





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

from accelerate.utils.dataclasses import DistributedType



class        _SCREAMING_SNAKE_CASE						:







	'''simple docstring'''

	def __init__(self    :       Any	,			UpperCAmelCase_    :       Optional[int]=2	,			UpperCAmelCase_    :       str=3	,			UpperCAmelCase_    :       Optional[Any]=64	,			UpperCAmelCase_    :       Dict=None)	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				str        =np.random.default_rng(__UpperCAmelCase)
			lowerCamelCase__:				List[str]        =length
			lowerCamelCase__:				List[Any]        =rng.normal(size=(length,)).astype(np.floataa)
			lowerCamelCase__:				Union[str, Any]        =a * self.x + b + rng.normal(scale=0.1	,			size=(length,)).astype(np.floataa)

	def __len__(self    :       Dict)	->Dict:



			'''simple docstring'''



			return self.length







	def __getitem__(self    :       List[Any]	,			UpperCAmelCase_    :       str)	->List[str]:



			'''simple docstring'''



			return {"x": self.x[i], "y": self.y[i]}



class        _SCREAMING_SNAKE_CASE						(     torch.nn.Module   ):







	'''simple docstring'''

	def __init__(self    :       Tuple	,			UpperCAmelCase_    :       int=0	,			UpperCAmelCase_    :       str=0	,			UpperCAmelCase_    :       List[Any]=False)	->int:



			'''simple docstring'''



			super().__init__()
			lowerCamelCase__:				List[Any]        =torch.nn.Parameter(torch.tensor([2, 3]).float())
			lowerCamelCase__:				Optional[Any]        =torch.nn.Parameter(torch.tensor([2, 3]).float())
			lowerCamelCase__:				Any        =True







	def 							SCREAMING_SNAKE_CASE_ (self    :       str	,			UpperCAmelCase_    :       str=None)	->str:



			'''simple docstring'''



			if self.first_batch:
					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
					lowerCamelCase__:				Optional[int]        =False
			return x * self.a[0] + self.b[0]



class        _SCREAMING_SNAKE_CASE						(     torch.nn.Module   ):







	'''simple docstring'''

	def __init__(self    :       Dict	,			UpperCAmelCase_    :       List[Any]=0	,			UpperCAmelCase_    :       List[str]=0	,			UpperCAmelCase_    :       List[Any]=False)	->Optional[Any]:



			'''simple docstring'''



			super().__init__()
			lowerCamelCase__:				Tuple        =torch.nn.Parameter(torch.tensor(__UpperCAmelCase).float())
			lowerCamelCase__:				List[str]        =torch.nn.Parameter(torch.tensor(__UpperCAmelCase).float())
			lowerCamelCase__:				str        =True







	def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       Any=None)	->Tuple:



			'''simple docstring'''



			if self.first_batch:
					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
					lowerCamelCase__:				int        =False
			return x * self.a + self.b





def 					lowerCAmelCase_			(	__a     ,   __a = 16		)					->		int:







		"""simple docstring"""





		from datasets import load_dataset
		from transformers import AutoTokenizer

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

		lowerCamelCase__:				str        ={v: i for i, v in enumerate(lowerCAmelCase__		)}

		def tokenize_function(__a		):
				# max_length=None => use the model max length (it's actually the default)
				lowerCamelCase__:				List[Any]        =tokenizer(
				    examples["sentence1"]     ,   examples["sentence2"]     ,   truncation=lowerCAmelCase__     ,   max_length=lowerCAmelCase__     ,   padding="max_length"		)
				if "label" in examples:
						lowerCamelCase__:				Optional[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
		lowerCamelCase__:				Tuple        =datasets.map(
		    lowerCAmelCase__     ,   batched=lowerCAmelCase__     ,   remove_columns=["sentence1", "sentence2", "label"]     ,   )

		def collate_fn(__a		):
				# 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=128     ,   return_tensors="pt"		)
				return tokenizer.pad(lowerCAmelCase__     ,   padding="longest"     ,   return_tensors="pt"		)

		# Instantiate dataloaders.
		lowerCamelCase__:				Optional[Any]        =DataLoader(tokenized_datasets["train"]     ,   shuffle=lowerCAmelCase__     ,   collate_fn=lowerCAmelCase__     ,   batch_size=2		)
		lowerCamelCase__:				List[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


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





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =[]

  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(__a     ,   __a		):
      inputs.append(create_inputs(__a		)		)
    else:
      raise ValueError(F"""Invalid type requested: {input_type}"""		)

  return inputs





def 					lowerCAmelCase_			(	__a		)					->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

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

  return output_types






@is_tool_test
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



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

   lowerCamelCase__:				Tuple        =self.tool.inputs
   for _input in inputs:
     if isinstance(_input	,			UpperCAmelCase_):
       for __input in _input:
         self.assertTrue(__input in authorized_types)
     else:
       self.assertTrue(_input in authorized_types)

   lowerCamelCase__:				Optional[Any]        =self.tool.outputs
   for _output in outputs:
     self.assertTrue(_output in authorized_types)

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



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				str        =self.tool(*UpperCAmelCase_)

   # There is a single output
   if len(self.tool.outputs) == 1:
     lowerCamelCase__:				Optional[Any]        =[outputs]

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->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 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				str        =create_inputs(self.tool.inputs)
   lowerCamelCase__:				Dict        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				Tuple        =[outputs]

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

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =create_inputs(self.tool.inputs)

   lowerCamelCase__:				int        =[]

   for _input, input_type in zip(UpperCAmelCase_	,			self.tool.inputs):
     if isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
       _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
   lowerCamelCase__:				Union[str, Any]        =self.tool(*UpperCAmelCase_)

   if not isinstance(UpperCAmelCase_	,			UpperCAmelCase_):
     lowerCamelCase__:				str        =[outputs]

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

