

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 		__A	(   self							:				Union[str, Any]	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

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

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

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

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				List[str]	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

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

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Optional[Any]	):
            # 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
            A_						  =				AutoTokenizer.from_pretrained("distilgpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("distilgpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

            # 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(UpperCAmelCase						,			return_tensors="pt"	)
            self.assertEqual(streamer_text_tokenized.input_ids.shape						,			(1, 1)	)







      def 		__A	(   self							:				int	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

            A_						  =				ids_tensor((1, 5)						,			vocab_size=model.config.vocab_size	).to(UpperCAmelCase	)
            A_						  =				TextIteratorStreamer(UpperCAmelCase						,			timeout=0.001	)
            A_						  =				{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
            A_						  =				Thread(target=model.generate						,			kwargs=UpperCAmelCase	)
            thread.start()

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



'''simple docstring'''
import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   lowercase      (				A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							LxmertTokenizer
		_a       					=							LxmertTokenizerFast
		_a       					=							True
		_a       					=							True




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().setUp()

							UpperCamelCase__  :Any				   =					[
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							UpperCamelCase__  :str				   =					os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['''vocab_file''']				)
							with open(self.vocab_file       ,  '''w'''       ,  encoding='''utf-8'''				) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					'''UNwant\u00E9d,running'''
							UpperCamelCase__  :Union[str, Any]				   =					'''unwanted, running'''
							return input_text, output_text




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					self.tokenizer_class(self.vocab_file				)

							UpperCamelCase__  :List[Any]				   =					tokenizer.tokenize('''UNwant\u00E9d,running'''				)
							self.assertListEqual(UpperCamelCase_       ,  ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']				)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)       ,  [7, 4, 5, 10, 8, 9]				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if not self.test_rust_tokenizer:
												return

							UpperCamelCase__  :str				   =					self.get_tokenizer()
							UpperCamelCase__  :Union[str, Any]				   =					self.get_rust_tokenizer()

							UpperCamelCase__  :int				   =					'''I was born in 92000, and this is falsé.'''

							UpperCamelCase__  :Optional[Any]				   =					tokenizer.tokenize(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					rust_tokenizer.tokenize(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							UpperCamelCase__  :Optional[Any]				   =					tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
							UpperCamelCase__  :int				   =					rust_tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					self.get_rust_tokenizer()
							UpperCamelCase__  :Any				   =					tokenizer.encode(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					rust_tokenizer.encode(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)