



import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers import AutoModelForCausalLM
@require_torch
class 					a	(  unittest.TestCase       ):



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
     SCREAMING_SNAKE_CASE_: List[str]     			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=-1

     SCREAMING_SNAKE_CASE_: List[str]     			=ids_tensor((1, 5)       ,		vocab_size=model.config.vocab_size		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=model.generate(lowerCAmelCase       ,		max_new_tokens=10       ,		do_sample=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=tokenizer.decode(greedy_ids[0]		)

     with CaptureStdout() as cs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=TextStreamer(lowerCAmelCase		)
        model.generate(lowerCAmelCase       ,		max_new_tokens=10       ,		do_sample=lowerCAmelCase       ,		streamer=lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     SCREAMING_SNAKE_CASE_: Optional[int]     			=cs.out[:-1]

     self.assertEqual(lowerCAmelCase       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
     SCREAMING_SNAKE_CASE_: Dict     			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=-1

     SCREAMING_SNAKE_CASE_: str     			=ids_tensor((1, 5)       ,		vocab_size=model.config.vocab_size		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=model.generate(lowerCAmelCase       ,		max_new_tokens=10       ,		do_sample=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=tokenizer.decode(greedy_ids[0]		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=TextIteratorStreamer(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			={"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
     SCREAMING_SNAKE_CASE_: Dict     			=Thread(target=model.generate       ,		kwargs=lowerCAmelCase		)
     thread.start()
     SCREAMING_SNAKE_CASE_: Dict     			=""""""
     for new_text in streamer:
        streamer_text += new_text

     self.assertEqual(lowerCAmelCase       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					str		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=-1

     SCREAMING_SNAKE_CASE_: str     			=ids_tensor((1, 5)       ,		vocab_size=model.config.vocab_size		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model.generate(lowerCAmelCase       ,		max_new_tokens=10       ,		do_sample=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=greedy_ids[:, input_ids.shape[1] :]
     SCREAMING_SNAKE_CASE_: Any     			=tokenizer.decode(new_greedy_ids[0]		)

     with CaptureStdout() as cs:
        SCREAMING_SNAKE_CASE_: Tuple     			=TextStreamer(lowerCAmelCase       ,		skip_prompt=lowerCAmelCase		)
        model.generate(lowerCAmelCase       ,		max_new_tokens=10       ,		do_sample=lowerCAmelCase       ,		streamer=lowerCAmelCase		)
     # The greedy text should be printed to stdout, except for the final "\n" in the streamer
     SCREAMING_SNAKE_CASE_: Any     			=cs.out[:-1]

     self.assertEqual(lowerCAmelCase       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Any		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=AutoTokenizer.from_pretrained("""distilgpt2"""		)
     SCREAMING_SNAKE_CASE_: int     			=AutoModelForCausalLM.from_pretrained("""distilgpt2"""		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=-1

     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.ones((1, 5)       ,		device=lowerCAmelCase		).long() * model.config.bos_token_id
     with CaptureStdout() as cs:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=TextStreamer(lowerCAmelCase       ,		skip_special_tokens=lowerCAmelCase		)
        model.generate(lowerCAmelCase       ,		max_new_tokens=1       ,		do_sample=lowerCAmelCase       ,		streamer=lowerCAmelCase		)

     # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
     # re-tokenized, must only contain one token
     SCREAMING_SNAKE_CASE_: int     			=cs.out[:-1]  # Remove the final "\n"
     SCREAMING_SNAKE_CASE_: List[str]     			=tokenizer(lowerCAmelCase       ,		return_tensors="""pt"""		)
     self.assertEqual(streamer_text_tokenized.input_ids.shape       ,		(1, 1)		)





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		)
     SCREAMING_SNAKE_CASE_: str     			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=-1

     SCREAMING_SNAKE_CASE_: List[Any]     			=ids_tensor((1, 5)       ,		vocab_size=model.config.vocab_size		).to(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=TextIteratorStreamer(lowerCAmelCase       ,		timeout=0.0_0_1		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			={"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
     SCREAMING_SNAKE_CASE_: List[str]     			=Thread(target=model.generate       ,		kwargs=lowerCAmelCase		)
     thread.start()

     # The streamer will timeout after 0.001 seconds, so an exception will be raised
     with self.assertRaises(lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Tuple     			=""""""
        for new_text in streamer:
           streamer_text += new_text








"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 					a	:



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



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


     '''simple docstring'''




     return self.length





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


     '''simple docstring'''




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







class 					a	(  torch.nn.Module       ):



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


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=False
     return x * self.a + self.b







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

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

   SCREAMING_SNAKE_CASE_: List[Any]     			={v: i for i, v in enumerate(lowercase )}

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

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

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=2 )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=1 )

   return train_dataloader, eval_dataloader



