

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 _a			(	SCREAMING_SNAKE_CASE		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      List[Any]   , SCREAMING_SNAKE_CASE		:      Dict=None   , SCREAMING_SNAKE_CASE		:      str=None   , SCREAMING_SNAKE_CASE		:      Optional[int]=None   , SCREAMING_SNAKE_CASE		:      Tuple=None   , SCREAMING_SNAKE_CASE		:      Any=None   , SCREAMING_SNAKE_CASE		:      Union[str, Any]=None   , )   ->   Tuple:

 """simple docstring"""






 if attention_mask is None:
  __lowerCAmelCase:       Any            =				np.where(input_ids != config.pad_token_id   , 1   , 0		)
 if decoder_attention_mask is None:
  __lowerCAmelCase:       List[str]            =				np.where(decoder_input_ids != config.pad_token_id   , 1   , 0		)
 if head_mask is None:
  __lowerCAmelCase:       Optional[int]            =				np.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  __lowerCAmelCase:       List[str]            =				np.ones((config.decoder_layers, config.decoder_attention_heads)		)
 if cross_attn_head_mask is None:
  __lowerCAmelCase:       List[str]            =				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      :		Tuple      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		List[str]=1_3      ,  UpperCAmelCase      :		Optional[int]=7      ,  UpperCAmelCase      :		Union[str, Any]=True      ,  UpperCAmelCase      :		Any=False      ,  UpperCAmelCase      :		List[str]=9_9      ,  UpperCAmelCase      :		List[str]=1_6      ,  UpperCAmelCase      :		List[Any]=2      ,  UpperCAmelCase      :		List[Any]=4      ,  UpperCAmelCase      :		Optional[int]=4      ,  UpperCAmelCase      :		List[Any]="gelu"      ,  UpperCAmelCase      :		Union[str, Any]=0.1      ,  UpperCAmelCase      :		Tuple=0.1      ,  UpperCAmelCase      :		Optional[int]=3_2      ,  UpperCAmelCase      :		List[Any]=2      ,  UpperCAmelCase      :		Union[str, Any]=1      ,  UpperCAmelCase      :		Optional[Any]=0      ,  UpperCAmelCase      :		Tuple=2      ,  UpperCAmelCase      :		Tuple=0.02      ,  )							->     Any:
      __lowerCAmelCase:       List[str]            =				parent
      __lowerCAmelCase:       int            =				batch_size
      __lowerCAmelCase:       List[str]            =				seq_length
      __lowerCAmelCase:       List[str]            =				is_training
      __lowerCAmelCase:       Optional[Any]            =				use_labels
      __lowerCAmelCase:       Dict            =				vocab_size
      __lowerCAmelCase:       Tuple            =				hidden_size
      __lowerCAmelCase:       Optional[int]            =				num_hidden_layers
      __lowerCAmelCase:       List[Any]            =				num_attention_heads
      __lowerCAmelCase:       Tuple            =				intermediate_size
      __lowerCAmelCase:       Any            =				hidden_act
      __lowerCAmelCase:       str            =				hidden_dropout_prob
      __lowerCAmelCase:       Optional[int]            =				attention_probs_dropout_prob
      __lowerCAmelCase:       List[Any]            =				max_position_embeddings
      __lowerCAmelCase:       str            =				eos_token_id
      __lowerCAmelCase:       Tuple            =				pad_token_id
      __lowerCAmelCase:       Optional[Any]            =				bos_token_id
      __lowerCAmelCase:       Union[str, Any]            =				decoder_start_token_id
      __lowerCAmelCase:       Any            =				initializer_range


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

      __lowerCAmelCase:       str            =				shift_tokens_right(UpperCAmelCase      ,  1 )

      __lowerCAmelCase:       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=UpperCAmelCase      ,  )
      __lowerCAmelCase:       List[Any]            =				prepare_mbart_inputs_dict(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )
      return config, inputs_dict


     def   UpperCAmelCase  (							self      :		Any )							->     Union[str, Any]:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Any            =				self.prepare_config_and_inputs()
      return config, inputs_dict


     def   UpperCAmelCase  (							self      :		Any      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		int )							->     List[str]:
      __lowerCAmelCase:       Dict            =				2_0
      __lowerCAmelCase:       Dict            =				model_class_name(UpperCAmelCase )

