'''simple docstring'''




import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__snake_case   :      int						    =  logging.get_logger(__name__)





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	bool = True   )	->      Optional[Any]:
    A_     =   {}
    for name in model.attn_processors.keys():
        A_     =   name.endswith('''attn2.processor'''   ) and not (
            name.startswith('''transformer_in'''   ) or '''temp_attentions''' in name.split('''.'''   )
        )
        A_     =   model.config.cross_attention_dim if has_cross_attention else None
        if name.startswith('''mid_block'''   ):
            A_     =   model.config.block_out_channels[-1]
        elif name.startswith('''up_blocks'''   ):
            A_     =   int(name[len('''up_blocks.'''   )]   )
            A_     =   list(reversed(model.config.block_out_channels   )   )[block_id]
        elif name.startswith('''down_blocks'''   ):
            A_     =   int(name[len('''down_blocks.'''   )]   )
            A_     =   model.config.block_out_channels[block_id]
        elif name.startswith('''transformer_in'''   ):
            # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
            A_     =   8 * model.config.attention_head_dim

        A_     =   LoRAAttnProcessor(hidden_size=_UpperCamelCase,   cross_attention_dim=_UpperCamelCase   )
        A_     =   lora_attn_procs[name].to(model.device   )

        if mock_weights:
            # add 1 to weights to mock trained weights
            with torch.no_grad():
                lora_attn_procs[name].to_q_lora.up.weight += 1
                lora_attn_procs[name].to_k_lora.up.weight += 1
                lora_attn_procs[name].to_v_lora.up.weight += 1
                lora_attn_procs[name].to_out_lora.up.weight += 1

    return lora_attn_procs




@skip_mps
class 	__UpperCAmelCase				(				_UpperCamelCase						,      _UpperCamelCase						,      unittest.TestCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[int]    =				UNetaDConditionModel
       __lowercase					:					Optional[int]    =				'sample'

       @property
       def 					__A       (      self )		->     List[str]:
           A_     =   4
           A_     =   4
           A_     =   4
           A_     =   (32, 32)

           A_     =   floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(_SCREAMING_SNAKE_CASE )
           A_     =   torch.tensor([10] ).to(_SCREAMING_SNAKE_CASE )
           A_     =   floats_tensor((batch_size, 4, 32) ).to(_SCREAMING_SNAKE_CASE )

           return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}

       @property
       def 					__A       (      self )		->     Union[str, Any]:
           return (4, 4, 32, 32)

       @property
       def 					__A       (      self )		->     Union[str, Any]:
           return (4, 4, 32, 32)

       def 					__A       (      self )		->     Any:
           A_     =   {
               '''block_out_channels''': (32, 64),
               '''down_block_types''': (
                   '''CrossAttnDownBlock3D''',
                   '''DownBlock3D''',
               ),
               '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
               '''cross_attention_dim''': 32,
               '''attention_head_dim''': 8,
               '''out_channels''': 4,
               '''in_channels''': 4,
               '''layers_per_block''': 1,
               '''sample_size''': 32,
           }
           A_     =   self.dummy_input
           return init_dict, inputs_dict

       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()    ,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,     )
       def 					__A       (      self )		->     int:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )

           model.enable_xformers_memory_efficient_attention()

           assert (
               model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
               == "XFormersAttnProcessor"
           ), "xformers is not enabled"

       def 					__A       (      self )		->     Dict:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   32

           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE )

               if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
                   A_     =   output.sample

           self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
           A_     =   inputs_dict['''sample'''].shape
           self.assertEqual(output.shape    ,     _SCREAMING_SNAKE_CASE    ,     '''Input and output shapes do not match''' )

       def 					__A       (      self )		->     int:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()

           with torch.no_grad():
               # Warmup pass when using mps (see #372)
               if torch_device == "mps" and isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
                   model(**self.dummy_input )

               A_     =   model(**_SCREAMING_SNAKE_CASE )
               if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
                   A_     =   first.sample

               A_     =   model(**_SCREAMING_SNAKE_CASE )
               if isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
                   A_     =   second.sample

           A_     =   first.cpu().numpy()
           A_     =   second.cpu().numpy()
           A_     =   out_a[~np.isnan(_SCREAMING_SNAKE_CASE )]
           A_     =   out_a[~np.isnan(_SCREAMING_SNAKE_CASE )]
           A_     =   np.amax(np.abs(out_a - out_a ) )
           self.assertLessEqual(_SCREAMING_SNAKE_CASE    ,     1E-5 )

       def 					__A       (      self )		->     Union[str, Any]:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   8

