






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      =				"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_					(						lowercase_	,  lowercase_	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  )				->Tuple:







 """simple docstring"""

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



     """simple docstring"""



     def __init__(		self					:	str  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Any=1_3  ,  lowerCAmelCase__					:	int=7  ,  lowerCAmelCase__					:	Dict=True  ,  lowerCAmelCase__					:	Union[str, Any]=False  ,  lowerCAmelCase__					:	Any=9_9  ,  lowerCAmelCase__					:	List[str]=1_6  ,  lowerCAmelCase__					:	str=2  ,  lowerCAmelCase__					:	Dict=4  ,  lowerCAmelCase__					:	List[str]=4  ,  lowerCAmelCase__					:	Dict="gelu"  ,  lowerCAmelCase__					:	int=0.1  ,  lowerCAmelCase__					:	str=0.1  ,  lowerCAmelCase__					:	str=3_2  ,  lowerCAmelCase__					:	str=2  ,  lowerCAmelCase__					:	str=1  ,  lowerCAmelCase__					:	Union[str, Any]=0  ,  lowerCAmelCase__					:	Optional[int]=2  ,  lowerCAmelCase__					:	Dict=0.02  ,  )							->	List[Any]:

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





     def     __UpperCamelCase						(		self					:	Dict)							->	Union[str, Any]:

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

      SCREAMING_SNAKE_CASE				    =		shift_tokens_right(lowerCAmelCase__  ,  1)

      SCREAMING_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=lowerCAmelCase__  ,  )
      SCREAMING_SNAKE_CASE				    =		prepare_mbart_inputs_dict(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      return config, inputs_dict





     def     __UpperCamelCase						(		self					:	Any)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.prepare_config_and_inputs()
      return config, inputs_dict





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	str)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		2_0
      SCREAMING_SNAKE_CASE				    =		model_class_name(lowerCAmelCase__)

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

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

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

      SCREAMING_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)  ,  )
      SCREAMING_SNAKE_CASE				    =		model.decode(
          decoder_input_ids[:, :-1]  ,  lowerCAmelCase__  ,  decoder_attention_mask=lowerCAmelCase__  ,  past_key_values=lowerCAmelCase__  ,  decoder_position_ids=lowerCAmelCase__  ,  )

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

      SCREAMING_SNAKE_CASE				    =		model.decode(lowerCAmelCase__  ,  lowerCAmelCase__)

      SCREAMING_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					:	List[str]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Optional[Any])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		2_0
      SCREAMING_SNAKE_CASE				    =		model_class_name(lowerCAmelCase__)

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

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

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

      SCREAMING_SNAKE_CASE				    =		model.init_cache(decoder_input_ids.shape[0]  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      SCREAMING_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)  ,  )

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

      SCREAMING_SNAKE_CASE				    =		model.decode(lowerCAmelCase__  ,  lowerCAmelCase__  ,  decoder_attention_mask=lowerCAmelCase__)

      SCREAMING_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 	a_(		unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				Optional[Any]  					=99





     def     __UpperCamelCase						(		self					:	Tuple)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		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  ,  )

      SCREAMING_SNAKE_CASE				    =		input_ids.shape[0]
      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	str)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._get_config_and_data()
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForSequenceClassification(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		model(input_ids=lowerCAmelCase__  ,  decoder_input_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(batch_size, config.num_labels)
      self.assertEqual(outputs['logits'].shape  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	List[str])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._get_config_and_data()
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForQuestionAnswering(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		model(input_ids=lowerCAmelCase__)

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





     def     __UpperCamelCase						(		self					:	Dict)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._get_config_and_data()
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForConditionalGeneration(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		lm_model(input_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['logits'].shape  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		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  ,  )
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForConditionalGeneration(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		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)
      SCREAMING_SNAKE_CASE				    =		np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]  ,  dtype=np.intaa)
      SCREAMING_SNAKE_CASE				    =		lm_model(input_ids=lowerCAmelCase__  ,  decoder_input_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(*summary.shape, config.vocab_size)
      self.assertEqual(outputs['logits'].shape  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	List[str])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		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)
      SCREAMING_SNAKE_CASE				    =		shift_tokens_right(lowerCAmelCase__  ,  1)
      SCREAMING_SNAKE_CASE				    =		np.equal(lowerCAmelCase__  ,  1).astype(np.floataa).sum()
      SCREAMING_SNAKE_CASE				    =		np.equal(lowerCAmelCase__  ,  1).astype(np.floataa).sum()
      self.assertEqual(shifted.shape  ,  input_ids.shape)
      self.assertEqual(lowerCAmelCase__  ,  n_pad_before - 1)
      self.assertTrue(np.equal(shifted[:, 0]  ,  2).all())

