=		[tuple(merge.split()		) for merge in bpe_merges]
													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
													a_					=		{}
													a_					=		add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													a_					=		re.compile(R"""\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""		)


							@property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return len(self.encoder		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													return dict(self.encoder			,    **self.added_tokens_encoder		)


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													if token in self.cache:
																			return self.cache[token]
													a_					=		tuple(_UpperCAmelCase		)
													a_					=		get_pairs(_UpperCAmelCase		)

													if not pairs:
																			return token

													while True:
																			a_					=		min(_UpperCAmelCase			,    key=lambda _UpperCAmelCase		: self.bpe_ranks.get(_UpperCAmelCase			,    float("""inf"""		)		)		)
																			if bigram not in self.bpe_ranks:
																									break
																			a_					=		bigram
																			a_					=		[]
																			a_					=		0
																			while i < len(_UpperCAmelCase		):
																									try:
																															a_					=		word.index(_UpperCAmelCase			,    _UpperCAmelCase		)
																									except ValueError:
																															new_word.extend(word[i:]		)
																															break
																									else:
																															new_word.extend(word[i:j]		)
																															a_					=		j

																									if word[i] == first and i < len(_UpperCAmelCase		) - 1 and word[i + 1] == second:
																															new_word.append(first + second		)
																															i += 2
																									else:
																															new_word.append(word[i]		)
																															i += 1
																			a_					=		tuple(_UpperCAmelCase		)
																			a_					=		new_word
																			if len(_UpperCAmelCase		) == 1:
																									break
																			else:
																									a_					=		get_pairs(_UpperCAmelCase		)
													a_					=		''' '''.join(_UpperCAmelCase		)
													a_					=		word
													return word


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		[]
													for token in re.findall(self.pat			,    _UpperCAmelCase		):
																			a_					=		''''''.join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_UpperCAmelCase		).split(""" """		)		)
													return bpe_tokens


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													return self.encoder.get(_UpperCAmelCase			,    self.encoder.get(self.unk_token		)		)


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													return self.decoder.get(_UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		''''''.join(_UpperCAmelCase		)
													a_					=		bytearray([self.byte_decoder[c] for c in text]		).decode("""utf-8"""			,    errors=self.errors		)
													return text


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):







													"""simple docstring"""


													if not os.path.isdir(_UpperCAmelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
													a_					=		os.path.join(
													    _UpperCAmelCase			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
													a_					=		os.path.join(
													    _UpperCAmelCase			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]		)

													with open(_UpperCAmelCase			,    """w"""			,    encoding="""utf-8"""		) as f:
																			f.write(json.dumps(self.encoder			,    indent=2			,    sort_keys=_UpperCAmelCase			,    ensure_ascii=_UpperCAmelCase		) + """\n"""		)

													a_					=		0
													with open(_UpperCAmelCase			,    """w"""			,    encoding="""utf-8"""		) as writer:
																			writer.write("""#version: 0.2\n"""		)
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,    key=lambda _UpperCAmelCase		: kv[1]		):
																									if index != token_index:
																															logger.warning(
																															    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																															    """ Please check that the tokenizer is not corrupted!"""		)
																															a_					=		token_index
																									writer.write(""" """.join(_UpperCAmelCase		) + """\n"""		)
																									index += 1

													return vocab_file, merge_file


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):







													"""simple docstring"""


													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													a_					=		[self.cls_token_id]
													a_					=		[self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None			,    _UpperCAmelCase = False		):







													"""simple docstring"""


													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=_UpperCAmelCase			,    token_ids_a=_UpperCAmelCase			,    already_has_special_tokens=_UpperCAmelCase		)

													if token_ids_a is None:
																			return [1] + ([0] * len(_UpperCAmelCase		)) + [1]
													return [1] + ([0] * len(_UpperCAmelCase		)) + [1, 1] + ([0] * len(_UpperCAmelCase		)) + [1]


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):







													"""simple docstring"""


													a_					=		[self.sep_token_id]
													a_					=		[self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep		) * [0]
													return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase=False			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space		)
													if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase		) > 0 and not text[0].isspace()):
																			a_					=		''' ''' + text
													return (text, kwargs)



