

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					=	'''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       snake_case_(_UpperCamelCase ,       _UpperCamelCase ,       _UpperCamelCase=None ,       _UpperCamelCase=None ,       _UpperCamelCase=None ,       _UpperCamelCase=None ,       _UpperCamelCase=None ,       _UpperCamelCase=None ,       )							->	Optional[int]:



  """simple docstring"""






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



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



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

        _snake_case						=							shift_tokens_right(_snake_case			,					1		)

        _snake_case						=							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			,					)
        _snake_case						=							prepare_mbart_inputs_dict(_snake_case			,					_snake_case			,					_snake_case		)
        return config, inputs_dict



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



      def 						UpperCamelCase_		(			self      :				int			,					A__      :				Union[str, Any]			,					A__      :				Dict			,					A__      :				List[str]		)    ->     List[Any]:
        _snake_case						=							20
        _snake_case						=							model_class_name(_snake_case		)

        _snake_case						=							model.encode(inputs_dict['''input_ids''']		)

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

        _snake_case						=							model.init_cache(decoder_input_ids.shape[0]			,					_snake_case			,					_snake_case		)
        _snake_case						=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,					dtype='''i4'''		)

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

        _snake_case						=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,					dtype='''i4'''		)
        _snake_case						=							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			,					)

        _snake_case						=							model.decode(_snake_case			,					_snake_case		)

        _snake_case						=							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      :				Union[str, Any]			,					A__      :				Dict			,					A__      :				List[Any]			,					A__      :				Optional[int]		)    ->     Tuple:
        _snake_case						=							20
        _snake_case						=							model_class_name(_snake_case		)

        _snake_case						=							model.encode(inputs_dict['''input_ids''']		)

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

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

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

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

        _snake_case						=							model.decode(_snake_case			,					_snake_case			,					decoder_attention_mask=_snake_case		)

        _snake_case						=							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     ):
      UpperCamelCase_			:		Tuple						      =				9_9



      def 						UpperCamelCase_		(			self      :				int		)    ->     int:
        _snake_case						=							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			,					)

        _snake_case						=							input_ids.shape[0]
        _snake_case						=							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      :				Optional[Any]		)    ->     Union[str, Any]:
        _snake_case						=							self._get_config_and_data()
        _snake_case						=							FlaxMBartForSequenceClassification(_snake_case		)
        _snake_case						=							model(input_ids=_snake_case			,					decoder_input_ids=_snake_case		)
        _snake_case						=							(batch_size, config.num_labels)
        self.assertEqual(outputs['''logits'''].shape			,					_snake_case		)



      def 						UpperCamelCase_		(			self      :				Optional[Any]		)    ->     List[Any]:
        _snake_case						=							self._get_config_and_data()
        _snake_case						=							FlaxMBartForQuestionAnswering(_snake_case		)
        _snake_case						=							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      :				Optional[Any]		)    ->     List[Any]:
        _snake_case						=							self._get_config_and_data()
        _snake_case						=							FlaxMBartForConditionalGeneration(_snake_case		)
        _snake_case						=							lm_model(input_ids=_snake_case		)
        _snake_case						=							(batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape			,					_snake_case		)



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







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



      def 						UpperCamelCase_		(			self      :				Tuple		)    ->     Any:
        _snake_case						=							FlaxMBartModelTester(self		)



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

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

            @jax.jit
            def encode_jitted(A__      :				int			,					A__      :				int=None			,					**A__      :				List[Any]		):
              return model.encode(input_ids=_snake_case			,					attention_mask=_snake_case		)

            with self.subTest('''JIT Enabled'''		):
              _snake_case						=							encode_jitted(**_snake_case		).to_tuple()

            with self.subTest('''JIT Disabled'''		):
              with jax.disable_jit():
                _snake_case						=							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      :				str		)    ->     Any:
        _snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()

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

            _snake_case						=							{
                "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(A__      :				Dict			,					A__      :				Optional[Any]			,					A__      :				List[Any]		):
              return model.decode(
                  decoder_input_ids=_snake_case			,					decoder_attention_mask=_snake_case			,					encoder_outputs=_snake_case			,					)

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

            with self.subTest('''JIT Disabled'''		):
              with jax.disable_jit():
                _snake_case						=							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      :				int		)    ->     Optional[Any]:
        for model_class_name in self.all_model_classes:
          _snake_case						=							model_class_name.from_pretrained('''facebook/mbart-large-cc25'''			,					from_pt=_snake_case		)
          # FlaxMBartForSequenceClassification expects eos token in input_ids
          _snake_case						=							np.ones((1, 1)		) * model.config.eos_token_id
          _snake_case						=							model(_snake_case		)
          self.assertIsNotNone(_snake_case		)



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



      @cached_property
      def 						UpperCamelCase_		(			self      :				List[Any]		)    ->     Tuple:
        return AutoTokenizer.from_pretrained(self.model_name		)



      @cached_property
      def 						UpperCamelCase_		(			self      :				str		)    ->     Optional[Any]:
        _snake_case						=							FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,					from_pt=_snake_case		)
        return model



      def 						UpperCamelCase_		(			self      :				List[str]			,					**A__      :				Optional[Any]		)    ->     List[Any]:
        _snake_case						=							self.translate_src_text(**_snake_case		)
        self.assertListEqual(self.expected_text			,					_snake_case		)



      def 						UpperCamelCase_		(			self      :				str			,					**A__      :				Optional[Any]		)    ->     List[str]:
        _snake_case						=							self.tokenizer(self.src_text			,					**_snake_case			,					return_tensors='''np'''		)
        _snake_case						=							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
        _snake_case						=							self.tokenizer.batch_decode(_snake_case			,					skip_special_tokens=_snake_case		)
        return generated_words







      @slow
      def 						UpperCamelCase_		(			self      :				Dict		)    ->     List[Any]:
        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()




