


"""simple docstring"""





import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

snake_case__		:     Optional[Any]    				=				'''bert-base-cased'''
snake_case__		:     Union[str, Any]    				=				'''fp16'''
snake_case__		:     Optional[int]    				=				'''bf16'''
snake_case__		:     List[str]    				=				[FPaa, BFaa]



@require_fsdp
@require_cuda
class 		snake_case_(       a__			):






     def      lowerCamelCase__     (	self  :					Tuple				):
           super().setUp()

           lowerCAmelCase					:	Dict       =   dict(
               ACCELERATE_USE_FSDP='''true'''					,				MASTER_ADDR='''localhost'''					,				MASTER_PORT='''10999'''					,				RANK='''0'''					,				LOCAL_RANK='''0'''					,				WORLD_SIZE='''1'''					,				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

           for i, strategy in enumerate(UpperCamelCase_				):
                 lowerCAmelCase					:	List[Any]       =   self.dist_env.copy()
                 lowerCAmelCase					:	int       =   F'''{i + 1}'''
                 lowerCAmelCase					:	Tuple       =   strategy
                 with mockenv_context(**UpperCamelCase_				):
                       lowerCAmelCase					:	Dict       =   FullyShardedDataParallelPlugin()
                       self.assertEqual(fsdp_plugin.sharding_strategy					,				ShardingStrategy(i + 1				)				)






     def      lowerCamelCase__     (	self  :					int				):
           from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

           for i, prefetch_policy in enumerate(UpperCamelCase_				):
                 lowerCAmelCase					:	Dict       =   self.dist_env.copy()
                 lowerCAmelCase					:	Optional[Any]       =   prefetch_policy
                 with mockenv_context(**UpperCamelCase_				):
                       lowerCAmelCase					:	Optional[Any]       =   FullyShardedDataParallelPlugin()
                       if prefetch_policy == "NO_PREFETCH":
                             self.assertIsNone(fsdp_plugin.backward_prefetch				)
                       else:
                             self.assertEqual(fsdp_plugin.backward_prefetch					,				BackwardPrefetch(i + 1				)				)






     def      lowerCamelCase__     (	self  :					Optional[Any]				):
           from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

           for i, state_dict_type in enumerate(UpperCamelCase_				):
                 lowerCAmelCase					:	str       =   self.dist_env.copy()
                 lowerCAmelCase					:	Optional[Any]       =   state_dict_type
                 with mockenv_context(**UpperCamelCase_				):
                       lowerCAmelCase					:	int       =   FullyShardedDataParallelPlugin()
                       self.assertEqual(fsdp_plugin.state_dict_type					,				StateDictType(i + 1				)				)
                       if state_dict_type == "FULL_STATE_DICT":
                             self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu				)
                             self.assertTrue(fsdp_plugin.state_dict_config.ranka_only				)






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	Tuple       =   AutoModel.from_pretrained(UpperCamelCase_				)
           for policy in FSDP_AUTO_WRAP_POLICY:
                 lowerCAmelCase					:	Union[str, Any]       =   self.dist_env.copy()
                 lowerCAmelCase					:	Union[str, Any]       =   policy
                 if policy == "TRANSFORMER_BASED_WRAP":
                       lowerCAmelCase					:	Tuple       =   '''BertLayer'''
                 elif policy == "SIZE_BASED_WRAP":
                       lowerCAmelCase					:	int       =   '''2000'''
                 with mockenv_context(**UpperCamelCase_				):
                       lowerCAmelCase					:	Any       =   FullyShardedDataParallelPlugin()
                       fsdp_plugin.set_auto_wrap_policy(UpperCamelCase_				)
                       if policy == "NO_WRAP":
                             self.assertIsNone(fsdp_plugin.auto_wrap_policy				)
                       else:
                             self.assertIsNotNone(fsdp_plugin.auto_wrap_policy				)

           lowerCAmelCase					:	List[str]       =   self.dist_env.copy()
           lowerCAmelCase					:	int       =   '''TRANSFORMER_BASED_WRAP'''
           lowerCAmelCase					:	Optional[int]       =   '''T5Layer'''
           with mockenv_context(**UpperCamelCase_				):
                 lowerCAmelCase					:	Union[str, Any]       =   FullyShardedDataParallelPlugin()
                 with self.assertRaises(UpperCamelCase_				) as cm:
                       fsdp_plugin.set_auto_wrap_policy(UpperCamelCase_				)
                 self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception				)				)