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
	A_    :					List[Any]             ="""platform"""

	import jax
	import jax.numpy as jnp

	from transformers import AutoTokenizer
	from transformers.models.mbart.modeling_flax_mbart import (
	    FlaxMBartForConditionalGeneration,
	    FlaxMBartForQuestionAnswering,
	    FlaxMBartForSequenceClassification,
	    FlaxMBartModel,
	    shift_tokens_right,
	)




def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    ):



						"""simple docstring"""




						if attention_mask is None:
												a_					=		np.where(input_ids != config.pad_token_id       ,    1       ,    0      )
						if decoder_attention_mask is None:
												a_					=		np.where(decoder_input_ids != config.pad_token_id       ,    1       ,    0      )
						if head_mask is None:
												a_					=		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
						if decoder_head_mask is None:
												a_					=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
						if cross_attn_head_mask is None:
												a_					=		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": decoder_attention_mask,
						}





class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=13			,    _UpperCAmelCase=7			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=99			,    _UpperCAmelCase=16			,    _UpperCAmelCase=2			,    _UpperCAmelCase=4			,    _UpperCAmelCase=4			,    _UpperCAmelCase="gelu"			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=32			,    _UpperCAmelCase=2			,    _UpperCAmelCase=1			,    _UpperCAmelCase=0			,    _UpperCAmelCase=2			,    _UpperCAmelCase=0.0_2			,    ):







													"""simple docstring"""


													a_					=		parent
													a_					=		batch_size
													a_					=		seq_length
													a_					=		is_training
													a_					=		use_labels
													a_					=		vocab_size
													a_					=		hidden_size
													a_					=		num_hidden_layers
													a_					=		num_attention_heads
													a_					=		intermediate_size
													a_					=		hidden_act
													a_					=		hidden_dropout_prob
													a_					=		attention_probs_dropout_prob
													a_					=		max_position_embeddings
													a_					=		eos_token_id
													a_					=		pad_token_id
													a_					=		bos_token_id
													a_					=		decoder_start_token_id
													a_					=		initializer_range


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,    self.vocab_size		)			,    3			,    self.vocab_size		)
													a_					=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,    dtype=np.intaa		))			,    -1		)

													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)

													a_					=		MBartConfig(
													    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			,    decoder_start_token_id=self.decoder_start_token_id			,    initializer_range=self.initializer_range			,    use_cache=_UpperCAmelCase			,    )
													a_					=		prepare_mbart_inputs_dict(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													return config, inputs_dict


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.prepare_config_and_inputs()
													return config, inputs_dict


							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		20
													a_					=		model_class_name(_UpperCAmelCase		)

													a_					=		model.encode(inputs_dict["""input_ids"""]		)

													a_			,   a_					=		(
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													a_					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,    dtype="""i4"""		)

													a_					=		jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
													a_					=		model.decode(
													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )

													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
													a_					=		model.decode(
													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_position_ids=_UpperCAmelCase			,    )

													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase		)

													a_					=		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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):







													"""simple docstring"""


													a_					=		20
													a_					=		model_class_name(_UpperCAmelCase		)

													a_					=		model.encode(inputs_dict["""input_ids"""]		)

													a_			,   a_					=		(
													    inputs_dict["""decoder_input_ids"""],
													    inputs_dict["""decoder_attention_mask"""],
													)

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

													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
													a_					=		jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )

													a_					=		model.decode(
													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
													a_					=		model.decode(
													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_attention_mask=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )

													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase		)

													a_					=		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):






