





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
snake_case__					:							Any													=     logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class A_					:
   lowerCAmelCase__        = field(
       default="""cifar10""" ,      metadata={"""help""": """Name of a dataset from the datasets package"""}   )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}   )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={"""help""": """The column name of the images in the files."""}   )
   lowerCAmelCase__        = field(default=_lowerCamelCase ,      metadata={"""help""": """A folder containing the training data."""}   )
   lowerCAmelCase__        = field(default=_lowerCamelCase ,      metadata={"""help""": """A folder containing the validation data."""}   )
   lowerCAmelCase__        = field(
       default=0.15 ,      metadata={"""help""": """Percent to split off of train for validation."""}   )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of training examples to this """
               """value if set."""
           )
       } ,      )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
               """value if set."""
           )
       } ,      )




   def _lowerCAmelCase      (self	:List[str] )->					Dict:
         __A          =				{}
         if self.train_dir is not None:
               __A          =				self.train_dir
         if self.validation_dir is not None:
               __A          =				self.validation_dir
         __A          =				data_files if data_files else None





@dataclass
class A_					:
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={
           """help""": (
               """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
           )
       } ,      )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}   )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={
           """help""": (
               """Override some existing default config settings when a model is trained from scratch. Example: """
               """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
           )
       } ,      )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}   )
   lowerCAmelCase__        = field(
       default="""main""" ,      metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} ,      )
   lowerCAmelCase__        = field(default=_lowerCamelCase ,      metadata={"""help""": """Name or path of preprocessor config."""}   )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={
           """help""": (
               """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
               """with private models)."""
           )
       } ,      )
   lowerCAmelCase__        = field(
       default=0.75 ,      metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}   )
   lowerCAmelCase__        = field(
       default=_lowerCamelCase ,      metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}   )





@dataclass
class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = field(
       default=1e-3 ,      metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}   )





def _a				(	lowerCamelCase:   Union[str, Any] )	->						Optional[int]:







      '''simple docstring'''






      __A          =				torch.stack([example['''pixel_values'''] for example in examples] )
      return {"pixel_values": pixel_values}



def _a				(	)	->						Optional[int]:







      '''simple docstring'''






      __A          =				HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
      if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
            # If we pass only one argument to the script and it's the path to a json file,
            # let's parse it to get our arguments.
            __A   ,						__A   ,						__A          =				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
      else:
            __A   ,						__A   ,						__A          =				parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry('''run_mae'''					,				lowerCamelCase					,				lowerCamelCase )

      # Setup logging
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,				datefmt='''%m/%d/%Y %H:%M:%S'''					,				handlers=[logging.StreamHandler(sys.stdout )]					,				)

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      __A          =				training_args.get_process_log_level()
      logger.setLevel(lowerCamelCase )
      transformers.utils.logging.set_verbosity(lowerCamelCase )
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
          + F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
      logger.info(F"""Training/evaluation parameters {training_args}""" )

      # Detecting last checkpoint.
      __A          =				None
      if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
            __A          =				get_last_checkpoint(training_args.output_dir )
            if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
                  raise ValueError(
                      F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                      '''Use --overwrite_output_dir to overcome.''' )
            elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                  logger.info(
                      F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                      '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )

    # Initialize our dataset.
      __A          =				load_dataset(
          data_args.dataset_name					,				data_args.dataset_config_name					,				data_files=data_args.data_files					,				cache_dir=model_args.cache_dir					,				use_auth_token=True if model_args.use_auth_token else None					,				)

      # If we don't have a validation split, split off a percentage of train as validation.
      __A          =				None if '''validation''' in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split					,				lowerCamelCase ) and data_args.train_val_split > 0.0:
            __A          =				ds['''train'''].train_test_split(data_args.train_val_split )
            __A          =				split['''train''']
            __A          =				split['''test''']