@require_flax
class 	a_(		lowercase__			,							unittest.TestCase			,							lowercase__	):



     """simple docstring"""



     __snake_case		:				Any  					=True
     __snake_case		:				List[str]  					=(
         (
             FlaxMBartModel,
             FlaxMBartForConditionalGeneration,
             FlaxMBartForSequenceClassification,
             FlaxMBartForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     __snake_case		:				Optional[int]  					=(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





     def     __UpperCamelCase						(		self					:	int)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		FlaxMBartModelTester(self)





     def     __UpperCamelCase						(		self					:	Tuple)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_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(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[int])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_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__):
        SCREAMING_SNAKE_CASE				    =		self._prepare_for_class(lowerCAmelCase__  ,  lowerCAmelCase__)
        SCREAMING_SNAKE_CASE				    =		model_class(lowerCAmelCase__)

        @jax.jit
        def encode_jitted(lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	int=None  ,  **lowerCAmelCase__					:	Tuple):
         return model.encode(input_ids=lowerCAmelCase__  ,  attention_mask=lowerCAmelCase__)

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

        with self.subTest('JIT Disabled'):
         with jax.disable_jit():
          SCREAMING_SNAKE_CASE				    =		encode_jitted(**lowerCAmelCase__).to_tuple()

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





     def     __UpperCamelCase						(		self					:	int)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_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__):
        SCREAMING_SNAKE_CASE				    =		model_class(lowerCAmelCase__)
        SCREAMING_SNAKE_CASE				    =		model.encode(inputs_dict['input_ids']  ,  inputs_dict['attention_mask'])

        SCREAMING_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(lowerCAmelCase__					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[Any]  ,  lowerCAmelCase__					:	Any):
         return model.decode(
             decoder_input_ids=lowerCAmelCase__  ,  decoder_attention_mask=lowerCAmelCase__  ,  encoder_outputs=lowerCAmelCase__  ,  )

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

        with self.subTest('JIT Disabled'):
         with jax.disable_jit():
          SCREAMING_SNAKE_CASE				    =		decode_jitted(**lowerCAmelCase__).to_tuple()

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





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

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

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



     """simple docstring"""



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





     @cached_property
     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      return AutoTokenizer.from_pretrained(self.model_name)





     @cached_property
     def     __UpperCamelCase						(		self					:	str)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name  ,  from_pt=lowerCAmelCase__)
      return model





     def     __UpperCamelCase						(		self					:	List[str]  ,  **lowerCAmelCase__					:	Dict)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.translate_src_text(**lowerCAmelCase__)
      self.assertListEqual(self.expected_text  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  **lowerCAmelCase__					:	List[str])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.tokenizer(self.src_text  ,  **lowerCAmelCase__  ,  return_tensors='np')
      SCREAMING_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=lowerCAmelCase__  ,  num_beams=2  ,  ).sequences
      SCREAMING_SNAKE_CASE				    =		self.tokenizer.batch_decode(lowerCAmelCase__  ,  skip_special_tokens=lowerCAmelCase__)
      return generated_words





     @slow
     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Optional[int]:

      """simple docstring"""
      self._assert_generated_batch_equal_expected()






'''simple docstring'''





import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


UpperCAmelCase            = logging.get_logger(__name__)

if is_vision_available():
						import PIL.Image
						from PIL import Image
						from PIL.Image import Image as ImageType
else:
						UpperCAmelCase            = object

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf

class     a :




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case							:  Any			=     value




				def __str__(				self						:					Optional[int]   ):
										return self.to_string()




				def 						__snake_case				(				self						:					Optional[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return self._value




				def 						__snake_case				(				self						:					List[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return str(self._value   )




class     a (					__magic_name__					,__magic_name__     ):




				def 						__snake_case				(				self						:					Optional[Any]   ):
										return self._value




				def 						__snake_case				(				self						:					str   ):
										return self._value




class     a (					__magic_name__					,__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

										if not is_vision_available():
																raise ImportError('''PIL must be installed in order to handle images.'''   )

										snake_case							:  Union[str, Any]			=     None
										snake_case							:  List[Any]			=     None
										snake_case							:  List[str]			=     None