           lowerCAmelCase					:	Optional[int]       =   self.dist_env.copy()
           lowerCAmelCase					:	Tuple       =   '''SIZE_BASED_WRAP'''
           lowerCAmelCase					:	int       =   '''0'''
           with mockenv_context(**UpperCamelCase_				):
                 lowerCAmelCase					:	Union[str, Any]       =   FullyShardedDataParallelPlugin()
                 fsdp_plugin.set_auto_wrap_policy(UpperCamelCase_				)
                 self.assertIsNone(fsdp_plugin.auto_wrap_policy				)






     def      lowerCamelCase__     (	self  :					str				):
           from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
           from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

           for mp_dtype in dtypes:
                 lowerCAmelCase					:	List[str]       =   self.dist_env.copy()
                 lowerCAmelCase					:	Union[str, Any]       =   mp_dtype
                 with mockenv_context(**UpperCamelCase_				):
                       lowerCAmelCase					:	Any       =   Accelerator()
                       if mp_dtype == "fp16":
                             lowerCAmelCase					:	List[str]       =   torch.floataa
                       elif mp_dtype == "bf16":
                             lowerCAmelCase					:	List[Any]       =   torch.bfloataa
                       lowerCAmelCase					:	int       =   MixedPrecision(param_dtype=UpperCamelCase_					,				reduce_dtype=UpperCamelCase_					,				buffer_dtype=UpperCamelCase_				)
                       self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy					,				UpperCamelCase_				)
                       if mp_dtype == FPaa:
                             self.assertTrue(isinstance(accelerator.scaler					,				UpperCamelCase_				)				)
                       elif mp_dtype == BFaa:
                             self.assertIsNone(accelerator.scaler				)
                       AcceleratorState._reset_state(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

           for flag in [True, False]:
                 lowerCAmelCase					:	Tuple       =   self.dist_env.copy()
                 lowerCAmelCase					:	Dict       =   str(UpperCamelCase_				).lower()
                 with mockenv_context(**UpperCamelCase_				):
                       lowerCAmelCase					:	List[Any]       =   FullyShardedDataParallelPlugin()
                       self.assertEqual(fsdp_plugin.cpu_offload					,				CPUOffload(offload_params=UpperCamelCase_				)				)



@require_fsdp
@require_multi_gpu
@slow
class 		snake_case_(       a__			):






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           super().setUp()
           lowerCAmelCase					:	str       =   0.82
           lowerCAmelCase					:	Union[str, Any]       =   [
               '''fsdp_shard_grad_op_transformer_based_wrap''',
               '''fsdp_full_shard_transformer_based_wrap''',
           ]
           lowerCAmelCase					:	int       =   {
               '''multi_gpu_fp16''': 3_2_0_0,
               '''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_0_0_0,
               '''fsdp_full_shard_transformer_based_wrap_fp16''': 1_9_0_0,
               # Disabling below test as it overwhelms the RAM memory usage
               # on CI self-hosted runner leading to tests getting killed.
               # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
           }
           lowerCAmelCase					:	List[str]       =   1_6_0
           lowerCAmelCase					:	Dict       =   1_6_0

           lowerCAmelCase					:	Optional[int]       =   inspect.getfile(accelerate.test_utils				)
           lowerCAmelCase					:	List[Any]       =   os.path.sep.join(mod_file.split(os.path.sep				)[:-1] + ['''scripts''', '''external_deps''']				)






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Optional[Any]       =   os.path.join(self.test_scripts_folder					,				'''test_performance.py'''				)
           lowerCAmelCase					:	Dict       =   ['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
           for config in self.performance_configs:
                 lowerCAmelCase					:	Optional[Any]       =   cmd.copy()
                 for i, strategy in enumerate(UpperCamelCase_				):
                       if strategy.lower() in config:
                             cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''				)
                             break

                 if "fp32" in config:
                       cmd_config.append('''--mixed_precision=no'''				)
                 else:
                       cmd_config.append('''--mixed_precision=fp16'''				)

                 if "cpu_offload" in config:
                       cmd_config.append('''--fsdp_offload_params=True'''				)

                 for policy in FSDP_AUTO_WRAP_POLICY:
                       if policy.lower() in config:
                             cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''				)
                             break

                 if policy == "TRANSFORMER_BASED_WRAP":
                       cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer'''				)
                 elif policy == "SIZE_BASED_WRAP":
                       cmd_config.append('''--fsdp_min_num_params=2000'''				)

                 cmd_config.extend(
                     [
                         self.test_file_path,
                         F'''--output_dir={self.tmpdir}''',
                         F'''--performance_lower_bound={self.performance_lower_bound}''',
                     ]				)
                 with patch_environment(omp_num_threads=1				):
                       execute_subprocess_async(UpperCamelCase_					,				env=os.environ.copy()				)