           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()

           model.set_attention_slice('''auto''' )
           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE )
           assert output is not None

           model.set_attention_slice('''max''' )
           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE )
           assert output is not None

           model.set_attention_slice(2 )
           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE )
           assert output is not None

       def 					__A       (      self )		->     Optional[Any]:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   8

           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

           A_     =   create_lora_layers(_SCREAMING_SNAKE_CASE )

           # make sure we can set a list of attention processors
           model.set_attn_processor(_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )

           # test that attn processors can be set to itself
           model.set_attn_processor(model.attn_processors )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.0} ).sample
               A_     =   model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.5} ).sample
               A_     =   model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.5} ).sample

           assert (samplea - samplea).abs().max() < 3E-3
           assert (samplea - samplea).abs().max() < 3E-3

           # sample 2 and sample 3 should be different
           assert (samplea - samplea).abs().max() > 3E-3

       def 					__A       (      self )		->     int:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   8

           torch.manual_seed(0 )
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

           A_     =   create_lora_layers(_SCREAMING_SNAKE_CASE )
           model.set_attn_processor(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.5} ).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(_SCREAMING_SNAKE_CASE )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,     '''pytorch_lora_weights.bin''' ) ) )
               torch.manual_seed(0 )
               A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
               new_model.to(_SCREAMING_SNAKE_CASE )
               new_model.load_attn_procs(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   new_model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.5} ).sample

           assert (sample - new_sample).abs().max() < 1E-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1E-4

       def 					__A       (      self )		->     List[str]:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   8

           torch.manual_seed(0 )
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

           A_     =   create_lora_layers(_SCREAMING_SNAKE_CASE )
           model.set_attn_processor(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.5} ).sample

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(_SCREAMING_SNAKE_CASE    ,     safe_serialization=_SCREAMING_SNAKE_CASE )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,     '''pytorch_lora_weights.safetensors''' ) ) )
               torch.manual_seed(0 )
               A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
               new_model.to(_SCREAMING_SNAKE_CASE )
               new_model.load_attn_procs(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   new_model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.5} ).sample

           assert (sample - new_sample).abs().max() < 3E-3

           # LoRA and no LoRA should NOT be the same
           assert (sample - old_sample).abs().max() > 1E-4

       def 					__A       (      self )		->     Optional[Any]:
           # enable deterministic behavior for gradient checkpointing
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   8

           torch.manual_seed(0 )
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )

           A_     =   create_lora_layers(_SCREAMING_SNAKE_CASE    ,     mock_weights=_SCREAMING_SNAKE_CASE )
           model.set_attn_processor(_SCREAMING_SNAKE_CASE )
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(_SCREAMING_SNAKE_CASE )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,     '''pytorch_lora_weights.bin''' ) ) )
               torch.manual_seed(0 )
               A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
               new_model.to(_SCREAMING_SNAKE_CASE )
               new_model.load_attn_procs(_SCREAMING_SNAKE_CASE    ,     weight_name='''pytorch_lora_weights.bin''' )

       def 					__A       (      self )		->     Tuple:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   8

           torch.manual_seed(0 )
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )

           A_     =   create_lora_layers(_SCREAMING_SNAKE_CASE    ,     mock_weights=_SCREAMING_SNAKE_CASE )
           model.set_attn_processor(_SCREAMING_SNAKE_CASE )
           # Saving as torch, properly reloads with directly filename
           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_attn_procs(_SCREAMING_SNAKE_CASE )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE    ,     '''pytorch_lora_weights.bin''' ) ) )
               torch.manual_seed(0 )
               A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
               new_model.to(_SCREAMING_SNAKE_CASE )
               with self.assertRaises(_SCREAMING_SNAKE_CASE ) as e:
                   new_model.load_attn_procs(_SCREAMING_SNAKE_CASE    ,     use_safetensors=_SCREAMING_SNAKE_CASE )
               self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''    ,     str(e.exception ) )

       def 					__A       (      self )		->     Tuple:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   8

           torch.manual_seed(0 )
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

           A_     =   create_lora_layers(_SCREAMING_SNAKE_CASE )
           model.set_attn_processor(_SCREAMING_SNAKE_CASE )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE    ,     cross_attention_kwargs={'''scale''': 0.0} ).sample

           model.set_attn_processor(AttnProcessor() )

