






from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel







@require_tf
class 				UpperCAmelCase_  :
     __lowerCamelCase      				=  BlenderbotConfig
     __lowerCamelCase      				=  {}
     __lowerCamelCase      				=  'gelu'




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=13	,						_lowerCAmelCase=7	,						_lowerCAmelCase=True	,						_lowerCAmelCase=False	,						_lowerCAmelCase=99	,						_lowerCAmelCase=32	,						_lowerCAmelCase=2	,						_lowerCAmelCase=4	,						_lowerCAmelCase=37	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=20	,						_lowerCAmelCase=2	,						_lowerCAmelCase=1	,						_lowerCAmelCase=0	,						):
          UpperCAmelCase__      :  List[str]				      =			parent
          UpperCAmelCase__      :  str				      =			batch_size
          UpperCAmelCase__      :  Optional[int]				      =			seq_length
          UpperCAmelCase__      :  Dict				      =			is_training
          UpperCAmelCase__      :  List[Any]				      =			use_labels
          UpperCAmelCase__      :  List[Any]				      =			vocab_size
          UpperCAmelCase__      :  Optional[int]				      =			hidden_size
          UpperCAmelCase__      :  Optional[Any]				      =			num_hidden_layers
          UpperCAmelCase__      :  int				      =			num_attention_heads
          UpperCAmelCase__      :  Any				      =			intermediate_size

          UpperCAmelCase__      :  Dict				      =			hidden_dropout_prob
          UpperCAmelCase__      :  int				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  Any				      =			max_position_embeddings
          UpperCAmelCase__      :  Dict				      =			eos_token_id
          UpperCAmelCase__      :  List[Any]				      =			pad_token_id
          UpperCAmelCase__      :  Optional[int]				      =			bos_token_id




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			ids_tensor([self.batch_size, self.seq_length - 1]	,						self.vocab_size		)
          UpperCAmelCase__      :  List[str]				      =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)	,						1		)
          UpperCAmelCase__      :  Any				      =			tf.concat([input_ids, eos_tensor]	,						axis=1		)

          UpperCAmelCase__      :  List[Any]				      =			ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size		)

          UpperCAmelCase__      :  Dict				      =			self.config_cls(
              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_ids=[2]	,						bos_token_id=self.bos_token_id	,						pad_token_id=self.pad_token_id	,						decoder_start_token_id=self.pad_token_id	,						**self.config_updates	,						)
          UpperCAmelCase__      :  Tuple				      =			prepare_blenderbot_inputs_dict(_lowerCAmelCase	,						_lowerCAmelCase	,						_lowerCAmelCase		)
          return config, inputs_dict




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			TFBlenderbotModel(config=_lowerCAmelCase		).get_decoder()
          UpperCAmelCase__      :  Dict				      =			inputs_dict["""input_ids"""]

          UpperCAmelCase__      :  Union[str, Any]				      =			input_ids[:1, :]
          UpperCAmelCase__      :  Dict				      =			inputs_dict["""attention_mask"""][:1, :]
          UpperCAmelCase__      :  List[Any]				      =			inputs_dict["""head_mask"""]
          UpperCAmelCase__      :  Optional[Any]				      =			1

          # first forward pass
          UpperCAmelCase__      :  List[str]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase	,						head_mask=_lowerCAmelCase	,						use_cache=_lowerCAmelCase		)

          UpperCAmelCase__      ,		UpperCAmelCase__      :  List[Any]				      =			outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          UpperCAmelCase__      :  List[str]				      =			ids_tensor((self.batch_size, 3)	,						config.vocab_size		)
          UpperCAmelCase__      :  Tuple				      =			tf.cast(ids_tensor((self.batch_size, 3)	,						2		)	,						tf.inta		)

          # append to next input_ids and
          UpperCAmelCase__      :  str				      =			tf.concat([input_ids, next_tokens]	,						axis=-1		)
          UpperCAmelCase__      :  int				      =			tf.concat([attention_mask, next_attn_mask]	,						axis=-1		)

          UpperCAmelCase__      :  List[str]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase		)[0]
          UpperCAmelCase__      :  Optional[Any]				      =			model(_lowerCAmelCase	,						attention_mask=_lowerCAmelCase	,						past_key_values=_lowerCAmelCase		)[0]

          self.parent.assertEqual(next_tokens.shape[1]	,						output_from_past.shape[1]		)

          # select random slice
          UpperCAmelCase__      :  Any				      =			int(ids_tensor((1,)	,						output_from_past.shape[-1]		)		)
          UpperCAmelCase__      :  Any				      =			output_from_no_past[:, -3:, random_slice_idx]
          UpperCAmelCase__      :  Any				      =			output_from_past[:, :, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(_lowerCAmelCase	,						_lowerCAmelCase	,						rtol=1e-3		)





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							__lowerCamelCase=None ,							) ->			List[Any]:

     '''simple docstring'''
     if attention_mask is None:
          UpperCAmelCase__      :  Tuple				      =			tf.cast(tf.math.not_equal(__lowerCamelCase ,							config.pad_token_id       ) ,							tf.inta       )
     if decoder_attention_mask is None:
          UpperCAmelCase__      :  Tuple				      =			tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape ,							dtype=tf.inta       ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,							config.pad_token_id       ) ,							tf.inta       ),
              ] ,							axis=-1 ,							)
     if head_mask is None:
          UpperCAmelCase__      :  Any				      =			tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
     if decoder_head_mask is None:
          UpperCAmelCase__      :  int				      =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
     if cross_attn_head_mask is None:
          UpperCAmelCase__      :  int				      =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }







@require_tf
class 				UpperCAmelCase_  (		__lowerCamelCase							,      __lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __lowerCamelCase      				=  (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __lowerCamelCase      				=  (
         {
             'conversational': TFBlenderbotForConditionalGeneration,
             'feature-extraction': TFBlenderbotModel,
             'summarization': TFBlenderbotForConditionalGeneration,
             'text2text-generation': TFBlenderbotForConditionalGeneration,
             'translation': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __lowerCamelCase      				=  True
     __lowerCamelCase      				=  False
     __lowerCamelCase      				=  False




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Union[str, Any]				      =			TFBlenderbotModelTester(self		)
          UpperCAmelCase__      :  int				      =			ConfigTester(self	,						config_class=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          self.config_tester.run_common_tests()




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Dict				      =			self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase		)







@require_tokenizers
@require_tf
class 				UpperCAmelCase_  (		unittest.TestCase		):
     __lowerCamelCase      				=  ['My friends are cool but they eat too many carbs.']
     __lowerCamelCase      				=  'facebook/blenderbot-400M-distill'




     @cached_property
     def   __UpperCAmelCase  (				self		):
          return BlenderbotTokenizer.from_pretrained(self.model_name		)




     @cached_property
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
          return model




     @slow
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			self.tokenizer(self.src_text	,						return_tensors="""tf"""		)
          UpperCAmelCase__      :  Union[str, Any]				      =			self.model.generate(
              model_inputs.input_ids	,						)
          UpperCAmelCase__      :  List[str]				      =			self.tokenizer.batch_decode(generated_ids.numpy()	,						skip_special_tokens=_lowerCAmelCase		)[0]
          assert (
              generated_words
              == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
          )







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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCAmelCase       =		logging.getLogger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return (preds == labels).mean()
@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained config name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},  )




@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
							UpperCAmelCase				   =     field(metadata={"help": "Should contain the data files for the task."} )
							UpperCAmelCase				   =     field(
							    default=128,  metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Overwrite the cached training and evaluation sets"} )




def 					_snake_case	(  ):
					# 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			     =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
					_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir						)
					    and os.listdir(training_args.output_dir						)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    ''' --overwrite_output_dir to overcome.'''						)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,     datefmt='''%m/%d/%Y %H:%M:%S'''  ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,     )
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,     training_args.local_rank  ,     training_args.device  ,     training_args.n_gpu  ,     bool(training_args.local_rank != -1						)  ,     training_args.fpaa  ,     )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank						):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s'''  ,     __snake_case						)

					# Set seed
					set_seed(training_args.seed						)

					try:
										_UpperCamelCase			     =     processors[data_args.task_name]()
										_UpperCamelCase			     =     processor.get_labels()
										_UpperCamelCase			     =     len(__snake_case						)
					except KeyError:
										raise ValueError('''Task not found: %s''' % (data_args.task_name)						)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					_UpperCamelCase			     =     AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,     num_labels=__snake_case  ,     finetuning_task=data_args.task_name  ,     cache_dir=model_args.cache_dir  ,     )
					_UpperCamelCase			     =     AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,     cache_dir=model_args.cache_dir  ,     )
					_UpperCamelCase			     =     AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path  ,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)  ,     config=__snake_case  ,     cache_dir=model_args.cache_dir  ,     )

					# Get datasets
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.train  ,     )
					    if training_args.do_train
					    else None
					)
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.dev  ,     )
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(__snake_case						) -> Dict:
										_UpperCamelCase			     =     np.argmax(p.predictions  ,     axis=1						)
										return {"acc": simple_accuracy(__snake_case  ,     p.label_ids						)}

					# Data collator
					_UpperCamelCase			     =     DataCollatorWithPadding(__snake_case  ,     pad_to_multiple_of=8						) if training_args.fpaa else None

					# Initialize our Trainer
					_UpperCamelCase			     =     Trainer(
					    model=__snake_case  ,     args=__snake_case  ,     train_dataset=__snake_case  ,     eval_dataset=__snake_case  ,     compute_metrics=__snake_case  ,     data_collator=__snake_case  ,     )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
					_UpperCamelCase			     =     {}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''						)

										_UpperCamelCase			     =     trainer.evaluate()

										_UpperCamelCase			     =     os.path.join(training_args.output_dir  ,     '''eval_results.txt'''						)
										if trainer.is_world_master():
															with open(__snake_case  ,     '''w'''						) as writer:
																				logger.info('''***** Eval results *****'''						)
																				for key, value in result.items():
																									logger.info('''  %s = %s'''  ,     __snake_case  ,     __snake_case						)
																									writer.write('''%s = %s\n''' % (key, value)						)

																				results.update(__snake_case						)

					return results
def 					_snake_case	(  __snake_case						):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
					main()
