



import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_UpperCAmelCase     =			"""true"""





def      __magic_name__				(							lowercase							,			lowercase=82							,			lowercase=16 ):
   set_seed(42 )
   SCREAMING_SNAKE_CASE_: List[str]     			=RegressionModel()
   SCREAMING_SNAKE_CASE_: Dict     			=deepcopy(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=RegressionDataset(length=lowercase )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(lowercase							,			batch_size=lowercase )
   model.to(accelerator.device )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=accelerator.prepare(lowercase							,			lowercase )
   return model, ddp_model, dataloader





def      __magic_name__				(							lowercase							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
   SCREAMING_SNAKE_CASE_: List[str]     			=load_dataset("""glue"""							,			"""mrpc"""							,			split="""validation""" )

   def tokenize_function(lowercase ):
      SCREAMING_SNAKE_CASE_: int     			=tokenizer(examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=lowercase							,			max_length=lowercase )
      return outputs

   with accelerator.main_process_first():
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=dataset.map(
          lowercase							,			batched=lowercase							,			remove_columns=["""idx""", """sentence1""", """sentence2"""]							,			)

   SCREAMING_SNAKE_CASE_: Dict     			=tokenized_datasets.rename_column("""label"""							,			"""labels""" )

   def collate_fn(lowercase ):
      if use_longest:
         return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )

   return DataLoader(lowercase							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=16 )





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=Accelerator(dispatch_batches=lowercase							,			split_batches=lowercase )
   SCREAMING_SNAKE_CASE_: str     			=get_dataloader(lowercase							,			not dispatch_batches )
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoModelForSequenceClassification.from_pretrained(
       """hf-internal-testing/mrpc-bert-base-cased"""							,			return_dict=lowercase )
   SCREAMING_SNAKE_CASE_: Tuple     			=accelerator.prepare(lowercase							,			lowercase )
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=[]
   for batch in dataloader:
      SCREAMING_SNAKE_CASE_: Dict     			=batch.values()
      with torch.no_grad():
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model(lowercase )
         SCREAMING_SNAKE_CASE_: Tuple     			=accelerator.gather_for_metrics((logit, target) )
         logits_and_targets.append((logit, target) )
   SCREAMING_SNAKE_CASE_: Any     			=[], []
   for logit, targ in logits_and_targets:
      logits.append(lowercase )
      targs.append(lowercase )
   SCREAMING_SNAKE_CASE_: Tuple     			=torch.cat(lowercase ), torch.cat(lowercase )
   return logits, targs





def      __magic_name__				(							lowercase							,			lowercase=82							,			lowercase=False							,			lowercase=False							,			lowercase=16 ):
   SCREAMING_SNAKE_CASE_: Tuple     			=get_basic_setup(lowercase							,			lowercase							,			lowercase )
   SCREAMING_SNAKE_CASE_: int     			=generate_predictions(lowercase							,			lowercase							,			lowercase )
   assert (
       len(lowercase ) == num_samples
   ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase )}'''





def      __magic_name__				(							lowercase = False							,			lowercase = False ):
   SCREAMING_SNAKE_CASE_: Dict     			=evaluate.load("""glue"""							,			"""mrpc""" )
   SCREAMING_SNAKE_CASE_: List[str]     			=get_mrpc_setup(lowercase							,			lowercase )
   # First do baseline
   SCREAMING_SNAKE_CASE_: Tuple     			=setup["""no"""]
   model.to(lowercase )
   model.eval()
   for batch in dataloader:
      batch.to(lowercase )
      with torch.inference_mode():
         SCREAMING_SNAKE_CASE_: Optional[int]     			=model(**lowercase )
      SCREAMING_SNAKE_CASE_: int     			=outputs.logits.argmax(dim=-1 )
      metric.add_batch(predictions=lowercase							,			references=batch["""labels"""] )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=metric.compute()

   # Then do distributed
   SCREAMING_SNAKE_CASE_: str     			=setup["""ddp"""]
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(**lowercase )
      SCREAMING_SNAKE_CASE_: Any     			=outputs.logits.argmax(dim=-1 )
      SCREAMING_SNAKE_CASE_: Dict     			=batch["""labels"""]
      SCREAMING_SNAKE_CASE_: Tuple     			=accelerator.gather_for_metrics((preds, references) )
      metric.add_batch(predictions=lowercase							,			references=lowercase )
   SCREAMING_SNAKE_CASE_: str     			=metric.compute()

   for key in "accuracy f1".split():
      assert math.isclose(
          baseline[key]							,			distributed[key] ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=Accelerator(split_batches=lowercase							,			dispatch_batches=lowercase )
   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_warning()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   # These are a bit slower so they should only be ran on the GPU or TPU
   if torch.cuda.is_available() or is_tpu_available():
      if accelerator.is_local_main_process:
         print("""**Testing gather_for_metrics**""" )
      for split_batches in [True, False]:
         for dispatch_batches in [True, False]:
            if accelerator.is_local_main_process:
               print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
            test_mrpc(lowercase							,			lowercase )
            accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test torch metrics**""" )
   for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
         SCREAMING_SNAKE_CASE_: Dict     			=Accelerator(split_batches=lowercase							,			dispatch_batches=lowercase )
         if accelerator.is_local_main_process:
            print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
         test_torch_metrics(lowercase							,			99 )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test last batch is not dropped when perfectly divisible**""" )
   SCREAMING_SNAKE_CASE_: List[str]     			=Accelerator()
   test_torch_metrics(lowercase							,			512 )
   accelerator.state._reset_state()





def      __magic_name__				(							lowercase ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()








"""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		)		)



