
"""simple docstring"""





import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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


_a		       =							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>=1.14.0', 'To fix: pip install -r examples/pytorch/audio-classification/requirements.txt')







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = 1_6000  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    int(round(sample_rate * max_length  )  )
   if len(__lowerCamelCase  ) <= sample_length:
      return wav
   UpperCAmelCase_					:					Union[str, Any]			     =    randint(0,		len(__lowerCamelCase  ) - sample_length - 1  )
   return wav[random_offset : random_offset + sample_length]


@dataclass
class A_	:




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(default=lowercase__				,metadata={"""help""": """Name of a dataset from the datasets package"""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """A file containing the training audio paths and labels."""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """A file containing the validation audio paths and labels."""}      )
  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default="""train"""				,metadata={
          """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
      }				,)
  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default="""validation"""				,metadata={
          """help""": (
              """The name of the training data set split to use (via the datasets library). Defaults to 'validation'"""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default="""audio"""				,metadata={"""help""": """The name of the dataset column containing the audio data. Defaults to 'audio'"""}				,)
  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default="""label"""				,metadata={"""help""": """The name of the dataset column containing the labels. Defaults to 'label'"""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		field(
      default=lowercase__				,metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		field(
      default=lowercase__				,metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						float       =		field(
      default=20				,metadata={"""help""": """Audio clips will be randomly cut to this length during training if the value is set."""}				,)




@dataclass
class A_	:




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default="""facebook/wav2vec2-base"""				,metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				,)
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """Where do you want to store the pretrained models downloaded from the Hub"""}      )
  SCREAMING_SNAKE_CASE__			:						str       =		field(
      default="""main"""				,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}				,)
  SCREAMING_SNAKE_CASE__			:						Optional[str]       =		field(
      default=lowercase__				,metadata={"""help""": """Name or path of preprocessor config."""}      )
  SCREAMING_SNAKE_CASE__			:						bool       =		field(
      default=lowercase__				,metadata={"""help""": """Whether to freeze the feature encoder layers of the model."""}      )
  SCREAMING_SNAKE_CASE__			:						bool       =		field(
      default=lowercase__				,metadata={"""help""": """Whether to generate an attention mask in the feature extractor."""}      )
  SCREAMING_SNAKE_CASE__			:						bool       =		field(
      default=lowercase__				,metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }				,)
  SCREAMING_SNAKE_CASE__			:						Optional[bool]       =		field(
      default=lowercase__				,metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}      )
  SCREAMING_SNAKE_CASE__			:						bool       =		field(
      default=lowercase__				,metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}				,)







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     if not self.freeze_feature_extractor and self.freeze_feature_encoder:
        warnings.warn(
            "The argument `--freeze_feature_extractor` is deprecated and "
            "will be removed in a future version. Use `--freeze_feature_encoder`"
            "instead. Setting `freeze_feature_encoder==True`."				,   lowercase_				,   )
     if self.freeze_feature_extractor and not self.freeze_feature_encoder:
        raise ValueError(
            "The argument `--freeze_feature_extractor` is deprecated and "
            "should not be used in combination with `--freeze_feature_encoder`."
            "Only make use of `--freeze_feature_encoder`."  )







def 	__a     (		):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   UpperCAmelCase_					:					List[str]			     =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
   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.
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
   else:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    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_audio_classification",		__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()

   UpperCAmelCase_					:					Union[str, Any]			     =    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}"""  )

   # Set seed before initializing model.
   set_seed(training_args.seed  )

   # Detecting last checkpoint.
   UpperCAmelCase_					:					Tuple			     =    None
   if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
      UpperCAmelCase_					:					Dict			     =    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 train from scratch."  )
      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 and prepare it for the audio classification task.
   UpperCAmelCase_					:					Optional[Any]			     =    DatasetDict()
   UpperCAmelCase_					:					int			     =    load_dataset(
       data_args.dataset_name,		data_args.dataset_config_name,		split=data_args.train_split_name,		use_auth_token=True if model_args.use_auth_token else None,		)
   UpperCAmelCase_					:					List[str]			     =    load_dataset(
       data_args.dataset_name,		data_args.dataset_config_name,		split=data_args.eval_split_name,		use_auth_token=True if model_args.use_auth_token else None,		)

   if data_args.audio_column_name not in raw_datasets["train"].column_names:
      raise ValueError(
          f"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
          "Make sure to set `--audio_column_name` to the correct audio column - one of "
          f"""{", ".join(raw_datasets["train"].column_names  )}."""  )

   if data_args.label_column_name not in raw_datasets["train"].column_names:
      raise ValueError(
          f"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
          "Make sure to set `--label_column_name` to the correct text column - one of "
          f"""{", ".join(raw_datasets["train"].column_names  )}."""  )

   # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
   # transformer outputs in the classifier, but it doesn't always lead to better accuracy
   UpperCAmelCase_					:					Any			     =    AutoFeatureExtractor.from_pretrained(
       model_args.feature_extractor_name or model_args.model_name_or_path,		return_attention_mask=model_args.attention_mask,		cache_dir=model_args.cache_dir,		revision=model_args.model_revision,		use_auth_token=True if model_args.use_auth_token else None,		)

   # `datasets` takes care of automatically loading and resampling the audio,
   # so we just need to set the correct target sampling rate.
   UpperCAmelCase_					:					Tuple			     =    raw_datasets.cast_column(
       data_args.audio_column_name,		datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate  )  )

   UpperCAmelCase_					:					str			     =    feature_extractor.model_input_names[0]

   def train_transforms(__lowerCamelCase  ):
      UpperCAmelCase_					:					List[Any]			     =    []
      for audio in batch[data_args.audio_column_name]:
         UpperCAmelCase_					:					str			     =    random_subsample(
             audio["array"],		max_length=data_args.max_length_seconds,		sample_rate=feature_extractor.sampling_rate  )
         subsampled_wavs.append(__lowerCamelCase  )
      UpperCAmelCase_					:					int			     =    feature_extractor(__lowerCamelCase,		sampling_rate=feature_extractor.sampling_rate  )
      UpperCAmelCase_					:					List[Any]			     =    {model_input_name: inputs.get(__lowerCamelCase  )}
      UpperCAmelCase_					:					Optional[int]			     =    list(batch[data_args.label_column_name]  )

      return output_batch

   def val_transforms(__lowerCamelCase  ):
      UpperCAmelCase_					:					Optional[int]			     =    [audio["array"] for audio in batch[data_args.audio_column_name]]
      UpperCAmelCase_					:					List[str]			     =    feature_extractor(__lowerCamelCase,		sampling_rate=feature_extractor.sampling_rate  )
      UpperCAmelCase_					:					Tuple			     =    {model_input_name: inputs.get(__lowerCamelCase  )}
      UpperCAmelCase_					:					Tuple			     =    list(batch[data_args.label_column_name]  )

      return output_batch

   # Prepare label mappings.
   # We'll include these in the model's config to get human readable labels in the Inference API.
   UpperCAmelCase_					:					Union[str, Any]			     =    raw_datasets["train"].features[data_args.label_column_name].names
   UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    {}, {}
   for i, label in enumerate(__lowerCamelCase  ):
      UpperCAmelCase_					:					List[Any]			     =    str(__lowerCamelCase  )
      UpperCAmelCase_					:					List[Any]			     =    label

   # Load the accuracy metric from the datasets package
   UpperCAmelCase_					:					List[str]			     =    evaluate.load("accuracy"  )

   # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
   # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
   def compute_metrics(__lowerCamelCase  ):
      UpperCAmelCase_					:					str			     =    np.argmax(eval_pred.predictions,		axis=1  )
      return metric.compute(predictions=__lowerCamelCase,		references=eval_pred.label_ids  )

   UpperCAmelCase_					:					int			     =    AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path,		num_labels=len(__lowerCamelCase  ),		labelaid=__lowerCamelCase,		idalabel=__lowerCamelCase,		finetuning_task="audio-classification",		cache_dir=model_args.cache_dir,		revision=model_args.model_revision,		use_auth_token=True if model_args.use_auth_token else None,		)
   UpperCAmelCase_					:					List[Any]			     =    AutoModelForAudioClassification.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,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,		)

   # freeze the convolutional waveform encoder
   if model_args.freeze_feature_encoder:
      model.freeze_feature_encoder()

   if training_args.do_train:
      if data_args.max_train_samples is not None:
         UpperCAmelCase_					:					Tuple			     =    (
             raw_datasets["train"].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
         )
      # Set the training transforms
      raw_datasets["train"].set_transform(__lowerCamelCase,		output_all_columns=__lowerCamelCase  )

   if training_args.do_eval:
      if data_args.max_eval_samples is not None:
         UpperCAmelCase_					:					Union[str, Any]			     =    (
             raw_datasets["eval"].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
         )
      # Set the validation transforms
      raw_datasets["eval"].set_transform(__lowerCamelCase,		output_all_columns=__lowerCamelCase  )

   # Initialize our trainer
   UpperCAmelCase_					:					str			     =    Trainer(
       model=__lowerCamelCase,		args=__lowerCamelCase,		train_dataset=raw_datasets["train"] if training_args.do_train else None,		eval_dataset=raw_datasets["eval"] if training_args.do_eval else None,		compute_metrics=__lowerCamelCase,		tokenizer=__lowerCamelCase,		)

