

'''simple docstring'''
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
					_lowerCamelCase	 =			"""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       a__    (  _SCREAMING_SNAKE_CASE					:    Tuple     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=None     ,							_SCREAMING_SNAKE_CASE					:    Optional[Any]=None     ,							_SCREAMING_SNAKE_CASE					:    List[Any]=None     ,							_SCREAMING_SNAKE_CASE					:    List[str]=None     ,							_SCREAMING_SNAKE_CASE					:    List[str]=None     ,							_SCREAMING_SNAKE_CASE					:    Union[str, Any]=None     ,							)			-> Optional[int]:


	"""simple docstring"""





	if attention_mask is None:
		UpperCAmelCase_			:	int      =   np.where(input_ids != config.pad_token_id     ,							1     ,							0							)
	if decoder_attention_mask is None:
		UpperCAmelCase_			:	str      =   np.where(decoder_input_ids != config.pad_token_id     ,							1     ,							0							)
	if head_mask is None:
		UpperCAmelCase_			:	List[Any]      =   np.ones((config.encoder_layers, config.encoder_attention_heads)							)
	if decoder_head_mask is None:
		UpperCAmelCase_			:	List[str]      =   np.ones((config.decoder_layers, config.decoder_attention_heads)							)
	if cross_attn_head_mask is None:
		UpperCAmelCase_			:	Optional[int]      =   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  _snake_case						(unittest.TestCase):






			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=2       ,_snake_case=0.02       ,):
				UpperCAmelCase_			:	str      =   parent
				UpperCAmelCase_			:	Optional[int]      =   batch_size
				UpperCAmelCase_			:	Optional[int]      =   seq_length
				UpperCAmelCase_			:	List[str]      =   is_training
				UpperCAmelCase_			:	Optional[Any]      =   use_labels
				UpperCAmelCase_			:	List[Any]      =   vocab_size
				UpperCAmelCase_			:	Tuple      =   hidden_size
				UpperCAmelCase_			:	Any      =   num_hidden_layers
				UpperCAmelCase_			:	Any      =   num_attention_heads
				UpperCAmelCase_			:	int      =   intermediate_size
				UpperCAmelCase_			:	int      =   hidden_act
				UpperCAmelCase_			:	List[str]      =   hidden_dropout_prob
				UpperCAmelCase_			:	Tuple      =   attention_probs_dropout_prob
				UpperCAmelCase_			:	List[Any]      =   max_position_embeddings
				UpperCAmelCase_			:	List[Any]      =   eos_token_id
				UpperCAmelCase_			:	Optional[Any]      =   pad_token_id
				UpperCAmelCase_			:	str      =   bos_token_id
				UpperCAmelCase_			:	Tuple      =   decoder_start_token_id
				UpperCAmelCase_			:	List[str]      =   initializer_range






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

				UpperCAmelCase_			:	List[Any]      =   shift_tokens_right(_snake_case       ,1			)

				UpperCAmelCase_			:	Optional[int]      =   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=_snake_case       ,)
				UpperCAmelCase_			:	str      =   prepare_mbart_inputs_dict(_snake_case       ,_snake_case       ,_snake_case			)
				return config, inputs_dict






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	int      =   self.prepare_config_and_inputs()
				return config, inputs_dict






			def 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	Dict      =   20
				UpperCAmelCase_			:	int      =   model_class_name(_snake_case			)

				UpperCAmelCase_			:	List[Any]      =   model.encode(inputs_dict["input_ids"]			)

				UpperCAmelCase_ , UpperCAmelCase_			:	str      =   (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				UpperCAmelCase_			:	Union[str, Any]      =   model.init_cache(decoder_input_ids.shape[0]       ,_snake_case       ,_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,dtype="i4"			)

				UpperCAmelCase_			:	List[Any]      =   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[Any]      =   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[str]      =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,dtype="i4"			)
				UpperCAmelCase_			:	List[str]      =   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_			:	Any      =   model.decode(_snake_case       ,_snake_case			)

				UpperCAmelCase_			:	List[Any]      =   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 						UpperCamelCase__				(   self       ,_snake_case       ,_snake_case       ,_snake_case			):
				UpperCAmelCase_			:	str      =   20
				UpperCAmelCase_			:	int      =   model_class_name(_snake_case			)

				UpperCAmelCase_			:	List[str]      =   model.encode(inputs_dict["input_ids"]			)

				UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

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

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

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

				UpperCAmelCase_			:	Any      =   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  _snake_case						(unittest.TestCase):
			__A   :     Tuple          =99






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	List[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_			:	Tuple      =   input_ids.shape[0]
				UpperCAmelCase_			:	Optional[Any]      =   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 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   self._get_config_and_data()
				UpperCAmelCase_			:	List[str]      =   FlaxMBartForSequenceClassification(_snake_case			)
				UpperCAmelCase_			:	Any      =   model(input_ids=_snake_case       ,decoder_input_ids=_snake_case			)
				UpperCAmelCase_			:	Any      =   (batch_size, config.num_labels)
				self.assertEqual(outputs["logits"].shape       ,_snake_case			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_			:	Union[str, Any]      =   self._get_config_and_data()
				UpperCAmelCase_			:	Optional[Any]      =   FlaxMBartForQuestionAnswering(_snake_case			)
				UpperCAmelCase_			:	Any      =   model(input_ids=_snake_case			)