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

           assert (sample - new_sample).abs().max() < 1E-4
           assert (sample - old_sample).abs().max() < 3E-3

       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()    ,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''    ,     )
       def 					__A       (      self )		->     Union[str, Any]:
           # enable deterministic behavior for gradient checkpointing
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()

           A_     =   4

           torch.manual_seed(0 )
           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           A_     =   create_lora_layers(_SCREAMING_SNAKE_CASE )
           model.set_attn_processor(_SCREAMING_SNAKE_CASE )

           # default
           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

               model.enable_xformers_memory_efficient_attention()
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

               model.disable_xformers_memory_efficient_attention()
               A_     =   model(**_SCREAMING_SNAKE_CASE ).sample

           assert (sample - on_sample).abs().max() < 1E-4
           assert (sample - off_sample).abs().max() < 1E-4

       def 					__A       (      self )		->     List[Any]:
           A_   ,A_     =   self.prepare_init_args_and_inputs_for_common()
           A_     =   32

           A_     =   self.model_class(**_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()

           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE )[0]

           model.enable_forward_chunking()
           with torch.no_grad():
               A_     =   model(**_SCREAMING_SNAKE_CASE )[0]

           self.assertEqual(output.shape    ,     output_a.shape    ,     '''Shape doesn\'t match''' )
           assert np.abs(output.cpu() - output_a.cpu() ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.










'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, 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      :     Union[str, Any]										= '''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import MarianTokenizer
   from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			List[Any]  ,lowerCamelCase     :			List[Any]=None  ,lowerCamelCase     :			Union[str, Any]=None  ,lowerCamelCase     :			int=None  ,lowerCamelCase     :			List[str]=None  ,lowerCamelCase     :			Optional[int]=None  ,lowerCamelCase     :			Dict=None  ,):
       if attention_mask is None:
              _A     :	str      =					np.where(input_ids != config.pad_token_id  ,1  ,0   )
       if decoder_attention_mask is None:
              _A     :	List[str]      =					np.where(decoder_input_ids != config.pad_token_id  ,1  ,0   )
       if head_mask is None:
              _A     :	List[str]      =					np.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _A     :	Any      =					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": attention_mask,
       }


class  __lowerCamelCase :
    """simple docstring"""


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


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

           _A     :	str      =					shift_tokens_right(SCREAMING_SNAKE_CASE      ,							1      ,							2)

           _A     :	List[Any]      =					MarianConfig(
               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      ,							initializer_range=self.initializer_range      ,							use_cache=SCREAMING_SNAKE_CASE      ,							)
           _A     :	List[Any]      =					prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           return config, inputs_dict


    def    A    (  self			:							List[str]):
           _A  , _A     :	Tuple      =					self.prepare_config_and_inputs()
           return config, inputs_dict


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	Optional[Any]      =					20
           _A     :	Any      =					model_class_name(SCREAMING_SNAKE_CASE)

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

           _A  , _A     :	Optional[int]      =					(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           _A     :	Any      =					model.init_cache(decoder_input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Tuple      =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,							dtype='i4')

           _A     :	Optional[int]      =					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]      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=SCREAMING_SNAKE_CASE      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)

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

           _A     :	List[Any]      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

           _A     :	Optional[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    A    (  self			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							Tuple):
           _A     :	List[Any]      =					20
           _A     :	str      =					model_class_name(SCREAMING_SNAKE_CASE)

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

           _A  , _A     :	Union[str, Any]      =					(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

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

           _A     :	Optional[int]      =					model.init_cache(decoder_input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Union[str, 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     :	Dict      =					model.decode(
               decoder_input_ids[:, :-1]      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=SCREAMING_SNAKE_CASE      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)
           _A     :	Optional[int]      =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='i4')
           _A     :	Dict      =					model.decode(
               decoder_input_ids[:, -1:]      ,							SCREAMING_SNAKE_CASE      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	Tuple      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE)

           _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}')




@require_flax
class  __lowerCamelCase (       a_				,					unittest.TestCase				,					a_       ):
    """simple docstring"""
    a													=    True
    a													=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
    a													=    (FlaxMarianMTModel,) if is_flax_available() else ()


    def    A    (  self			:							str):
           _A     :	Any      =					FlaxMarianModelTester(self)


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	int      =					self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							int):
           _A  , _A     :	Any      =					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(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Tuple):
           _A  , _A     :	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     :	int      =					self._prepare_for_class(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	int      =					model_class(SCREAMING_SNAKE_CASE)