							"""simple docstring"""







							snake_case_        =		99


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		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			,    )

													a_					=		input_ids.shape[0]
													a_					=		MBartConfig(
													    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 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_			,   a_					=		self._get_config_and_data()
													a_					=		FlaxMBartForSequenceClassification(_UpperCAmelCase		)
													a_					=		model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
													a_					=		(batch_size, config.num_labels)
													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_			,   a_					=		self._get_config_and_data()
													a_					=		FlaxMBartForQuestionAnswering(_UpperCAmelCase		)
													a_					=		model(input_ids=_UpperCAmelCase		)

													self.assertEqual(outputs["""start_logits"""].shape			,    input_ids.shape		)
													self.assertEqual(outputs["""end_logits"""].shape			,    input_ids.shape		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_			,   a_					=		self._get_config_and_data()
													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
													a_					=		lm_model(input_ids=_UpperCAmelCase		)
													a_					=		(batch_size, input_ids.shape[1], config.vocab_size)
													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		MBartConfig(
													    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			,    )
													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
													a_					=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,    dtype=np.intaa		)
													a_					=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,    dtype=np.intaa		)
													a_					=		lm_model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
													a_					=		(*summary.shape, config.vocab_size)
													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)






							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,    dtype=np.intaa		)
													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)
													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
													self.assertEqual(shifted.shape			,    input_ids.shape		)
													self.assertEqual(_UpperCAmelCase			,    n_pad_before - 1		)
													self.assertTrue(np.equal(shifted[:, 0]			,    2		).all()		)





@require_flax
class        lowercase_				( UpperCamelCase__			,unittest.TestCase			,UpperCamelCase__):






							"""simple docstring"""







							snake_case_        =		True
							snake_case_        =		(
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							snake_case_        =		(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		FlaxMBartModelTester(self		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		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(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__		):
																									a_					=		self._prepare_for_class(_UpperCAmelCase			,    _UpperCAmelCase		)
																									a_					=		model_class(_UpperCAmelCase		)

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

																									with self.subTest("""JIT Enabled"""		):
																															a_					=		encode_jitted(**_UpperCAmelCase		).to_tuple()

																									with self.subTest("""JIT Disabled"""		):
																															with jax.disable_jit():
																																					a_					=		encode_jitted(**_UpperCAmelCase		).to_tuple()

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


							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__		):
																									a_					=		model_class(_UpperCAmelCase		)
																									a_					=		model.encode(inputs_dict["""input_ids"""]			,    inputs_dict["""attention_mask"""]		)

																									a_					=		{
																									    """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(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
																															return model.decode(
																															    decoder_input_ids=_UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    )

																									with self.subTest("""JIT Enabled"""		):
																															a_					=		decode_jitted(**_UpperCAmelCase		).to_tuple()

																									with self.subTest("""JIT Disabled"""		):
																															with jax.disable_jit():
																																					a_					=		decode_jitted(**_UpperCAmelCase		).to_tuple()

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






							@slow
							def 						lowercase__     (   self		):







													"""simple docstring"""


													for model_class_name in self.all_model_classes:
																			a_					=		model_class_name.from_pretrained("""facebook/mbart-large-cc25"""			,    from_pt=_UpperCAmelCase		)
																			# FlaxMBartForSequenceClassification expects eos token in input_ids
																			a_					=		np.ones((1, 1)		) * model.config.eos_token_id
																			a_					=		model(_UpperCAmelCase		)
																			self.assertIsNotNone(_UpperCAmelCase		)





@require_flax
@require_sentencepiece
@require_tokenizers
class        lowercase_				( unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		[
							    ''' UN Chief Says There Is No Military Solution in Syria''',
							]
							snake_case_        =		[
							    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
							]
							snake_case_        =		'''facebook/mbart-large-en-ro'''


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return AutoTokenizer.from_pretrained(self.model_name		)


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,    from_pt=_UpperCAmelCase		)
													return model


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		self.translate_src_text(**_UpperCAmelCase		)
													self.assertListEqual(self.expected_text			,    _UpperCAmelCase		)


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													a_					=		self.tokenizer(self.src_text			,    **_UpperCAmelCase			,    return_tensors="""np"""		)
													a_					=		self.model.generate(
													    model_inputs.input_ids			,    attention_mask=model_inputs.attention_mask			,    decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]			,    early_stopping=_UpperCAmelCase			,    num_beams=2			,    ).sequences
													a_					=		self.tokenizer.batch_decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		)
													return generated_words






							@slow
							def 						lowercase__     (   self		):







													"""simple docstring"""


													self._assert_generated_batch_equal_expected()