

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			):







				"""simple docstring"""





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





								'''simple docstring'''







								A__   				=       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'		)
								A__   				=       AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'		).to(lowercase_		)
								A__   				=       -1

								A__   				=       ids_tensor((1, 5),vocab_size=model.config.vocab_size		).to(lowercase_		)
								A__   				=       model.generate(lowercase_,max_new_tokens=1_0,do_sample=lowercase_		)
								A__   				=       tokenizer.decode(greedy_ids[0]		)

								with CaptureStdout() as cs:
												A__   				=       TextStreamer(lowercase_		)
												model.generate(lowercase_,max_new_tokens=1_0,do_sample=lowercase_,streamer=lowercase_		)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								A__   				=       cs.out[:-1]

								self.assertEqual(lowercase_,lowercase_		)


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





								'''simple docstring'''







								A__   				=       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'		)
								A__   				=       AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'		).to(lowercase_		)
								A__   				=       -1

								A__   				=       ids_tensor((1, 5),vocab_size=model.config.vocab_size		).to(lowercase_		)
								A__   				=       model.generate(lowercase_,max_new_tokens=1_0,do_sample=lowercase_		)
								A__   				=       tokenizer.decode(greedy_ids[0]		)

								A__   				=       TextIteratorStreamer(lowercase_		)
								A__   				=       {'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
								A__   				=       Thread(target=model.generate,kwargs=lowercase_		)
								thread.start()
								A__   				=       ''
								for new_text in streamer:
												streamer_text += new_text

								self.assertEqual(lowercase_,lowercase_		)


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





								'''simple docstring'''







								A__   				=       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'		)
								A__   				=       AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'		).to(lowercase_		)
								A__   				=       -1

								A__   				=       ids_tensor((1, 5),vocab_size=model.config.vocab_size		).to(lowercase_		)
								A__   				=       model.generate(lowercase_,max_new_tokens=1_0,do_sample=lowercase_		)
								A__   				=       greedy_ids[:, input_ids.shape[1] :]
								A__   				=       tokenizer.decode(new_greedy_ids[0]		)

								with CaptureStdout() as cs:
												A__   				=       TextStreamer(lowercase_,skip_prompt=lowercase_		)
												model.generate(lowercase_,max_new_tokens=1_0,do_sample=lowercase_,streamer=lowercase_		)
								# The greedy text should be printed to stdout, except for the final "\n" in the streamer
								A__   				=       cs.out[:-1]

								self.assertEqual(lowercase_,lowercase_		)


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





								'''simple docstring'''







								A__   				=       AutoTokenizer.from_pretrained('distilgpt2'		)
								A__   				=       AutoModelForCausalLM.from_pretrained('distilgpt2'		).to(lowercase_		)
								A__   				=       -1

								A__   				=       torch.ones((1, 5),device=lowercase_		).long() * model.config.bos_token_id
								with CaptureStdout() as cs:
												A__   				=       TextStreamer(lowercase_,skip_special_tokens=lowercase_		)
												model.generate(lowercase_,max_new_tokens=1,do_sample=lowercase_,streamer=lowercase_		)

								# 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
								A__   				=       cs.out[:-1]  # Remove the final "\n"
								A__   				=       tokenizer(lowercase_,return_tensors='pt'		)
								self.assertEqual(streamer_text_tokenized.input_ids.shape,(1, 1)		)


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





								'''simple docstring'''







								A__   				=       AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'		)
								A__   				=       AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'		).to(lowercase_		)
								A__   				=       -1

								A__   				=       ids_tensor((1, 5),vocab_size=model.config.vocab_size		).to(lowercase_		)
								A__   				=       TextIteratorStreamer(lowercase_,timeout=0.001		)
								A__   				=       {'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
								A__   				=       Thread(target=model.generate,kwargs=lowercase_		)
								thread.start()

								# The streamer will timeout after 0.001 seconds, so an exception will be raised
								with self.assertRaises(lowercase_		):
												A__   				=       ''
												for new_text in streamer:
																streamer_text += new_text




import numpy as np

from transformers import Pipeline
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				int:





    '''simple docstring'''






    A__   				=       np.max(SCREAMING_SNAKE_CASE__   ,      axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)
    A__   				=       np.exp(outputs - maxes				)
    return shifted_exp / shifted_exp.sum(axis=-1   ,      keepdims=SCREAMING_SNAKE_CASE__				)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Dict,**lowercase_			:	Tuple		)->   Tuple:





        '''simple docstring'''







        A__   				=       {}
        if "second_text" in kwargs:
            A__   				=       kwargs['second_text']
        return preprocess_kwargs, {}, {}


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	int,lowercase_			:	Optional[int]=None		)->   List[str]:





        '''simple docstring'''







        return self.tokenizer(lowercase_,text_pair=lowercase_,return_tensors=self.framework		)


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        return self.model(**lowercase_		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[int]		)->   Dict:





        '''simple docstring'''







        A__   				=       model_outputs.logits[0].numpy()
        A__   				=       softmax(lowercase_		)

        A__   				=       np.argmax(lowercase_		)
        A__   				=       self.model.config.idalabel[best_class]
        A__   				=       probabilities[best_class].item()
        A__   				=       logits.tolist()
        return {"label": label, "score": score, "logits": logits}

