






"""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
   _UpperCamelCase				:    List[str]     =						'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  (A_      :Optional[Any]	,			A_      :List[str]	,			A_      :Any=None	,			A_      :str=None	,			A_      :Any=None	,			A_      :Union[str, Any]=None	,			A_      :List[Any]=None	,			A_      :Optional[int]=None	,			):







  '''simple docstring'''

  if attention_mask is None:
    a     :			Dict				=   np.where(input_ids != config.pad_token_id	,			1	,			0      )
  if decoder_attention_mask is None:
    a     :			Optional[int]				=   np.where(decoder_input_ids != config.pad_token_id	,			1	,			0      )
  if head_mask is None:
    a     :			Union[str, Any]				=   np.ones((config.encoder_layers, config.encoder_attention_heads)      )
  if decoder_head_mask is None:
    a     :			List[str]				=   np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  if cross_attn_head_mask is None:
    a     :			Tuple				=   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	:					Tuple				,		A	:					Any				,		A	:					List[Any]=1_3				,		A	:					Union[str, Any]=7				,		A	:					int=True				,		A	:					Any=False				,		A	:					Any=9_9				,		A	:					List[str]=1_6				,		A	:					Optional[int]=2				,		A	:					int=4				,		A	:					str=4				,		A	:					Dict="gelu"				,		A	:					List[Any]=0.1				,		A	:					Any=0.1				,		A	:					Optional[Any]=3_2				,		A	:					Dict=2				,		A	:					Optional[int]=1				,		A	:					str=0				,		A	:					int=2				,		A	:					Optional[Any]=0.02				,		):


      '''simple docstring'''




      a     :			int				=   parent
      a     :			List[Any]				=   batch_size
      a     :			Dict				=   seq_length
      a     :			Any				=   is_training
      a     :			Union[str, Any]				=   use_labels
      a     :			List[str]				=   vocab_size
      a     :			Union[str, Any]				=   hidden_size
      a     :			Optional[int]				=   num_hidden_layers
      a     :			Tuple				=   num_attention_heads
      a     :			List[str]				=   intermediate_size
      a     :			Dict				=   hidden_act
      a     :			int				=   hidden_dropout_prob
      a     :			Dict				=   attention_probs_dropout_prob
      a     :			List[str]				=   max_position_embeddings
      a     :			Union[str, Any]				=   eos_token_id
      a     :			Any				=   pad_token_id
      a     :			Optional[int]				=   bos_token_id
      a     :			int				=   decoder_start_token_id
      a     :			Dict				=   initializer_range





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




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

      a     :			Union[str, Any]				=   shift_tokens_right(A				,		1			)

      a     :			Dict				=   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=A				,		)
      a     :			Optional[int]				=   prepare_mbart_inputs_dict(A				,		A				,		A			)
      return config, inputs_dict





    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a,     a     :			Dict				=   self.prepare_config_and_inputs()
      return config, inputs_dict





    def 				lowerCamelCase__      (							self	:					List[Any]				,		A	:					List[Any]				,		A	:					str				,		A	:					Optional[int]			):


      '''simple docstring'''




      a     :			Any				=   2_0
      a     :			Union[str, Any]				=   model_class_name(A			)

      a     :			Any				=   model.encode(inputs_dict['input_ids']			)

      a,     a     :			str				=   (
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      a     :			Union[str, Any]				=   model.init_cache(decoder_input_ids.shape[0]				,		A				,		A			)
      a     :			str				=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,		dtype='i4'			)

      a     :			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)				,		)
      a     :			int				=   model.decode(
          decoder_input_ids[:, :-1]				,		A				,		decoder_attention_mask=A				,		past_key_values=A				,		decoder_position_ids=A				,		)

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

      a     :			Optional[int]				=   model.decode(A				,		A			)

      a     :			int				=   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	:					Dict				,		A	:					Optional[Any]				,		A	:					Optional[int]				,		A	:					str			):


      '''simple docstring'''




      a     :			Optional[Any]				=   2_0
      a     :			Dict				=   model_class_name(A			)

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

      a,     a     :			Optional[Any]				=   (
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

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

      a     :			Tuple				=   model.init_cache(decoder_input_ids.shape[0]				,		A				,		A			)
      a     :			str				=   jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]				,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,		)

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

      a     :			Union[str, Any]				=   model.decode(A				,		A				,		decoder_attention_mask=A			)

      a     :			Tuple				=   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       ):
    __magic_name__         =     99





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      a     :			List[Any]				=   np.array(
          [
              [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
              [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
              [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
              [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
              [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
              [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
              [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
              [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
              [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
              [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
              [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
              [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
              [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
          ]				,		dtype=np.intaa				,		)

      a     :			Optional[Any]				=   input_ids.shape[0]
      a     :			Optional[int]				=   MBartConfig(
          vocab_size=self.vocab_size				,		d_model=2_4				,		encoder_layers=2				,		decoder_layers=2				,		encoder_attention_heads=2				,		decoder_attention_heads=2				,		encoder_ffn_dim=3_2				,		decoder_ffn_dim=3_2				,		max_position_embeddings=4_8				,		eos_token_id=2				,		pad_token_id=1				,		bos_token_id=0				,		)
      return config, input_ids, batch_size





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a,     a,     a     :			int				=   self._get_config_and_data()
      a     :			Dict				=   FlaxMBartForSequenceClassification(A			)
      a     :			str				=   model(input_ids=A				,		decoder_input_ids=A			)
      a     :			int				=   (batch_size, config.num_labels)
      self.assertEqual(outputs['logits'].shape				,		A			)