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

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

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A     :	List[str]      =					encode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

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


    def    A    (  self			:							Tuple):
           _A  , _A     :	Optional[int]      =					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[Any]      =					model_class(SCREAMING_SNAKE_CASE)
                         _A     :	Tuple      =					model.encode(inputs_dict['input_ids']      ,							inputs_dict['attention_mask'])

                         _A     :	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(SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
                                return model.decode(
                                    decoder_input_ids=SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							encoder_outputs=SCREAMING_SNAKE_CASE      ,							)

                         with self.subTest('JIT Enabled'):
                                _A     :	Optional[int]      =					decode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A     :	List[str]      =					decode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

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


    @slow
    def    A    (  self			:							Optional[Any]):
           for model_class_name in self.all_model_classes:
                  _A     :	str      =					model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de')
                  # FlaxMarianForSequenceClassification expects eos token in input_ids
                  _A     :	Optional[int]      =					np.ones((1, 1)) * model.config.eos_token_id
                  _A     :	str      =					model(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       unittest.TestCase       ):
    """simple docstring"""
    a													=    None
    a													=    None


    @classmethod
    def    A    (  cls			:							List[Any]):
           _A     :	Optional[int]      =					F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
           return cls


    @cached_property
    def    A    (  self			:							Optional[Any]):
           return MarianTokenizer.from_pretrained(self.model_name)


    @property
    def    A    (  self			:							Dict):
           return self.tokenizer.eos_token_id


    @cached_property
    def    A    (  self			:							int):
           _A     :	FlaxMarianMTModel      =					FlaxMarianMTModel.from_pretrained(self.model_name)
           self.assertEqual(model.config.decoder_start_token_id      ,							model.config.pad_token_id)
           return model


    def    A    (  self			:							Union[str, Any]      ,							**SCREAMING_SNAKE_CASE			:							int):
           _A     :	str      =					self.translate_src_text(**SCREAMING_SNAKE_CASE)
           self.assertListEqual(self.expected_text      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							str      ,							**SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	int      =					self.tokenizer(self.src_text      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='np'      ,							**SCREAMING_SNAKE_CASE)
           _A     :	Union[str, Any]      =					self.model.generate(
               model_inputs.input_ids      ,							attention_mask=model_inputs.attention_mask      ,							num_beams=2      ,							max_length=128      ,							).sequences
           _A     :	int      =					self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,							skip_special_tokens=SCREAMING_SNAKE_CASE)
           return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "fr"
    a													=    [
        "I am a small frog.",
        "Now I can forget the 100 words of german that I know.",
    ]
    a													=    [
        "Je suis une petite grenouille.",
        "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "fr"
    a													=    "en"
    a													=    [
        "Donnez moi le micro.",
        "Tom et Mary étaient assis à une table.",  # Accents
    ]
    a													=    [
        "Give me the microphone.",
        "Tom and Mary were sitting at a table.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "mt"
    a													=    "en"
    a													=    ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
    a													=    ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


    @slow
    def    A    (  self			:							List[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "de"
    a													=    [
        "I am a small frog.",
        "Now I can forget the 100 words of german that I know.",
        "Tom asked his teacher for advice.",
        "That's how I would do it.",
        "Tom really admired Mary's courage.",
        "Turn around and close your eyes.",
    ]
    a													=    [
        "Ich bin ein kleiner Frosch.",
        "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
        "Tom bat seinen Lehrer um Rat.",
        "So würde ich das machen.",
        "Tom bewunderte Marias Mut wirklich.",
        "Drehen Sie sich um und schließen Sie die Augen.",
    ]


    @slow
    def    A    (  self			:							Optional[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "zh"
    a													=    ["My name is Wolfgang and I live in Berlin"]
    a													=    ["我叫沃尔夫冈 我住在柏林"]


    @slow
    def    A    (  self			:							Optional[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "ru"
    a													=    "fr"
    a													=    ["Он показал мне рукопись своей новой пьесы."]
    a													=    ["Il m'a montré le manuscrit de sa nouvelle pièce."]


    @slow
    def    A    (  self			:							Optional[int]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "ROMANCE"
    a													=    [
        ">>fr<< Don't spend so much time watching TV.",
        ">>pt<< Your message has been sent.",
        ">>es<< He's two years older than me.",
    ]
    a													=    [
        "Ne passez pas autant de temps à regarder la télé.",
        "A sua mensagem foi enviada.",
        "Es dos años más viejo que yo.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()






