





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 _snake_case    (						self							):
								lowerCamelCase			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
								lowerCamelCase			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(lowerCAmelCase__							)
								lowerCamelCase			=-1

								lowerCamelCase			=ids_tensor((1, 5)				,	vocab_size=model.config.vocab_size							).to(lowerCAmelCase__							)
								lowerCamelCase			=model.generate(lowerCAmelCase__				,	max_new_tokens=10				,	do_sample=lowerCAmelCase__							)
								lowerCamelCase			=tokenizer.decode(greedy_ids[0]							)

								with CaptureStdout() as cs:
												lowerCamelCase			=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
								lowerCamelCase			=cs.out[:-1]

								self.assertEqual(lowerCAmelCase__				,	lowerCAmelCase__							)


				def _snake_case    (						self							):
								lowerCamelCase			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
								lowerCamelCase			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(lowerCAmelCase__							)
								lowerCamelCase			=-1

								lowerCamelCase			=ids_tensor((1, 5)				,	vocab_size=model.config.vocab_size							).to(lowerCAmelCase__							)
								lowerCamelCase			=model.generate(lowerCAmelCase__				,	max_new_tokens=10				,	do_sample=lowerCAmelCase__							)
								lowerCamelCase			=tokenizer.decode(greedy_ids[0]							)

								lowerCamelCase			=TextIteratorStreamer(lowerCAmelCase__							)
								lowerCamelCase			={"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
								lowerCamelCase			=Thread(target=model.generate				,	kwargs=lowerCAmelCase__							)
								thread.start()
								lowerCamelCase			=""
								for new_text in streamer:
												streamer_text += new_text

								self.assertEqual(lowerCAmelCase__				,	lowerCAmelCase__							)


				def _snake_case    (						self							):
								lowerCamelCase			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
								lowerCamelCase			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(lowerCAmelCase__							)
								lowerCamelCase			=-1

								lowerCamelCase			=ids_tensor((1, 5)				,	vocab_size=model.config.vocab_size							).to(lowerCAmelCase__							)
								lowerCamelCase			=model.generate(lowerCAmelCase__				,	max_new_tokens=10				,	do_sample=lowerCAmelCase__							)
								lowerCamelCase			=greedy_ids[:, input_ids.shape[1] :]
								lowerCamelCase			=tokenizer.decode(new_greedy_ids[0]							)

								with CaptureStdout() as cs:
												lowerCamelCase			=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
								lowerCamelCase			=cs.out[:-1]

								self.assertEqual(lowerCAmelCase__				,	lowerCAmelCase__							)


				def _snake_case    (						self							):
								# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
								# with actual models -- the dummy models' tokenizers are not aligned with their models, and
								# `skip_special_tokens=True` has no effect on them
								lowerCamelCase			=AutoTokenizer.from_pretrained("""distilgpt2"""							)
								lowerCamelCase			=AutoModelForCausalLM.from_pretrained("""distilgpt2"""							).to(lowerCAmelCase__							)
								lowerCamelCase			=-1

								lowerCamelCase			=torch.ones((1, 5)				,	device=lowerCAmelCase__							).long() * model.config.bos_token_id
								with CaptureStdout() as cs:
												lowerCamelCase			=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
								lowerCamelCase			=cs.out[:-1]  # Remove the final "\n"
								lowerCamelCase			=tokenizer(lowerCAmelCase__				,	return_tensors="""pt"""							)
								self.assertEqual(streamer_text_tokenized.input_ids.shape				,	(1, 1)							)


				def _snake_case    (						self							):
								lowerCamelCase			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							)
								lowerCamelCase			=AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""							).to(lowerCAmelCase__							)
								lowerCamelCase			=-1

								lowerCamelCase			=ids_tensor((1, 5)				,	vocab_size=model.config.vocab_size							).to(lowerCAmelCase__							)
								lowerCamelCase			=TextIteratorStreamer(lowerCAmelCase__				,	timeout=0.0_0_1							)
								lowerCamelCase			={"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
								lowerCamelCase			=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__							):
												lowerCamelCase			=""
												for new_text in streamer:
																streamer_text += new_text












import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class  __A       (  a      ,   unittest.TestCase      ):
				__A								=			BioGptTokenizer
				__A								=			False


				def _snake_case    (						self							):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase			=[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								lowerCamelCase			=dict(zip(UpperCAmelCase_				,	range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase			=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""merges_file"""]							)
								with open(self.vocab_file				,	"""w"""							) as fp:
												fp.write(json.dumps(UpperCAmelCase_							)							)
								with open(self.merges_file				,	"""w"""							) as fp:
												fp.write("""\n""".join(UpperCAmelCase_							)							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""lower newer"""
								lowerCamelCase			="""lower newer"""
								return input_text, output_text


				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer(self.vocab_file				,	self.merges_file							)

								lowerCamelCase			="""lower"""
								lowerCamelCase			=["""low""", """er</w>"""]
								lowerCamelCase			=tokenizer.tokenize(UpperCAmelCase_							)
								self.assertListEqual(UpperCAmelCase_				,	UpperCAmelCase_							)

								lowerCamelCase			=tokens + ["""<unk>"""]
								lowerCamelCase			=[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)				,	UpperCAmelCase_							)


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer.from_pretrained("""microsoft/biogpt"""							)

								lowerCamelCase			=tokenizer.encode("""sequence builders"""				,	add_special_tokens=UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.encode("""multi-sequence build"""				,	add_special_tokens=UpperCAmelCase_							)

								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,	UpperCAmelCase_							)

								self.assertTrue(encoded_sentence == [2] + text							)
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)