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






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   self._get_config_and_data()
				UpperCAmelCase_			:	Union[str, Any]      =   FlaxMBartForConditionalGeneration(_snake_case			)
				UpperCAmelCase_			:	Union[str, Any]      =   lm_model(input_ids=_snake_case			)
				UpperCAmelCase_			:	List[Any]      =   (batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs["logits"].shape       ,_snake_case			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Union[str, Any]      =   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       ,)
				UpperCAmelCase_			:	str      =   FlaxMBartForConditionalGeneration(_snake_case			)
				UpperCAmelCase_			:	str      =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,dtype=np.intaa			)
				UpperCAmelCase_			:	List[str]      =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,dtype=np.intaa			)
				UpperCAmelCase_			:	Dict      =   lm_model(input_ids=_snake_case       ,decoder_input_ids=_snake_case			)
				UpperCAmelCase_			:	Any      =   (*summary.shape, config.vocab_size)
				self.assertEqual(outputs["logits"].shape       ,_snake_case			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Tuple      =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,dtype=np.intaa			)
				UpperCAmelCase_			:	str      =   shift_tokens_right(_snake_case       ,1			)
				UpperCAmelCase_			:	Any      =   np.equal(_snake_case       ,1			).astype(np.floataa			).sum()
				UpperCAmelCase_			:	Optional[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  _snake_case						(__SCREAMING_SNAKE_CASE       ,       unittest.TestCase       ,       __SCREAMING_SNAKE_CASE):
			__A   :     Optional[Any]          =True
			__A   :     List[Any]          =(
			    (
			        FlaxMBartModel,
			        FlaxMBartForConditionalGeneration,
			        FlaxMBartForSequenceClassification,
			        FlaxMBartForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			__A   :     Any          =(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	int      =   FlaxMBartModelTester(self			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	Union[str, 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 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	Optional[int]      =   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 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	Dict      =   self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__			):
						UpperCAmelCase_			:	str      =   self._prepare_for_class(_snake_case       ,_snake_case			)
						UpperCAmelCase_			:	Tuple      =   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_			:	List[str]      =   encode_jitted(**_snake_case			).to_tuple()

						with self.subTest("JIT Disabled"			):
							with jax.disable_jit():
								UpperCAmelCase_			:	Dict      =   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 						UpperCamelCase__				(   self			):
				UpperCAmelCase_ , UpperCAmelCase_			:	List[str]      =   self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCAmelCase_			:	Any      =   {
						    "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_			:	Optional[int]      =   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 						UpperCamelCase__				(   self			):
				for model_class_name in self.all_model_classes:
					UpperCAmelCase_			:	List[Any]      =   model_class_name.from_pretrained("facebook/mbart-large-cc25"       ,from_pt=_snake_case			)
					# FlaxMBartForSequenceClassification expects eos token in input_ids
					UpperCAmelCase_			:	Optional[int]      =   np.ones((1, 1)			) * model.config.eos_token_id
					UpperCAmelCase_			:	Any      =   model(_snake_case			)
					self.assertIsNotNone(_snake_case			)




@require_flax
@require_sentencepiece
@require_tokenizers
class  _snake_case						(unittest.TestCase):
			__A   :     Union[str, Any]          =[
			    " UN Chief Says There Is No Military Solution in Syria",
			]
			__A   :     Union[str, Any]          =[
			    "Şeful ONU declară că nu există o soluţie militară în Siria",
			]
			__A   :     Union[str, Any]          ="facebook/mbart-large-en-ro"






			@cached_property
			def 						UpperCamelCase__				(   self			):
				return AutoTokenizer.from_pretrained(self.model_name			)






			@cached_property
			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[int]      =   FlaxMBartForConditionalGeneration.from_pretrained(self.model_name       ,from_pt=_snake_case			)
				return model






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	Tuple      =   self.translate_src_text(**_snake_case			)
				self.assertListEqual(self.expected_text       ,_snake_case			)






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[Any]      =   self.tokenizer(self.src_text       ,**_snake_case       ,return_tensors="np"			)
				UpperCAmelCase_			:	List[Any]      =   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=_snake_case       ,num_beams=2       ,).sequences
				UpperCAmelCase_			:	Any      =   self.tokenizer.batch_decode(_snake_case       ,skip_special_tokens=_snake_case			)
				return generated_words






			@slow
			def 						UpperCamelCase__				(   self			):
				self._assert_generated_batch_equal_expected()







'''simple docstring'''
from __future__ import annotations

import re


def       a__    (  _SCREAMING_SNAKE_CASE					:    list[str]							)			-> list[str]:


	"""simple docstring"""





	def alphanum_key(_SCREAMING_SNAKE_CASE					:    Optional[Any]							):
		return [int(_SCREAMING_SNAKE_CASE							) if s.isdigit() else s.lower() for s in re.split("([0-9]+)"     ,							_SCREAMING_SNAKE_CASE							)]

	return sorted(_SCREAMING_SNAKE_CASE     ,							key=_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




