


"""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
		lowercase_							 =       '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     UpperCAmelCase    (					_lowercase   :      Union[str, Any] ,      _lowercase   :      Tuple ,      _lowercase   :      str=None ,      _lowercase   :      int=None ,      _lowercase   :      List[str]=None ,      _lowercase   :      Tuple=None ,      _lowercase   :      int=None ,      _lowercase   :      str=None ,      )	->   Tuple:


	"""simple docstring"""




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







				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'''






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







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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

					lowerCAmelCase_			       = shift_tokens_right(UpperCAmelCase			,  1      )

					lowerCAmelCase_			       = 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			,  )
					lowerCAmelCase_			       = prepare_mbart_inputs_dict(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
					return config, inputs_dict







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = 20
					lowerCAmelCase_			       = model_class_name(UpperCAmelCase      )

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

					lowerCAmelCase_       ,  lowerCAmelCase_			       = (
					    inputs_dict['''decoder_input_ids'''],
					    inputs_dict['''decoder_attention_mask'''],
					)

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

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

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

					lowerCAmelCase_			       = model.decode(UpperCAmelCase			,  UpperCAmelCase      )

					lowerCAmelCase_			       = 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     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = 20
					lowerCAmelCase_			       = model_class_name(UpperCAmelCase      )

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

					lowerCAmelCase_       ,  lowerCAmelCase_			       = (
					    inputs_dict['''decoder_input_ids'''],
					    inputs_dict['''decoder_attention_mask'''],
					)

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

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

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

					lowerCAmelCase_			       = model.decode(UpperCAmelCase			,  UpperCAmelCase			,  decoder_attention_mask=UpperCAmelCase      )

					lowerCAmelCase_			       = 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       __a	(   unittest.TestCase       ):
				lowerCamelCase    :					Union[str, Any] 					=99







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = 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			,  )

					lowerCAmelCase_			       = input_ids.shape[0]
					lowerCAmelCase_			       = 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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_			       = self._get_config_and_data()
					lowerCAmelCase_			       = FlaxMBartForSequenceClassification(UpperCAmelCase      )
					lowerCAmelCase_			       = model(input_ids=UpperCAmelCase			,  decoder_input_ids=UpperCAmelCase      )
					lowerCAmelCase_			       = (batch_size, config.num_labels)
					self.assertEqual(outputs['''logits'''].shape			,  UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_			       = self._get_config_and_data()
					lowerCAmelCase_			       = FlaxMBartForQuestionAnswering(UpperCAmelCase      )
					lowerCAmelCase_			       = model(input_ids=UpperCAmelCase      )

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







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_			       = self._get_config_and_data()
					lowerCAmelCase_			       = FlaxMBartForConditionalGeneration(UpperCAmelCase      )
					lowerCAmelCase_			       = lm_model(input_ids=UpperCAmelCase      )
					lowerCAmelCase_			       = (batch_size, input_ids.shape[1], config.vocab_size)
					self.assertEqual(outputs['''logits'''].shape			,  UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,  dtype=np.intaa      )
					lowerCAmelCase_			       = shift_tokens_right(UpperCAmelCase			,  1      )
					lowerCAmelCase_			       = np.equal(UpperCAmelCase			,  1      ).astype(np.floataa      ).sum()
					lowerCAmelCase_			       = 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       __a	(   __snake_case				, unittest.TestCase				, __snake_case       ):
				lowerCamelCase    :					List[str] 					=True
				lowerCamelCase    :					Optional[Any] 					=(
				    (
				        FlaxMBartModel,
				        FlaxMBartForConditionalGeneration,
				        FlaxMBartForSequenceClassification,
				        FlaxMBartForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)
				lowerCamelCase    :					Union[str, Any] 					=(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = FlaxMBartModelTester(self      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_       ,  lowerCAmelCase_			       = 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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_       ,  lowerCAmelCase_			       = 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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_       ,  lowerCAmelCase_			       = self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__      ):
							lowerCAmelCase_			       = self._prepare_for_class(UpperCAmelCase			,  UpperCAmelCase      )
							lowerCAmelCase_			       = 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'''      ):
								lowerCAmelCase_			       = encode_jitted(**UpperCAmelCase      ).to_tuple()

							with self.subTest('''JIT Disabled'''      ):
								with jax.disable_jit():
									lowerCAmelCase_			       = 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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_       ,  lowerCAmelCase_			       = self.model_tester.prepare_config_and_inputs_for_common()

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

							lowerCAmelCase_			       = {
							    '''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'''      ):
								lowerCAmelCase_			       = decode_jitted(**UpperCAmelCase      ).to_tuple()

							with self.subTest('''JIT Disabled'''      ):
								with jax.disable_jit():
									lowerCAmelCase_			       = 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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







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







				@cached_property
				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					return AutoTokenizer.from_pretrained(self.model_name      )







				@cached_property
				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,  from_pt=UpperCAmelCase      )
					return model







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.translate_src_text(**UpperCAmelCase      )
					self.assertListEqual(self.expected_text			,  UpperCAmelCase      )







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.tokenizer(self.src_text			,  **UpperCAmelCase			,  return_tensors='''np'''      )
					lowerCAmelCase_			       = 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
					lowerCAmelCase_			       = self.tokenizer.batch_decode(UpperCAmelCase			,  skip_special_tokens=UpperCAmelCase      )
					return generated_words







				@slow
				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					self._assert_generated_batch_equal_expected()



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_							 =       {
    'configuration_prophetnet': ['PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ProphetNetConfig'],
    'tokenization_prophetnet': ['ProphetNetTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    'PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ProphetNetDecoder',
		    'ProphetNetEncoder',
		    'ProphetNetForCausalLM',
		    'ProphetNetForConditionalGeneration',
		    'ProphetNetModel',
		    'ProphetNetPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
		from .tokenization_prophetnet import ProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_prophetnet import (
				    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ProphetNetDecoder,
				    ProphetNetEncoder,
				    ProphetNetForCausalLM,
				    ProphetNetForConditionalGeneration,
				    ProphetNetModel,
				    ProphetNetPreTrainedModel,
				)

else:
		import sys

		lowercase_							 =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)