   # Training
   if training_args.do_train:
      UpperCAmelCase_					:					Union[str, Any]			     =    None
      if training_args.resume_from_checkpoint is not None:
         UpperCAmelCase_					:					Optional[int]			     =    training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         UpperCAmelCase_					:					Union[str, Any]			     =    last_checkpoint
      UpperCAmelCase_					:					str			     =    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:
      UpperCAmelCase_					:					List[Any]			     =    trainer.evaluate()
      trainer.log_metrics("eval",		__lowerCamelCase  )
      trainer.save_metrics("eval",		__lowerCamelCase  )

   # Write model card and (optionally) push to hub
   UpperCAmelCase_					:					int			     =    {
       "finetuned_from": model_args.model_name_or_path,
       "tasks": "audio-classification",
       "dataset": data_args.dataset_name,
       "tags": ["audio-classification"],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__lowerCamelCase  )
   else:
      trainer.create_model_card(**__lowerCamelCase  )


if __name__ == "__main__":
    main()




"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
    _a		       =							'platform'

    import jax
    import jax.numpy as jnp

    from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
        FlaxBlenderbotSmallForConditionalGeneration,
        FlaxBlenderbotSmallModel,
        shift_tokens_right,
    )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		__lowerCamelCase=None,		):
   if attention_mask is None:
      UpperCAmelCase_					:					Union[str, Any]			     =    np.where(input_ids != config.pad_token_id,		1,		0  )
   if decoder_attention_mask is None:
      UpperCAmelCase_					:					Optional[int]			     =    np.where(decoder_input_ids != config.pad_token_id,		1,		0  )
   if head_mask is None:
      UpperCAmelCase_					:					int			     =    np.ones((config.encoder_layers, config.encoder_attention_heads)  )
   if decoder_head_mask is None:
      UpperCAmelCase_					:					Union[str, Any]			     =    np.ones((config.decoder_layers, config.decoder_attention_heads)  )
   if cross_attn_head_mask is None:
      UpperCAmelCase_					:					List[Any]			     =    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_	:




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=13				,   lowercase_=7				,   lowercase_=True				,   lowercase_=False				,   lowercase_=99				,   lowercase_=16				,   lowercase_=2				,   lowercase_=4				,   lowercase_=4				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=32				,   lowercase_=2				,   lowercase_=1				,   lowercase_=0				,   lowercase_=0.02				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    parent
     UpperCAmelCase_					:					Tuple			     =    batch_size
     UpperCAmelCase_					:					str			     =    seq_length
     UpperCAmelCase_					:					Dict			     =    is_training
     UpperCAmelCase_					:					List[Any]			     =    use_labels
     UpperCAmelCase_					:					Optional[int]			     =    vocab_size
     UpperCAmelCase_					:					int			     =    hidden_size
     UpperCAmelCase_					:					Optional[Any]			     =    num_hidden_layers
     UpperCAmelCase_					:					Dict			     =    num_attention_heads
     UpperCAmelCase_					:					List[str]			     =    intermediate_size
     UpperCAmelCase_					:					Optional[int]			     =    hidden_act
     UpperCAmelCase_					:					str			     =    hidden_dropout_prob
     UpperCAmelCase_					:					int			     =    attention_probs_dropout_prob
     UpperCAmelCase_					:					Optional[Any]			     =    max_position_embeddings
     UpperCAmelCase_					:					str			     =    eos_token_id
     UpperCAmelCase_					:					str			     =    pad_token_id
     UpperCAmelCase_					:					str			     =    bos_token_id
     UpperCAmelCase_					:					List[Any]			     =    initializer_range







  def        UpperCamelCase__					(		self  ):

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

     UpperCAmelCase_					:					str			     =    shift_tokens_right(lowercase_				,   1				,   2  )

     UpperCAmelCase_					:					str			     =    BlenderbotSmallConfig(
         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=lowercase_				,   )
     UpperCAmelCase_					:					Optional[int]			     =    prepare_blenderbot_inputs_dict(lowercase_				,   lowercase_				,   lowercase_  )
     return config, inputs_dict







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    self.prepare_config_and_inputs()
     return config, inputs_dict







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    20
     UpperCAmelCase_					:					int			     =    model_class_name(lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    model.encode(inputs_dict["input_ids"]  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     UpperCAmelCase_					:					Any			     =    model.init_cache(decoder_input_ids.shape[0]				,   lowercase_				,   lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,   dtype="i4"  )

     UpperCAmelCase_					:					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)				,   )
     UpperCAmelCase_					:					int			     =    model.decode(
         decoder_input_ids[:, :-1]				,   lowercase_				,   decoder_attention_mask=lowercase_				,   past_key_values=lowercase_				,   decoder_position_ids=lowercase_				,   )

     UpperCAmelCase_					:					int			     =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,   dtype="i4"  )
     UpperCAmelCase_					:					Dict			     =    model.decode(
         decoder_input_ids[:, -1:]				,   lowercase_				,   decoder_attention_mask=lowercase_				,   past_key_values=outputs_cache.past_key_values				,   decoder_position_ids=lowercase_				,   )