      # Load pretrained model and image processor
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      __A          =				{
          '''cache_dir''': model_args.cache_dir,
          '''revision''': model_args.model_revision,
          '''use_auth_token''': True if model_args.use_auth_token else None,
      }
      if model_args.config_name:
            __A          =				ViTMAEConfig.from_pretrained(model_args.config_name					,				**lowerCamelCase )
      elif model_args.model_name_or_path:
            __A          =				ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,				**lowerCamelCase )
      else:
            __A          =				ViTMAEConfig()
            logger.warning('''You are instantiating a new config instance from scratch.''' )
            if model_args.config_overrides is not None:
                  logger.info(F"""Overriding config: {model_args.config_overrides}""" )
                  config.update_from_string(model_args.config_overrides )
                  logger.info(F"""New config: {config}""" )

    # adapt config
      config.update(
          {
              '''mask_ratio''': model_args.mask_ratio,
              '''norm_pix_loss''': model_args.norm_pix_loss,
          } )

      # create image processor
      if model_args.image_processor_name:
            __A          =				ViTImageProcessor.from_pretrained(model_args.image_processor_name					,				**lowerCamelCase )
      elif model_args.model_name_or_path:
            __A          =				ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,				**lowerCamelCase )
      else:
            __A          =				ViTImageProcessor()

      # create model
      if model_args.model_name_or_path:
            __A          =				ViTMAEForPreTraining.from_pretrained(
                model_args.model_name_or_path					,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path )					,				config=lowerCamelCase					,				cache_dir=model_args.cache_dir					,				revision=model_args.model_revision					,				use_auth_token=True if model_args.use_auth_token else None					,				)
      else:
            logger.info('''Training new model from scratch''' )
            __A          =				ViTMAEForPreTraining(lowerCamelCase )

      if training_args.do_train:
            __A          =				ds['''train'''].column_names
      else:
            __A          =				ds['''validation'''].column_names

      if data_args.image_column_name is not None:
            __A          =				data_args.image_column_name
      elif "image" in column_names:
            __A          =				'''image'''
      elif "img" in column_names:
            __A          =				'''img'''
      else:
            __A          =				column_names[0]

      # transformations as done in original MAE paper
      # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
      if "shortest_edge" in image_processor.size:
            __A          =				image_processor.size['''shortest_edge''']
      else:
            __A          =				(image_processor.size['''height'''], image_processor.size['''width'''])
      __A          =				Compose(
          [
              Lambda(lambda lowerCamelCase : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
              RandomResizedCrop(lowerCamelCase					,				scale=(0.2, 1.0)					,				interpolation=InterpolationMode.BICUBIC ),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean					,				std=image_processor.image_std ),
          ] )

      def preprocess_images(lowerCamelCase:   Any ):
            __A          =				[transforms(lowerCamelCase ) for image in examples[image_column_name]]
            return examples

      if training_args.do_train:
            if "train" not in ds:
                  raise ValueError('''--do_train requires a train dataset''' )
            if data_args.max_train_samples is not None:
                  __A          =				ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
            # Set the training transforms
            ds["train"].set_transform(lowerCamelCase )

      if training_args.do_eval:
            if "validation" not in ds:
                  raise ValueError('''--do_eval requires a validation dataset''' )
            if data_args.max_eval_samples is not None:
                  __A          =				(
                      ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
                  )
            # Set the validation transforms
            ds["validation"].set_transform(lowerCamelCase )

      # Compute absolute learning rate
      __A          =				(
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            __A          =				training_args.base_learning_rate * total_train_batch_size / 2_56

      # Initialize our trainer
      __A          =				Trainer(
          model=lowerCamelCase					,				args=lowerCamelCase					,				train_dataset=ds['''train'''] if training_args.do_train else None					,				eval_dataset=ds['''validation'''] if training_args.do_eval else None					,				tokenizer=lowerCamelCase					,				data_collator=lowerCamelCase					,				)

      # Training
      if training_args.do_train:
            __A          =				None
            if training_args.resume_from_checkpoint is not None:
                  __A          =				training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  __A          =				last_checkpoint
            __A          =				trainer.train(resume_from_checkpoint=lowerCamelCase )
            trainer.save_model()
            trainer.log_metrics('''train'''					,				train_result.metrics )
            trainer.save_metrics('''train'''					,				train_result.metrics )
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            __A          =				trainer.evaluate()
            trainer.log_metrics('''eval'''					,				lowerCamelCase )
            trainer.save_metrics('''eval'''					,				lowerCamelCase )