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	Union[str, Any]       =   os.path.join(self.test_scripts_folder					,				'''test_checkpointing.py'''				)
           lowerCAmelCase					:	int       =   [
               '''accelerate''',
               '''launch''',
               '''--num_processes=2''',
               '''--num_machines=1''',
               '''--machine_rank=0''',
               '''--use_fsdp''',
               '''--mixed_precision=fp16''',
               '''--fsdp_transformer_layer_cls_to_wrap=BertLayer''',
           ]

           for i, strategy in enumerate(UpperCamelCase_				):
                 lowerCAmelCase					:	Union[str, Any]       =   cmd.copy()
                 cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''				)
                 if strategy != "FULL_SHARD":
                       continue
                 lowerCAmelCase					:	Optional[int]       =   len(UpperCamelCase_				)
                 for state_dict_type in FSDP_STATE_DICT_TYPE:
                       lowerCAmelCase					:	List[str]       =   cmd_config[:state_dict_config_index]
                       cmd_config.append(F'''--fsdp_state_dict_type={state_dict_type}'''				)
                       cmd_config.extend(
                           [
                               self.test_file_path,
                               F'''--output_dir={self.tmpdir}''',
                               '''--partial_train_epoch=1''',
                           ]				)
                       with patch_environment(omp_num_threads=1				):
                             execute_subprocess_async(UpperCamelCase_					,				env=os.environ.copy()				)

                       lowerCAmelCase					:	Tuple       =   cmd_config[:-1]
                       lowerCAmelCase					:	Dict       =   os.path.join(self.tmpdir					,				'''epoch_0'''				)
                       cmd_config.extend(
                           [
                               F'''--resume_from_checkpoint={resume_from_checkpoint}''',
                           ]				)
                       with patch_environment(omp_num_threads=1				):
                             execute_subprocess_async(UpperCamelCase_					,				env=os.environ.copy()				)






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase					:	Optional[Any]       =   os.path.join(self.test_scripts_folder					,				'''test_peak_memory_usage.py'''				)
           lowerCAmelCase					:	Dict       =   [
               '''accelerate''',
               '''launch''',
               '''--num_processes=2''',
               '''--num_machines=1''',
               '''--machine_rank=0''',
           ]
           for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
                 lowerCAmelCase					:	Dict       =   cmd.copy()
                 if "fp16" in spec:
                       cmd_config.extend(['''--mixed_precision=fp16''']				)
                 else:
                       cmd_config.extend(['''--mixed_precision=no''']				)

                 if "multi_gpu" in spec:
                       continue
                 else:
                       cmd_config.extend(['''--use_fsdp''']				)
                       for i, strategy in enumerate(UpperCamelCase_				):
                             if strategy.lower() in spec:
                                   cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''				)
                                   break

                       if "cpu_offload" in spec:
                             cmd_config.append('''--fsdp_offload_params=True'''				)

                       for policy in FSDP_AUTO_WRAP_POLICY:
                             if policy.lower() in spec:
                                   cmd_config.append(F'''--fsdp_auto_wrap_policy={policy}'''				)
                                   break

                       if policy == "TRANSFORMER_BASED_WRAP":
                             cmd_config.append('''--fsdp_transformer_layer_cls_to_wrap=BertLayer'''				)
                       elif policy == "SIZE_BASED_WRAP":
                             cmd_config.append('''--fsdp_min_num_params=2000'''				)

                 cmd_config.extend(
                     [
                         self.test_file_path,
                         F'''--output_dir={self.tmpdir}''',
                         F'''--peak_memory_upper_bound={peak_mem_upper_bound}''',
                         F'''--n_train={self.n_train}''',
                         F'''--n_val={self.n_val}''',
                     ]				)
                 with patch_environment(omp_num_threads=1				):
                       execute_subprocess_async(UpperCamelCase_					,				env=os.environ.copy()				)






