
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType



class 		UpperCamelCase__  :
   def __init__(self :					List[str]					,				snake_case_ :					Union[str, Any]=2					,				snake_case_ :					Optional[int]=3					,				snake_case_ :					int=6_4					,				snake_case_ :					Optional[int]=None   ):
    __a :  Dict						=  np.random.default_rng(snake_case_   )
    __a :  str						=  length
    __a :  List[Any]						=  rng.normal(size=(length,)   ).astype(np.floataa   )
    __a :  Union[str, Any]						=  a * self.x + b + rng.normal(scale=0.1					,				size=(length,)   ).astype(np.floataa   )
   def __len__(self :					List[str]   ):
    return self.length







   def __getitem__(self :					List[Any]					,				snake_case_ :					List[str]   ):
    return {"x": self.x[i], "y": self.y[i]}





class 		UpperCamelCase__  (       torch.nn.Module    ):
   def __init__(self :					List[str]					,				snake_case_ :					Optional[Any]=0					,				snake_case_ :					Optional[int]=0					,				snake_case_ :					Dict=False   ):
    super().__init__()
    __a :  Optional[int]						=  torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
    __a :  int						=  torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
    __a :  str						=  True







   def       lowerCAmelCase			(self :					List[str]					,				snake_case_ :					Optional[Any]=None   ):
    if self.first_batch:
     print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"   )
     __a :  List[Any]						=  False
    return x * self.a[0] + self.b[0]





class 		UpperCamelCase__  (       torch.nn.Module    ):
   def __init__(self :					Tuple					,				snake_case_ :					Union[str, Any]=0					,				snake_case_ :					int=0					,				snake_case_ :					Any=False   ):
    super().__init__()
    __a :  Union[str, Any]						=  torch.nn.Parameter(torch.tensor(snake_case_   ).float()   )
    __a :  Optional[int]						=  torch.nn.Parameter(torch.tensor(snake_case_   ).float()   )
    __a :  Union[str, Any]						=  True







   def       lowerCAmelCase			(self :					int					,				snake_case_ :					str=None   ):
    if self.first_batch:
     print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"   )
     __a :  List[Any]						=  False
    return x * self.a + self.b





def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int = 1_6  ):
 from datasets import load_dataset
 from transformers import AutoTokenizer

 __a :  Any						=  AutoTokenizer.from_pretrained('''bert-base-cased'''  )
 __a :  Union[str, Any]						=  {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
 __a :  Any						=  load_dataset('''csv'''  ,   data_files=lowerCAmelCase__  )
 __a :  Optional[Any]						=  datasets['''train'''].unique('''label'''  )

 __a :  Union[str, Any]						=  {v: i for i, v in enumerate(lowerCAmelCase__  )}

 def tokenize_function(lowerCAmelCase__							:				Any  ):
  # max_length=None => use the model max length (it's actually the default)
  __a :  Dict						=  tokenizer(
      examples['''sentence1''']  ,   examples['''sentence2''']  ,   truncation=lowerCAmelCase__  ,   max_length=lowerCAmelCase__  ,   padding='''max_length'''  )
  if "label" in examples:
   __a :  Union[str, Any]						=  [label_to_id[l] for l in examples['''label''']]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __a :  Tuple						=  datasets.map(
     lowerCAmelCase__  ,   batched=lowerCAmelCase__  ,   remove_columns=['''sentence1''', '''sentence2''', '''label''']  ,   )

 def collate_fn(lowerCAmelCase__							:				List[str]  ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(lowerCAmelCase__  ,   padding='''max_length'''  ,   max_length=1_2_8  ,   return_tensors='''pt'''  )
  return tokenizer.pad(lowerCAmelCase__  ,   padding='''longest'''  ,   return_tensors='''pt'''  )

 # Instantiate dataloaders.
 __a :  Union[str, Any]						=  DataLoader(tokenized_datasets['''train''']  ,   shuffle=lowerCAmelCase__  ,   collate_fn=lowerCAmelCase__  ,   batch_size=2  )
 __a :  Any						=  DataLoader(tokenized_datasets['''validation''']  ,   shuffle=lowerCAmelCase__  ,   collate_fn=lowerCAmelCase__  ,   batch_size=1  )

 return train_dataloader, eval_dataloader


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


lowercase__       =['text', 'image', 'audio']




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Optional[int]						=  []

 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((5_1_2, 5_1_2)  )  )
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0  )  )
  elif isinstance(lowerCAmelCase__  ,   lowerCAmelCase__  ):
   inputs.append(create_inputs(lowerCAmelCase__  )  )
  else:
   raise ValueError(f"Invalid type requested: {input_type}"  )

 return inputs




def    __UpperCamelCase     (				lowerCAmelCase__							:				List  ):
 __a :  List[str]						=  []

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

 return output_types



@is_tool_test
class 		UpperCamelCase__  :
   def       lowerCAmelCase			(self :					Any   ):
    self.assertTrue(hasattr(self.tool					,				'''inputs'''   )   )
    self.assertTrue(hasattr(self.tool					,				'''outputs'''   )   )

    __a :  Any						=  self.tool.inputs
    for _input in inputs:
     if isinstance(_input					,				snake_case_   ):
      for __input in _input:
       self.assertTrue(__input in authorized_types   )
     else:
      self.assertTrue(_input in authorized_types   )

    __a :  Optional[int]						=  self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  Union[str, Any]						=  create_inputs(self.tool.inputs   )
    __a :  List[Any]						=  self.tool(*snake_case_   )

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

    self.assertListEqual(output_types(snake_case_   )					,				self.tool.outputs   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    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 :					Any   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )
    __a :  Union[str, Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Tuple						=  [outputs]

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

    for output, output_type in zip(snake_case_					,				self.tool.outputs   ):
     __a :  List[Any]						=  AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(snake_case_					,				snake_case_   )   )







   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  create_inputs(self.tool.inputs   )

    __a :  Dict						=  []

    for _input, input_type in zip(snake_case_					,				self.tool.inputs   ):
     if isinstance(snake_case_					,				snake_case_   ):
      _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
    __a :  Optional[Any]						=  self.tool(*snake_case_   )

    if not isinstance(snake_case_					,				snake_case_   ):
     __a :  Dict						=  [outputs]

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