      # Write model card and (optionally) push to hub
      __A          =				{
          '''tasks''': '''masked-auto-encoding''',
          '''dataset''': data_args.dataset_name,
          '''tags''': ['''masked-auto-encoding'''],
      }
      if training_args.push_to_hub:
            trainer.push_to_hub(**lowerCamelCase )
      else:
            trainer.create_model_card(**lowerCamelCase )



def _a				(	lowerCamelCase:   Any )	->						int:







      '''simple docstring'''






      main()


if __name__ == "__main__":
       main()












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__					:							Tuple													=     '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 _a				(	lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   int=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Tuple=None					,				lowerCamelCase:   Union[str, Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				)	->						Union[str, Any]:







      '''simple docstring'''






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




   def __init__(self	:int    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Dict=13    ,   _UpperCamelCase	:Optional[Any]=7    ,   _UpperCamelCase	:str=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:int=99    ,   _UpperCamelCase	:int=16    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:int=4    ,   _UpperCamelCase	:str=4    ,   _UpperCamelCase	:Dict="gelu"    ,   _UpperCamelCase	:int=0.1    ,   _UpperCamelCase	:Tuple=0.1    ,   _UpperCamelCase	:Union[str, Any]=32    ,   _UpperCamelCase	:Any=2    ,   _UpperCamelCase	:Union[str, Any]=1    ,   _UpperCamelCase	:Tuple=0    ,   _UpperCamelCase	:List[str]=0.0_2    ,   )->					str:
         __A          =				parent
         __A          =				batch_size
         __A          =				seq_length
         __A          =				is_training
         __A          =				use_labels
         __A          =				vocab_size
         __A          =				hidden_size
         __A          =				num_hidden_layers
         __A          =				num_attention_heads
         __A          =				intermediate_size
         __A          =				hidden_act
         __A          =				hidden_dropout_prob
         __A          =				attention_probs_dropout_prob
         __A          =				max_position_embeddings
         __A          =				eos_token_id
         __A          =				pad_token_id
         __A          =				bos_token_id
         __A          =				initializer_range




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

         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )

         __A          =				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    ,   )
         __A          =				prepare_blenderbot_inputs_dict(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A   ,						__A          =				self.prepare_config_and_inputs()
         return config, inputs_dict




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:Optional[int] )->					str:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

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

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

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

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

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

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase )

         __A          =				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	:Optional[int]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:Any )->					Dict:
         __A          =				20
         __A          =				model_class_name(_UpperCamelCase )

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

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

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

         __A          =				model.init_cache(decoder_input_ids.shape[0]    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]    ,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,   )

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

         __A          =				model.decode(_UpperCamelCase    ,   _UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase )

         __A          =				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   ):
   lowerCAmelCase__        = 99




   def _lowerCAmelCase      (self	:Dict )->					int:
         __A          =				np.array(
             [
                 [71, 82, 18, 33, 46, 91, 2],
                 [68, 34, 26, 58, 30, 82, 2],
                 [5, 97, 17, 39, 94, 40, 2],
                 [76, 83, 94, 25, 70, 78, 2],
                 [87, 59, 41, 35, 48, 66, 2],
                 [55, 13, 16, 58, 5, 2, 1],  # note padding
                 [64, 27, 31, 51, 12, 75, 2],
                 [52, 64, 86, 17, 83, 39, 2],
                 [48, 61, 9, 24, 71, 82, 2],
                 [26, 1, 60, 48, 22, 13, 2],
                 [21, 5, 62, 28, 14, 76, 2],
                 [45, 98, 37, 86, 59, 48, 2],
                 [70, 70, 50, 9, 28, 0, 2],
             ]    ,   dtype=np.intaa    ,   )