"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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
 snake_case__		:     int    				=				'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import BlenderbotTokenizer
 from transformers.models.blenderbot.modeling_flax_blenderbot import (
     FlaxBlenderbotForConditionalGeneration,
     FlaxBlenderbotModel,
     shift_tokens_right,
 )


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



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					Tuple					,				UpperCamelCase_  :					int=1_3					,				UpperCamelCase_  :					Union[str, Any]=7					,				UpperCamelCase_  :					Union[str, Any]=True					,				UpperCamelCase_  :					List[Any]=False					,				UpperCamelCase_  :					Dict=9_9					,				UpperCamelCase_  :					Optional[int]=1_6					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					List[str]=4					,				UpperCamelCase_  :					List[Any]=4					,				UpperCamelCase_  :					int="gelu"					,				UpperCamelCase_  :					Optional[int]=0.1					,				UpperCamelCase_  :					Any=0.1					,				UpperCamelCase_  :					str=3_2					,				UpperCamelCase_  :					str=2					,				UpperCamelCase_  :					Tuple=1					,				UpperCamelCase_  :					List[Any]=0					,				UpperCamelCase_  :					Any=0.02					,				):
           lowerCAmelCase					:	Tuple       =   parent
           lowerCAmelCase					:	str       =   batch_size
           lowerCAmelCase					:	List[Any]       =   seq_length
           lowerCAmelCase					:	Optional[int]       =   is_training
           lowerCAmelCase					:	int       =   use_labels
           lowerCAmelCase					:	List[Any]       =   vocab_size
           lowerCAmelCase					:	str       =   hidden_size
           lowerCAmelCase					:	List[Any]       =   num_hidden_layers
           lowerCAmelCase					:	Any       =   num_attention_heads
           lowerCAmelCase					:	List[Any]       =   intermediate_size
           lowerCAmelCase					:	Optional[int]       =   hidden_act
           lowerCAmelCase					:	Dict       =   hidden_dropout_prob
           lowerCAmelCase					:	Optional[int]       =   attention_probs_dropout_prob
           lowerCAmelCase					:	List[Any]       =   max_position_embeddings
           lowerCAmelCase					:	Union[str, Any]       =   eos_token_id
           lowerCAmelCase					:	Dict       =   pad_token_id
           lowerCAmelCase					:	Optional[Any]       =   bos_token_id
           lowerCAmelCase					:	List[str]       =   initializer_range






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	List[Any]       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,				self.vocab_size				)					,				3					,				self.vocab_size				)
           lowerCAmelCase					:	str       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,				dtype=np.intaa				))					,				-1				)

           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)

           lowerCAmelCase					:	Union[str, Any]       =   BlenderbotConfig(
               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=UpperCamelCase_					,				)
           lowerCAmelCase					:	Union[str, Any]       =   prepare_blenderbot_inputs_dict(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	Optional[int]       =   self.prepare_config_and_inputs()
           return config, inputs_dict






     def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					List[str]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	int       =   2_0
           lowerCAmelCase					:	Tuple       =   model_class_name(UpperCamelCase_				)

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

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

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

           lowerCAmelCase					:	Tuple       =   jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)
           lowerCAmelCase					:	List[Any]       =   model.decode(
               decoder_input_ids[:, :-1]					,				UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				past_key_values=UpperCamelCase_					,				decoder_position_ids=UpperCamelCase_					,				)

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

           lowerCAmelCase					:	Union[str, Any]       =   model.decode(UpperCamelCase_					,				UpperCamelCase_				)

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






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Optional[int]       =   2_0
           lowerCAmelCase					:	List[Any]       =   model_class_name(UpperCamelCase_				)

           lowerCAmelCase					:	Union[str, Any]       =   model.encode(inputs_dict['''input_ids''']				)

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

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

           lowerCAmelCase					:	Union[str, Any]       =   model.init_cache(decoder_input_ids.shape[0]					,				UpperCamelCase_					,				UpperCamelCase_				)
           lowerCAmelCase					:	str       =   jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]					,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,				)

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

           lowerCAmelCase					:	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}'''				)



