



from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
   import datasets
class 					a	(  UpperCamelCase_       ):



  @slow
  @require_torch
  def 	lowerCamelCase__       (						self :					Any		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""       ,		"""prajjwal1/bert-tiny"""		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=BertTokenizer.from_pretrained("""bert-base-uncased"""		)

     SCREAMING_SNAKE_CASE_: Any     			=bertabert.config.encoder.vocab_size
     SCREAMING_SNAKE_CASE_: List[str]     			=tokenizer.sep_token_id
     SCREAMING_SNAKE_CASE_: Any     			=tokenizer.cls_token_id
     SCREAMING_SNAKE_CASE_: Tuple     			=128

     SCREAMING_SNAKE_CASE_: str     			=datasets.load_dataset("""cnn_dailymail"""       ,		"""3.0.0"""       ,		split="""train[:1%]"""		)
     SCREAMING_SNAKE_CASE_: List[str]     			=datasets.load_dataset("""cnn_dailymail"""       ,		"""3.0.0"""       ,		split="""validation[:1%]"""		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=train_dataset.select(range(32		)		)
     SCREAMING_SNAKE_CASE_: Tuple     			=val_dataset.select(range(16		)		)

     SCREAMING_SNAKE_CASE_: List[str]     			=4

     def _map_to_encoder_decoder_inputs(lowerCAmelCase :					Optional[int]		):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        SCREAMING_SNAKE_CASE_: Dict     			=tokenizer(batch["""article"""]       ,		padding="""max_length"""       ,		truncation=__A       ,		max_length=512		)
        SCREAMING_SNAKE_CASE_: List[str]     			=tokenizer(batch["""highlights"""]       ,		padding="""max_length"""       ,		truncation=__A       ,		max_length=128		)
        SCREAMING_SNAKE_CASE_: str     			=inputs.input_ids
        SCREAMING_SNAKE_CASE_: int     			=inputs.attention_mask

        SCREAMING_SNAKE_CASE_: Tuple     			=outputs.input_ids
        SCREAMING_SNAKE_CASE_: Dict     			=outputs.input_ids.copy()
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=[
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
        ]
        SCREAMING_SNAKE_CASE_: List[Any]     			=outputs.attention_mask

        assert all(len(__A		) == 512 for x in inputs.input_ids		)
        assert all(len(__A		) == 128 for x in outputs.input_ids		)

        return batch

     def _compute_metrics(lowerCAmelCase :					Union[str, Any]		):
        SCREAMING_SNAKE_CASE_: Dict     			=pred.label_ids
        SCREAMING_SNAKE_CASE_: Any     			=pred.predictions

        # all unnecessary tokens are removed
        SCREAMING_SNAKE_CASE_: str     			=tokenizer.batch_decode(__A       ,		skip_special_tokens=__A		)
        SCREAMING_SNAKE_CASE_: Any     			=tokenizer.batch_decode(__A       ,		skip_special_tokens=__A		)

        SCREAMING_SNAKE_CASE_: str     			=sum([int(pred_str[i] == label_str[i]		) for i in range(len(__A		)		)]		) / len(__A		)

        return {"accuracy": accuracy}

     # map train dataset
     SCREAMING_SNAKE_CASE_: Tuple     			=train_dataset.map(
         _map_to_encoder_decoder_inputs       ,		batched=__A       ,		batch_size=__A       ,		remove_columns=["""article""", """highlights"""]       ,		)
     train_dataset.set_format(
         type="""torch"""       ,		columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]       ,		)

     # same for validation dataset
     SCREAMING_SNAKE_CASE_: int     			=val_dataset.map(
         _map_to_encoder_decoder_inputs       ,		batched=__A       ,		batch_size=__A       ,		remove_columns=["""article""", """highlights"""]       ,		)
     val_dataset.set_format(
         type="""torch"""       ,		columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]       ,		)

     SCREAMING_SNAKE_CASE_: Dict     			=self.get_auto_remove_tmp_dir()