         __A          =				input_ids.shape[0]
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=24    ,   encoder_layers=2    ,   decoder_layers=2    ,   encoder_attention_heads=2    ,   decoder_attention_heads=2    ,   encoder_ffn_dim=32    ,   decoder_ffn_dim=32    ,   max_position_embeddings=48    ,   eos_token_id=2    ,   pad_token_id=1    ,   bos_token_id=0    ,   )
         return config, input_ids, batch_size




   def _lowerCAmelCase      (self	:Any )->					List[Any]:
         __A   ,						__A   ,						__A          =				self._get_config_and_data()
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				lm_model(input_ids=_UpperCamelCase )
         __A          =				(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:int )->					Dict:
         __A          =				BlenderbotConfig(
             vocab_size=self.vocab_size    ,   d_model=14    ,   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=48    ,   )
         __A          =				FlaxBlenderbotForConditionalGeneration(_UpperCamelCase )
         __A          =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,   dtype=np.intaa )
         __A          =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,   dtype=np.intaa )
         __A          =				lm_model(input_ids=_UpperCamelCase    ,   decoder_input_ids=_UpperCamelCase )
         __A          =				(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['''logits'''].shape    ,   _UpperCamelCase )






   def _lowerCAmelCase      (self	:Tuple )->					Tuple:
         __A          =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,   dtype=np.intaa )
         __A          =				shift_tokens_right(_UpperCamelCase    ,   1    ,   2 )
         __A          =				np.equal(_UpperCamelCase    ,   1 ).astype(np.floataa ).sum()
         __A          =				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_					(			_lowerCamelCase ,      unittest.TestCase ,      _lowerCamelCase   ):
   lowerCAmelCase__        = True
   lowerCAmelCase__        = (
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   lowerCAmelCase__        = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




   def _lowerCAmelCase      (self	:List[str] )->					Optional[int]:
         __A          =				FlaxBlenderbotModelTester(self )




   def _lowerCAmelCase      (self	:List[str] )->					List[str]:
         __A   ,						__A          =				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	:Dict )->					List[str]:
         __A   ,						__A          =				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	:Union[str, Any] )->					Union[str, Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				self._prepare_for_class(_UpperCamelCase    ,   _UpperCamelCase )
                     __A          =				model_class(_UpperCamelCase )

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

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				encode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				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	:List[str] )->					List[Any]:
         __A   ,						__A          =				self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__ ):
                     __A          =				model_class(_UpperCamelCase )
                     __A          =				model.encode(inputs_dict['''input_ids''']    ,   inputs_dict['''attention_mask'''] )

                     __A          =				{
                         '''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	:Tuple    ,   _UpperCamelCase	:Dict ):
                           return model.decode(
                               decoder_input_ids=_UpperCamelCase    ,   decoder_attention_mask=_UpperCamelCase    ,   encoder_outputs=_UpperCamelCase    ,   )

                     with self.subTest('''JIT Enabled''' ):
                           __A          =				decode_jitted(**_UpperCamelCase ).to_tuple()

                     with self.subTest('''JIT Disabled''' ):
                           with jax.disable_jit():
                                 __A          =				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	:int )->					Optional[int]:
         for model_class_name in self.all_model_classes:
               __A          =				model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __A          =				np.ones((1, 1) ) * model.config.eos_token_id
               __A          =				model(_UpperCamelCase )
               self.assertIsNotNone(_UpperCamelCase )






   @unittest.skipUnless(jax_device != '''cpu'''    ,   '''3B test too slow on CPU.''' )
   @slow
   def _lowerCAmelCase      (self	:Dict )->					List[str]:
         __A          =				{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
         __A          =				{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

         __A          =				FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''    ,   from_pt=_UpperCamelCase )
         __A          =				BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )

         __A          =				['''Sam''']
         __A          =				tokenizer(_UpperCamelCase    ,   return_tensors='''jax''' )

         __A          =				model.generate(**_UpperCamelCase    ,   **_UpperCamelCase )
         __A          =				'''Sam is a great name. It means "sun" in Gaelic.'''

         __A          =				tokenizer.batch_decode(_UpperCamelCase    ,   **_UpperCamelCase )
         assert generated_txt[0].strip() == tgt_text