@require_flax
class 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					99






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

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






     def      lowerCamelCase__     (	self  :					List[str]				):
           lowerCAmelCase,      lowerCAmelCase,      lowerCAmelCase					:	Any       =   self._get_config_and_data()
           lowerCAmelCase					:	Any       =   FlaxBlenderbotForConditionalGeneration(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   lm_model(input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   (batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any				):
           lowerCAmelCase					:	Any       =   BlenderbotConfig(
               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					:	int       =   FlaxBlenderbotForConditionalGeneration(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					:	List[str]       =   np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	List[Any]       =   lm_model(input_ids=UpperCamelCase_					,				decoder_input_ids=UpperCamelCase_				)
           lowerCAmelCase					:	str       =   (*summary.shape, config.vocab_size)
           self.assertEqual(outputs['''logits'''].shape					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					int				):
           lowerCAmelCase					:	Any       =   np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]					,				dtype=np.intaa				)
           lowerCAmelCase					:	Tuple       =   shift_tokens_right(UpperCamelCase_					,				1					,				2				)
           lowerCAmelCase					:	Optional[int]       =   np.equal(UpperCamelCase_					,				1				).astype(np.floataa				).sum()
           lowerCAmelCase					:	str       =   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 		snake_case_(       a__							,					unittest.TestCase							,					a__			):
     __UpperCamelCase  =					True
     __UpperCamelCase  =					(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     __UpperCamelCase  =					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






     def      lowerCamelCase__     (	self  :					Dict				):
           lowerCAmelCase					:	Any       =   FlaxBlenderbotModelTester(self				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[str]				):
           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_with_attn_mask(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase,      lowerCAmelCase					:	str       =   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[int]       =   self._prepare_for_class(UpperCamelCase_					,				UpperCamelCase_				)
                       lowerCAmelCase					:	Optional[Any]       =   model_class(UpperCamelCase_				)

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

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	List[str]       =   encode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	int       =   encode_jitted(**UpperCamelCase_				).to_tuple()

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






     def      lowerCamelCase__     (	self  :					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					:	Tuple       =   model_class(UpperCamelCase_				)
                       lowerCAmelCase					:	int       =   model.encode(inputs_dict['''input_ids''']					,				inputs_dict['''attention_mask''']				)

                       lowerCAmelCase					:	List[Any]       =   {
                           '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                           '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                           '''encoder_outputs''': encoder_outputs,
                       }

                       @jax.jit
                       def decode_jitted(UpperCamelCase_  :					Union[str, Any]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					int				):
                             return model.decode(
                                 decoder_input_ids=UpperCamelCase_					,				decoder_attention_mask=UpperCamelCase_					,				encoder_outputs=UpperCamelCase_					,				)

                       with self.subTest('''JIT Enabled'''				):
                             lowerCAmelCase					:	str       =   decode_jitted(**UpperCamelCase_				).to_tuple()

                       with self.subTest('''JIT Disabled'''				):
                             with jax.disable_jit():
                                   lowerCAmelCase					:	Union[str, Any]       =   decode_jitted(**UpperCamelCase_				).to_tuple()

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






     @slow
     def      lowerCamelCase__     (	self  :					Optional[int]				):
           for model_class_name in self.all_model_classes:
                 lowerCAmelCase					:	Optional[int]       =   model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''				)
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 lowerCAmelCase					:	int       =   np.ones((1, 1)				) * model.config.eos_token_id
                 lowerCAmelCase					:	List[str]       =   model(UpperCamelCase_				)
                 self.assertIsNotNone(UpperCamelCase_				)






     @unittest.skipUnless(jax_device != '''cpu'''					,				'''3B test too slow on CPU.'''				)
     @slow
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           lowerCAmelCase					:	Dict       =   {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
           lowerCAmelCase					:	List[str]       =   {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

           lowerCAmelCase					:	Tuple       =   FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''					,				from_pt=UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''				)

           lowerCAmelCase					:	List[Any]       =   ['''Sam''']
           lowerCAmelCase					:	str       =   tokenizer(UpperCamelCase_					,				return_tensors='''jax'''				)

           lowerCAmelCase					:	Union[str, Any]       =   model.generate(**UpperCamelCase_					,				**UpperCamelCase_				)
           lowerCAmelCase					:	Tuple       =   '''Sam is a great name. It means "sun" in Gaelic.'''

           lowerCAmelCase					:	Union[str, Any]       =   tokenizer.batch_decode(UpperCamelCase_					,				**UpperCamelCase_				)
           assert generated_txt[0].strip() == tgt_text