     UpperCAmelCase_					:					Optional[Any]			     =    model.decode(lowercase_				,   lowercase_  )

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    20
     UpperCAmelCase_					:					Any			     =    model_class_name(lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    model.encode(inputs_dict["input_ids"]  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

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

     UpperCAmelCase_					:					int			     =    model.init_cache(decoder_input_ids.shape[0]				,   lowercase_				,   lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]				,   (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,   )

     UpperCAmelCase_					:					List[str]			     =    model.decode(
         decoder_input_ids[:, :-1]				,   lowercase_				,   decoder_attention_mask=lowercase_				,   past_key_values=lowercase_				,   decoder_position_ids=lowercase_				,   )
     UpperCAmelCase_					:					Any			     =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,   dtype="i4"  )
     UpperCAmelCase_					:					Dict			     =    model.decode(
         decoder_input_ids[:, -1:]				,   lowercase_				,   past_key_values=outputs_cache.past_key_values				,   decoder_attention_mask=lowercase_				,   decoder_position_ids=lowercase_				,   )

     UpperCAmelCase_					:					Dict			     =    model.decode(lowercase_				,   lowercase_				,   decoder_attention_mask=lowercase_  )

     UpperCAmelCase_					:					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}"""  )




@require_flax
class A_	(unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		99







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    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				,   )

     UpperCAmelCase_					:					Any			     =    input_ids.shape[0]
     UpperCAmelCase_					:					Dict			     =    BlenderbotSmallConfig(
         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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self._get_config_and_data()
     UpperCAmelCase_					:					List[str]			     =    FlaxBlenderbotSmallForConditionalGeneration(lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    lm_model(input_ids=lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    BlenderbotSmallConfig(
         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				,   )
     UpperCAmelCase_					:					Optional[int]			     =    FlaxBlenderbotSmallForConditionalGeneration(lowercase_  )
     UpperCAmelCase_					:					str			     =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				,   dtype=np.intaa  )
     UpperCAmelCase_					:					str			     =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				,   dtype=np.intaa  )
     UpperCAmelCase_					:					Tuple			     =    lm_model(input_ids=lowercase_				,   decoder_input_ids=lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    (*summary.shape, config.vocab_size)
     self.assertEqual(outputs["logits"].shape				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				,   dtype=np.intaa  )
     UpperCAmelCase_					:					Dict			     =    shift_tokens_right(lowercase_				,   1				,   2  )
     UpperCAmelCase_					:					Tuple			     =    np.equal(lowercase_				,   1  ).astype(np.floataa  ).sum()
     UpperCAmelCase_					:					Optional[Any]			     =    np.equal(lowercase_				,   1  ).astype(np.floataa  ).sum()
     self.assertEqual(shifted.shape				,   input_ids.shape  )
     self.assertEqual(lowercase_				,   n_pad_before - 1  )
     self.assertTrue(np.equal(shifted[:, 0]				,   2  ).all()  )




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




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						str       =		True
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(
      (
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__			:						List[Any]       =		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    FlaxBlenderbotSmallModelTester(self  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(lowercase_				,   lowercase_				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[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(lowercase_				,   lowercase_				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					List[Any]			     =    self._prepare_for_class(lowercase_				,   lowercase_  )
           UpperCAmelCase_					:					Dict			     =    model_class(lowercase_  )

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

           with self.subTest("JIT Enabled"  ):
              UpperCAmelCase_					:					List[Any]			     =    encode_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					Optional[Any]			     =    encode_jitted(**lowercase_  ).to_tuple()

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__  ):
           UpperCAmelCase_					:					Optional[int]			     =    model_class(lowercase_  )
           UpperCAmelCase_					:					Tuple			     =    model.encode(inputs_dict["input_ids"]				,   inputs_dict["attention_mask"]  )

           UpperCAmelCase_					:					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(lowercase_				,   lowercase_				,   lowercase_  ):
              return model.decode(
                  decoder_input_ids=lowercase_				,   decoder_attention_mask=lowercase_				,   encoder_outputs=lowercase_				,   )

           with self.subTest("JIT Enabled"  ):
              UpperCAmelCase_					:					str			     =    decode_jitted(**lowercase_  ).to_tuple()

           with self.subTest("JIT Disabled"  ):
              with jax.disable_jit():
                 UpperCAmelCase_					:					List[Any]			     =    decode_jitted(**lowercase_  ).to_tuple()

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







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_class_name in self.all_model_classes:
        UpperCAmelCase_					:					Optional[Any]			     =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"  )
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        UpperCAmelCase_					:					List[str]			     =    np.ones((1, 1)  ) * model.config.eos_token_id
        UpperCAmelCase_					:					Optional[int]			     =    model(lowercase_  )
        self.assertIsNotNone(lowercase_  )