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

      __lowerCAmelCase     ,							__lowerCAmelCase:       Tuple            =				(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      __lowerCAmelCase:       Any            =				model.init_cache(decoder_input_ids.shape[0]      ,  UpperCAmelCase      ,  UpperCAmelCase )
      __lowerCAmelCase:       Dict            =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,  dtype='i4' )

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

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

      __lowerCAmelCase:       Dict            =				model.decode(UpperCAmelCase      ,  UpperCAmelCase )

      __lowerCAmelCase:       List[Any]            =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
      self.parent.assertTrue(diff < 1E-3      ,  msg=F'''Max diff is {diff}''' )


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Optional[int] )							->     List[Any]:
      __lowerCAmelCase:       Optional[int]            =				2_0
      __lowerCAmelCase:       Any            =				model_class_name(UpperCAmelCase )

      __lowerCAmelCase:       str            =				model.encode(inputs_dict['input_ids'] )

      __lowerCAmelCase     ,							__lowerCAmelCase:       Tuple            =				(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

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

      __lowerCAmelCase:       Optional[int]            =				model.init_cache(decoder_input_ids.shape[0]      ,  UpperCAmelCase      ,  UpperCAmelCase )
      __lowerCAmelCase:       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)      ,  )

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

      __lowerCAmelCase:       str            =				model.decode(UpperCAmelCase      ,  UpperCAmelCase      ,  decoder_attention_mask=UpperCAmelCase )

      __lowerCAmelCase:       Optional[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}''' )







@require_flax
class 							A_			(		unittest.TestCase ):
     _lowercase      :					Optional[int]       =     9_9


     def   UpperCAmelCase  (							self      :		int )							->     List[str]:
      __lowerCAmelCase:       Union[str, 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      ,  )

      __lowerCAmelCase:       Any            =				input_ids.shape[0]
      __lowerCAmelCase:       Dict            =				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 )							->     int:
      __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       Tuple            =				self._get_config_and_data()
      __lowerCAmelCase:       int            =				FlaxMBartForSequenceClassification(UpperCAmelCase )
      __lowerCAmelCase:       str            =				model(input_ids=UpperCAmelCase      ,  decoder_input_ids=UpperCAmelCase )
      __lowerCAmelCase:       str            =				(batch_size, config.num_labels)
      self.assertEqual(outputs['logits'].shape      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Dict )							->     Dict:
      __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				self._get_config_and_data()
      __lowerCAmelCase:       Optional[int]            =				FlaxMBartForQuestionAnswering(UpperCAmelCase )
      __lowerCAmelCase:       Optional[Any]            =				model(input_ids=UpperCAmelCase )

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


     def   UpperCAmelCase  (							self      :		Tuple )							->     Dict:
      __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       Any            =				self._get_config_and_data()
      __lowerCAmelCase:       Optional[int]            =				FlaxMBartForConditionalGeneration(UpperCAmelCase )
      __lowerCAmelCase:       List[str]            =				lm_model(input_ids=UpperCAmelCase )
      __lowerCAmelCase:       Tuple            =				(batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['logits'].shape      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Tuple )							->     Optional[int]:
      __lowerCAmelCase:       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      ,  )
      __lowerCAmelCase:       str            =				FlaxMBartForConditionalGeneration(UpperCAmelCase )
      __lowerCAmelCase:       Optional[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, 2, 1]]      ,  dtype=np.intaa )
      __lowerCAmelCase:       str            =				np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]      ,  dtype=np.intaa )
      __lowerCAmelCase:       Optional[int]            =				lm_model(input_ids=UpperCAmelCase      ,  decoder_input_ids=UpperCAmelCase )
      __lowerCAmelCase:       int            =				(*summary.shape, config.vocab_size)
      self.assertEqual(outputs['logits'].shape      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		int )							->     Dict:
      __lowerCAmelCase:       int            =				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 )
      __lowerCAmelCase:       str            =				shift_tokens_right(UpperCAmelCase      ,  1 )
      __lowerCAmelCase:       Union[str, Any]            =				np.equal(UpperCAmelCase      ,  1 ).astype(np.floataa ).sum()
      __lowerCAmelCase:       int            =				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__ ):
     _lowercase      :					Optional[Any]       =     True
     _lowercase      :					Dict       =     (
         (
             FlaxMBartModel,
             FlaxMBartForConditionalGeneration,
             FlaxMBartForSequenceClassification,
             FlaxMBartForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     _lowercase      :					Any       =     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     List[Any]:
      __lowerCAmelCase:       Any            =				FlaxMBartModelTester(self )