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a,     a,     a     :			Any				=   self._get_config_and_data()
      a     :			int				=   FlaxMBartForQuestionAnswering(A			)
      a     :			int				=   model(input_ids=A			)

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





    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a,     a,     a     :			Dict				=   self._get_config_and_data()
      a     :			Dict				=   FlaxMBartForConditionalGeneration(A			)
      a     :			int				=   lm_model(input_ids=A			)
      a     :			Optional[int]				=   (batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['logits'].shape				,		A			)





    def 				lowerCamelCase__      (							self	:					Union[str, Any]			):


      '''simple docstring'''




      a     :			List[str]				=   MBartConfig(
          vocab_size=self.vocab_size				,		d_model=1_4				,		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=4_8				,		)
      a     :			str				=   FlaxMBartForConditionalGeneration(A			)
      a     :			Tuple				=   np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]				,		dtype=np.intaa			)
      a     :			List[str]				=   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]				,		dtype=np.intaa			)
      a     :			str				=   lm_model(input_ids=A				,		decoder_input_ids=A			)
      a     :			List[Any]				=   (*summary.shape, config.vocab_size)
      self.assertEqual(outputs['logits'].shape				,		A			)





    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			List[Any]				=   np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]				,		dtype=np.intaa			)
      a     :			Dict				=   shift_tokens_right(A				,		1			)
      a     :			Dict				=   np.equal(A				,		1			).astype(np.floataa			).sum()
      a     :			str				=   np.equal(A				,		1			).astype(np.floataa			).sum()
      self.assertEqual(shifted.shape				,		input_ids.shape			)
      self.assertEqual(A				,		n_pad_before - 1			)
      self.assertTrue(np.equal(shifted[:, 0]				,		2			).all()			)






@require_flax
class 						snake_case  (  UpperCAmelCase   , unittest.TestCase   , UpperCAmelCase       ):
    __magic_name__         =     True
    __magic_name__         =     (
        (
            FlaxMBartModel,
            FlaxMBartForConditionalGeneration,
            FlaxMBartForSequenceClassification,
            FlaxMBartForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )
    __magic_name__         =     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      a     :			List[str]				=   FlaxMBartModelTester(self			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a,     a     :			List[Any]				=   self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(A				,		A				,		A			)





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a,     a     :			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(A				,		A				,		A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a,     a     :			Union[str, Any]				=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__			):
          a     :			List[str]				=   self._prepare_for_class(A				,		A			)
          a     :			Tuple				=   model_class(A			)

          @jax.jit
          def encode_jitted(A	:					Optional[Any]				,		A	:					List[Any]=None				,		**A	:					str			):
            return model.encode(input_ids=A				,		attention_mask=A			)

          with self.subTest('JIT Enabled'			):
            a     :			str				=   encode_jitted(**A			).to_tuple()

          with self.subTest('JIT Disabled'			):
            with jax.disable_jit():
              a     :			Optional[Any]				=   encode_jitted(**A			).to_tuple()

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





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a,     a     :			Tuple				=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__			):
          a     :			int				=   model_class(A			)
          a     :			List[Any]				=   model.encode(inputs_dict['input_ids']				,		inputs_dict['attention_mask']			)

          a     :			List[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(A	:					List[Any]				,		A	:					Tuple				,		A	:					List[Any]			):
            return model.decode(
                decoder_input_ids=A				,		decoder_attention_mask=A				,		encoder_outputs=A				,		)

          with self.subTest('JIT Enabled'			):
            a     :			str				=   decode_jitted(**A			).to_tuple()

          with self.subTest('JIT Disabled'			):
            with jax.disable_jit():
              a     :			Dict				=   decode_jitted(**A			).to_tuple()

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





    @slow
    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      for model_class_name in self.all_model_classes:
        a     :			List[str]				=   model_class_name.from_pretrained('facebook/mbart-large-cc25'				,		from_pt=A			)
        # FlaxMBartForSequenceClassification expects eos token in input_ids
        a     :			str				=   np.ones((1, 1)			) * model.config.eos_token_id
        a     :			Optional[int]				=   model(A			)
        self.assertIsNotNone(A			)






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





    @cached_property
    def 				lowerCamelCase__      (							self	:					Optional[Any]			):


      '''simple docstring'''




      return AutoTokenizer.from_pretrained(self.model_name			)





    @cached_property
    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Any				=   FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				,		from_pt=A			)
      return model





    def 				lowerCamelCase__      (							self	:					Optional[int]				,		**A	:					Optional[Any]			):


      '''simple docstring'''




      a     :			int				=   self.translate_src_text(**A			)
      self.assertListEqual(self.expected_text				,		A			)





    def 				lowerCamelCase__      (							self	:					Any				,		**A	:					List[str]			):


      '''simple docstring'''




      a     :			Tuple				=   self.tokenizer(self.src_text				,		**A				,		return_tensors='np'			)
      a     :			Optional[int]				=   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=A				,		num_beams=2				,		).sequences
      a     :			str				=   self.tokenizer.batch_decode(A				,		skip_special_tokens=A			)
      return generated_words





    @slow
    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      self._assert_generated_batch_equal_expected()












"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   torch.tensor(
          [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]				,		device=A				,		dtype=torch.float				,		)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]				,		A				,		atol=1E-4			)			)