     SCREAMING_SNAKE_CASE_: List[str]     			=SeqaSeqTrainingArguments(
         output_dir=__A       ,		per_device_train_batch_size=__A       ,		per_device_eval_batch_size=__A       ,		predict_with_generate=__A       ,		evaluation_strategy="""steps"""       ,		do_train=__A       ,		do_eval=__A       ,		warmup_steps=0       ,		eval_steps=2       ,		logging_steps=2       ,		)

     # instantiate trainer
     SCREAMING_SNAKE_CASE_: Dict     			=SeqaSeqTrainer(
         model=__A       ,		args=__A       ,		compute_metrics=_compute_metrics       ,		train_dataset=__A       ,		eval_dataset=__A       ,		tokenizer=__A       ,		)

     # start training
     trainer.train()








"""simple docstring"""
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image


_UpperCAmelCase     =			["""text""", """image""", """audio"""]





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=[]

   for input_type in input_types:
      if input_type == "text":
         inputs.append("""Text input""" )
      elif input_type == "image":
         inputs.append(
             Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((512, 512) ) )
      elif input_type == "audio":
         inputs.append(torch.ones(3000 ) )
      elif isinstance(lowercase							,			lowercase ):
         inputs.append(create_inputs(lowercase ) )
      else:
         raise ValueError(f'''Invalid type requested: {input_type}''' )

   return inputs





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=[]

   for output in outputs:
      if isinstance(lowercase							,			(str, AgentText) ):
         output_types.append("""text""" )
      elif isinstance(lowercase							,			(Image.Image, AgentImage) ):
         output_types.append("""image""" )
      elif isinstance(lowercase							,			(torch.Tensor, AgentAudio) ):
         output_types.append("""audio""" )
      else:
         raise ValueError(f'''Invalid output: {output}''' )

   return output_types
@is_tool_test
class 					a	:



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[Any]:


     '''simple docstring'''




     self.assertTrue(hasattr(self.tool       ,		"""inputs"""		)		)
     self.assertTrue(hasattr(self.tool       ,		"""outputs"""		)		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.tool.inputs
     for _input in inputs:
        if isinstance(_input       ,		lowerCAmelCase		):
           for __input in _input:
              self.assertTrue(__input in authorized_types		)
        else:
           self.assertTrue(_input in authorized_types		)

     SCREAMING_SNAKE_CASE_: Any     			=self.tool.outputs
     for _output in outputs:
        self.assertTrue(_output in authorized_types		)



  def 	lowerCamelCase__       (						self :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.tool(*lowerCAmelCase		)

     # There is a single output
     if len(self.tool.outputs		) == 1:
        SCREAMING_SNAKE_CASE_: str     			=[outputs]

     self.assertListEqual(output_types(lowerCAmelCase		)       ,		self.tool.outputs		)



  def 	lowerCamelCase__       (						self :					str		)     ->					Optional[Any]:


     '''simple docstring'''




     self.assertTrue(hasattr(self.tool       ,		"""description"""		)		)
     self.assertTrue(hasattr(self.tool       ,		"""default_checkpoint"""		)		)
     self.assertTrue(self.tool.description.startswith("""This is a tool that"""		)		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=create_inputs(self.tool.inputs		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=[outputs]

     self.assertEqual(len(lowerCAmelCase		)       ,		len(self.tool.outputs		)		)

     for output, output_type in zip(lowerCAmelCase       ,		self.tool.outputs		):
        SCREAMING_SNAKE_CASE_: int     			=AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(lowerCAmelCase       ,		lowerCAmelCase		)		)





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=create_inputs(self.tool.inputs		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[]

     for _input, input_type in zip(lowerCAmelCase       ,		self.tool.inputs		):
        if isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
           _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input		) for _input_type in input_type]		)
        else:
           _inputs.append(AGENT_TYPE_MAPPING[input_type](_input		)		)

        # Should not raise an error
     SCREAMING_SNAKE_CASE_: Dict     			=self.tool(*lowerCAmelCase		)

     if not isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: List[str]     			=[outputs]

     self.assertEqual(len(lowerCAmelCase		)       ,		len(self.tool.outputs		)		)