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  List[str]			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  Any			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  Union[str, Any]			=     value
										else:
																raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										from IPython.display import Image, display

										display(Image(self.to_string()   )   )




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._raw is not None:
																return self._raw

										if self._path is not None:
																snake_case							:  str			=     Image.open(self._path   )
																return self._raw




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										if self._path is not None:
																return self._path

										if self._raw is not None:
																snake_case							:  int			=     tempfile.mkdtemp()
																snake_case							:  str			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )
																self._raw.save(self._path   )

																return self._path

										if self._tensor is not None:
																snake_case							:  Tuple			=     self._tensor.cpu().detach().numpy()

																# There is likely simpler than load into image into save
																snake_case							:  str			=     Image.fromarray((array * 2_55).astype(np.uinta   )   )

																snake_case							:  List[Any]			=     tempfile.mkdtemp()
																snake_case							:  Optional[Any]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )

																img.save(self._path   )

																return self._path




class     a (					__magic_name__     ):




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int]=1_60_00   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

										if not is_soundfile_availble():
																raise ImportError('''soundfile must be installed in order to handle audio.'''   )

										snake_case							:  List[Any]			=     None
										snake_case							:  int			=     None

										snake_case							:  Tuple			=     samplerate

										if isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  int			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  List[Any]			=     value
										else:
																raise ValueError(F"""Unsupported audio type: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None   ):
										from IPython.display import Audio, display

										display(Audio(self.to_string(),     rate=self.samplerate   )   )




				def 						__snake_case				(				self						:					List[str]   ):
										if self._tensor is not None:
																return self._tensor

										if self._path is not None:
																snake_case,       snake_case							:  Optional[Any]			=     sf.read(self._path   )
																snake_case							:  Any			=     torch.tensor(SCREAMING_SNAKE_CASE_   )
																return self._tensor




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._path is not None:
																return self._path

										if self._tensor is not None:
																snake_case							:  Optional[Any]			=     tempfile.mkdtemp()
																snake_case							:  List[str]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.wav'''   )
																sf.write(self._path,     self._tensor,     samplerate=self.samplerate   )
																return self._path




UpperCAmelCase            = {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCAmelCase            = {str: AgentText}

if is_vision_available():
						UpperCAmelCase            = AgentImage

def   A			(							*A_				:       List[str]     ,				**A_				:       Any							):
						snake_case							:  str			=     [(arg.to_raw() if isinstance(A_     ,				A_							) else arg) for arg in args]
						snake_case							:  Dict			=     {k: (v.to_raw() if isinstance(A_     ,				A_							) else v) for k, v in kwargs.items()}
						return args, kwargs





def   A			(							A_				:       Union[str, Any]     ,				A_				:       Any=None							):
						if isinstance(A_     ,				A_							):
												snake_case							:  Union[str, Any]			=     {}
												for i, (k, v) in enumerate(outputs.items()							):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														snake_case							:  Optional[int]			=     AGENT_TYPE_MAPPING[output_types[i]](A_							)
																								else:
																														snake_case							:  List[str]			=     AgentType(A_							)

																		else:
																								# If the class does not have defined output, then we map according to the type
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(A_     ,				_k							):
																																				snake_case							:  Dict			=     _v(A_							)
																								if k not in decoded_outputs:
																														snake_case							:  List[str]			=     AgentType[v]

						elif isinstance(A_     ,				(list, tuple)							):
												snake_case							:  Union[str, Any]			=     type(A_							)()
												for i, v in enumerate(A_							):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](A_							)							)
																								else:
																														decoded_outputs.append(AgentType(A_							)							)
																		else:
																								# If the class does not have defined output, then we map according to the type
																								snake_case							:  Union[str, Any]			=     False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(A_     ,				_k							):
																																				decoded_outputs.append(_v(A_							)							)
																																				snake_case							:  Tuple			=     True

																								if not found:
																														decoded_outputs.append(AgentType(A_							)							)

						else:
												if output_types[0] in AGENT_TYPE_MAPPING:
																		# If the class has defined outputs, we can map directly according to the class definition
																		snake_case							:  List[str]			=     AGENT_TYPE_MAPPING[output_types[0]](A_							)

												else:
																		# If the class does not have defined output, then we map according to the type
																		for _k, _v in INSTANCE_TYPE_MAPPING.items():
																								if isinstance(A_     ,				_k							):
																														return _v(A_							)
																		return AgentType(A_							)

						return decoded_outputs