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     Tuple:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		str )							->     Union[str, Any]:
      __lowerCAmelCase     ,							__lowerCAmelCase:       str            =				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   UpperCAmelCase  (							self      :		Dict )							->     str:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Any            =				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__ ):
        __lowerCAmelCase:       Any            =				self._prepare_for_class(UpperCAmelCase      ,  UpperCAmelCase )
        __lowerCAmelCase:       Tuple            =				model_class(UpperCAmelCase )

        @jax.jit
        def encode_jitted(UpperCAmelCase      :		Dict      ,  UpperCAmelCase      :		Optional[int]=None      ,  **UpperCAmelCase      :		Dict ):
         return model.encode(input_ids=UpperCAmelCase      ,  attention_mask=UpperCAmelCase )

        with self.subTest('JIT Enabled' ):
         __lowerCAmelCase:       List[Any]            =				encode_jitted(**UpperCAmelCase ).to_tuple()

        with self.subTest('JIT Disabled' ):
         with jax.disable_jit():
          __lowerCAmelCase:       Union[str, Any]            =				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   UpperCAmelCase  (							self      :		Dict )							->     Union[str, Any]:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Any            =				self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCAmelCase:       Optional[int]            =				{
            '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      :		str      ,  UpperCAmelCase      :		int      ,  UpperCAmelCase      :		List[Any] ):
         return model.decode(
             decoder_input_ids=UpperCAmelCase      ,  decoder_attention_mask=UpperCAmelCase      ,  encoder_outputs=UpperCAmelCase      ,  )

        with self.subTest('JIT Enabled' ):
         __lowerCAmelCase:       Any            =				decode_jitted(**UpperCAmelCase ).to_tuple()

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







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


     @cached_property
     def   UpperCAmelCase  (							self      :		Any )							->     Optional[int]:
      return AutoTokenizer.from_pretrained(self.model_name )


     @cached_property
     def   UpperCAmelCase  (							self      :		List[Any] )							->     Any:
      __lowerCAmelCase:       Optional[int]            =				FlaxMBartForConditionalGeneration.from_pretrained(self.model_name      ,  from_pt=UpperCAmelCase )
      return model


     def   UpperCAmelCase  (							self      :		int      ,  **UpperCAmelCase      :		int )							->     Tuple:
      __lowerCAmelCase:       Optional[int]            =				self.translate_src_text(**UpperCAmelCase )
      self.assertListEqual(self.expected_text      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		str      ,  **UpperCAmelCase      :		Dict )							->     Any:
      __lowerCAmelCase:       List[Any]            =				self.tokenizer(self.src_text      ,  **UpperCAmelCase      ,  return_tensors='np' )
      __lowerCAmelCase:       Any            =				self.model.generate(
          model_inputs.input_ids      ,  attention_mask=model_inputs.attention_mask      ,  decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']      ,  early_stopping=UpperCAmelCase      ,  num_beams=2      ,  ).sequences
      __lowerCAmelCase:       List[str]            =				self.tokenizer.batch_decode(UpperCAmelCase      ,  skip_special_tokens=UpperCAmelCase )
      return generated_words


     @slow
     def   UpperCAmelCase  (							self      :		str )							->     Any:
      self._assert_generated_batch_equal_expected()








import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
  from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import PoolFormerImageProcessor






class 							A_			(		snake_case__ ):


     def   UpperCAmelCase  (							self      :		int )							->     Tuple:
      __lowerCAmelCase:       Optional[Any]            =				self.config_class(**self.inputs_dict )
      self.parent.assertTrue(hasattr(UpperCAmelCase      ,  'hidden_sizes' ) )
      self.parent.assertTrue(hasattr(UpperCAmelCase      ,  'num_encoder_blocks' ) )







class 							A_			:


     def __init__(							self      :		Optional[int]      ,  UpperCAmelCase      :		Any      ,  UpperCAmelCase      :		List[str]=1_3      ,  UpperCAmelCase      :		Optional[Any]=6_4      ,  UpperCAmelCase      :		Union[str, Any]=3      ,  UpperCAmelCase      :		Optional[Any]=4      ,  UpperCAmelCase      :		Any=[2, 2, 2, 2]      ,  UpperCAmelCase      :		Optional[Any]=[8, 4, 2, 1]      ,  UpperCAmelCase      :		str=[1_6, 3_2, 6_4, 1_2_8]      ,  UpperCAmelCase      :		str=[1, 4, 8, 1_6]      ,  UpperCAmelCase      :		Optional[Any]=False      ,  UpperCAmelCase      :		str=True      ,  UpperCAmelCase      :		Optional[int]="gelu"      ,  UpperCAmelCase      :		Union[str, Any]=0.1      ,  UpperCAmelCase      :		Optional[Any]=0.02      ,  UpperCAmelCase      :		List[Any]=3      ,  UpperCAmelCase      :		Optional[int]=None      ,  )							->     Any:
      __lowerCAmelCase:       List[str]            =				parent
      __lowerCAmelCase:       Union[str, Any]            =				batch_size
      __lowerCAmelCase:       Union[str, Any]            =				image_size
      __lowerCAmelCase:       str            =				num_channels
      __lowerCAmelCase:       Tuple            =				num_encoder_blocks
      __lowerCAmelCase:       Optional[int]            =				sr_ratios
      __lowerCAmelCase:       Union[str, Any]            =				depths
      __lowerCAmelCase:       int            =				hidden_sizes
      __lowerCAmelCase:       List[Any]            =				downsampling_rates
      __lowerCAmelCase:       List[Any]            =				is_training
      __lowerCAmelCase:       Union[str, Any]            =				use_labels
      __lowerCAmelCase:       Union[str, Any]            =				hidden_act
      __lowerCAmelCase:       Any            =				hidden_dropout_prob
      __lowerCAmelCase:       List[Any]            =				initializer_range
      __lowerCAmelCase:       Dict            =				num_labels
      __lowerCAmelCase:       List[Any]            =				scope


     def   UpperCAmelCase  (							self      :		Optional[int] )							->     List[str]:
      __lowerCAmelCase:       Dict            =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

      __lowerCAmelCase:       str            =				None
      if self.use_labels:
       __lowerCAmelCase:       int            =				ids_tensor([self.batch_size, self.image_size, self.image_size]      ,  self.num_labels )

      __lowerCAmelCase:       Dict            =				PoolFormerConfig(
          image_size=self.image_size      ,  num_channels=self.num_channels      ,  num_encoder_blocks=self.num_encoder_blocks      ,  depths=self.depths      ,  hidden_sizes=self.hidden_sizes      ,  hidden_act=self.hidden_act      ,  hidden_dropout_prob=self.hidden_dropout_prob      ,  initializer_range=self.initializer_range      ,  )

      return config, pixel_values, labels


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		Tuple      ,  UpperCAmelCase      :		int      ,  UpperCAmelCase      :		List[Any] )							->     str:
      __lowerCAmelCase:       Any            =				PoolFormerModel(config=UpperCAmelCase )
      model.to(UpperCAmelCase )
      model.eval()
      __lowerCAmelCase:       List[str]            =				model(UpperCAmelCase )
      __lowerCAmelCase:       Union[str, Any]            =				self.image_size // 32.0
      self.parent.assertEqual(
          result.last_hidden_state.shape      ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )


     def   UpperCAmelCase  (							self      :		Any )							->     int:
      __lowerCAmelCase:       List[str]            =				self.prepare_config_and_inputs()
      __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				config_and_inputs
      __lowerCAmelCase:       List[str]            =				{'pixel_values': pixel_values}
      return config, inputs_dict







@require_torch
class 							A_			(		snake_case__							,     snake_case__							,     unittest.TestCase ):
     _lowercase      :					Optional[Any]       =     (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     _lowercase      :					List[Any]       =     (
         {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     _lowercase      :					Tuple       =     False
     _lowercase      :					Tuple       =     False
     _lowercase      :					List[Any]       =     False
     _lowercase      :					Any       =     False
     _lowercase      :					List[Any]       =     False


     def   UpperCAmelCase  (							self      :		str )							->     Any:
      __lowerCAmelCase:       int            =				PoolFormerModelTester(self )
      __lowerCAmelCase:       int            =				PoolFormerConfigTester(self      ,  config_class=UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     int:
      self.config_tester.run_common_tests()


     def   UpperCAmelCase  (							self      :		int )							->     int:
      __lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase )


     @unittest.skip('PoolFormer does not use inputs_embeds' )
     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Union[str, Any]:
      pass


     @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods' )
     def   UpperCAmelCase  (							self      :		int )							->     List[str]:
      pass


     def   UpperCAmelCase  (							self      :		List[str] )							->     List[str]:
      def check_hidden_states_output(UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		int      ,  UpperCAmelCase      :		List[Any] ):
       __lowerCAmelCase:       Union[str, Any]            =				model_class(UpperCAmelCase )
       model.to(UpperCAmelCase )
       model.eval()

       with torch.no_grad():
        __lowerCAmelCase:       Dict            =				model(**self._prepare_for_class(UpperCAmelCase      ,  UpperCAmelCase ) )

       __lowerCAmelCase:       int            =				outputs.hidden_states

       __lowerCAmelCase:       Optional[int]            =				self.model_tester.num_encoder_blocks
       self.assertEqual(len(UpperCAmelCase )      ,  UpperCAmelCase )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:] )      ,  [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ]      ,  )

      __lowerCAmelCase     ,							__lowerCAmelCase:       Dict            =				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:       Optional[int]            =				True
       check_hidden_states_output(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __lowerCAmelCase:       Optional[Any]            =				True

       check_hidden_states_output(UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		List[Any] )							->     Optional[int]:
      if not self.model_tester.is_training:
       return

      __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCAmelCase:       Optional[int]            =				True

      for model_class in self.all_model_classes:
       if model_class in get_values(UpperCAmelCase ):
        continue
       __lowerCAmelCase:       str            =				model_class(UpperCAmelCase )
       model.to(UpperCAmelCase )
       model.train()
       __lowerCAmelCase:       Any            =				self._prepare_for_class(UpperCAmelCase      ,  UpperCAmelCase      ,  return_labels=UpperCAmelCase )
       __lowerCAmelCase:       str            =				model(**UpperCAmelCase ).loss
       loss.backward()


     def   UpperCAmelCase  (							self      :		Dict )							->     Tuple:
      __lowerCAmelCase     ,							__lowerCAmelCase:       Union[str, Any]            =				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __lowerCAmelCase:       Union[str, Any]            =				model_class(UpperCAmelCase )
       __lowerCAmelCase:       Dict            =				inspect.signature(model.forward )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowerCAmelCase:       List[str]            =				[*signature.parameters.keys()]

       __lowerCAmelCase:       List[Any]            =				['pixel_values']
       self.assertListEqual(arg_names[:1]      ,  UpperCAmelCase )


     @slow
     def   UpperCAmelCase  (							self      :		int )							->     str:
      for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowerCAmelCase:       List[str]            =				PoolFormerModel.from_pretrained(UpperCAmelCase )
       self.assertIsNotNone(UpperCAmelCase )







def _a			(	)   ->   Any:

 """simple docstring"""






 __lowerCAmelCase:       Optional[Any]            =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
 return image






@require_torch
class 							A_			(		unittest.TestCase ):


     @slow
     def   UpperCAmelCase  (							self      :		Optional[Any] )							->     int:
      __lowerCAmelCase:       Optional[int]            =				PoolFormerImageProcessor()
      __lowerCAmelCase:       Any            =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12' ).to(UpperCAmelCase )

      __lowerCAmelCase:       int            =				image_processor(images=prepare_img()      ,  return_tensors='pt' ).to(UpperCAmelCase )

      # forward pass
      with torch.no_grad():
       __lowerCAmelCase:       List[Any]            =				model(**UpperCAmelCase )

      # verify the logits
      __lowerCAmelCase:       List[Any]            =				torch.Size((1, 1_0_0_0) )
      self.assertEqual(outputs.logits.shape      ,  UpperCAmelCase )

      __lowerCAmelCase:       Union[str, Any]            =				torch.tensor([-0.6113, 0.1685, -0.0492] ).to(UpperCAmelCase )
      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,  UpperCAmelCase      ,  atol=1E-4 ) )





