"""simple docstring"""







_a  :  Any=				"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       bytes						)							->    bytes:

	'''simple docstring'''





	if not isinstance(UpperCAmelCase_    ,							UpperCAmelCase_						):
		__snake_case    :							int   	=				F"a bytes-like object is required, not '{data.__class__.__name__}'"
		raise TypeError(UpperCAmelCase_						)

	__snake_case    :							Any   	=				''.join(bin(UpperCAmelCase_						)[2:].zfill(8						) for byte in data						)

	__snake_case    :							Tuple   	=				len(UpperCAmelCase_						) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		__snake_case    :							List[str]   	=				B'=' * ((6 - len(UpperCAmelCase_						) % 6) // 2)

		# Append binary_stream with arbitrary binary digits (0's by default) to make its
		# length a multiple of 6.
		binary_stream += "0" * (6 - len(UpperCAmelCase_						) % 6)
	else:
		__snake_case    :							Optional[int]   	=				B''

	# Encode every 6 binary digits to their corresponding Base64 character
	return (
	    "".join(
	        B64_CHARSET[int(binary_stream[index : index + 6]    ,							2						)]
	        for index in range(0    ,							len(UpperCAmelCase_						)    ,							6						)						).encode()
	    + padding
	)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    bytes:

	'''simple docstring'''





	if not isinstance(UpperCAmelCase_    ,							UpperCAmelCase_						) and not isinstance(UpperCAmelCase_    ,							UpperCAmelCase_						):
		__snake_case    :							List[str]   	=				(
		    'argument should be a bytes-like object or ASCII string, '
		    F"not '{encoded_data.__class__.__name__}'"
		)
		raise TypeError(UpperCAmelCase_						)

	# In case encoded_data is a bytes-like object, make sure it contains only
	# ASCII characters so we convert it to a string object
	if isinstance(UpperCAmelCase_    ,							UpperCAmelCase_						):
		try:
			__snake_case    :							Optional[int]   	=				encoded_data.decode('utf-8'						)
		except UnicodeDecodeError:
			raise ValueError('base64 encoded data should only contain ASCII characters'						)

	__snake_case    :							Dict   	=				encoded_data.count('='						)

	# Check if the encoded string contains non base64 characters
	if padding:
		assert all(
		    char in B64_CHARSET for char in encoded_data[:-padding]						), "Invalid base64 character(s) found."
	else:
		assert all(
		    char in B64_CHARSET for char in encoded_data						), "Invalid base64 character(s) found."

	# Check the padding
	assert len(UpperCAmelCase_						) % 4 == 0 and padding < 3, "Incorrect padding"

	if padding:
		# Remove padding if there is one
		__snake_case    :							int   	=				encoded_data[:-padding]

		__snake_case    :							Union[str, Any]   	=				''.join(
		    bin(B64_CHARSET.index(UpperCAmelCase_						)						)[2:].zfill(6						) for char in encoded_data						)[: -padding * 2]
	else:
		__snake_case    :							Optional[int]   	=				''.join(
		    bin(B64_CHARSET.index(UpperCAmelCase_						)						)[2:].zfill(6						) for char in encoded_data						)

	__snake_case    :							Optional[int]   	=				[
	    int(binary_stream[index : index + 8]    ,							2						)
	    for index in range(0    ,							len(UpperCAmelCase_						)    ,							8						)
	]

	return bytes(UpperCAmelCase_						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






"""simple docstring"""







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 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

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

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

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Any   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							List[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Optional[int]   	=				-1

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

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

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Any   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

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

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

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   List[str])					->			Dict:
					# 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
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('distilgpt2')
					__snake_case    :							Dict   	=				AutoModelForCausalLM.from_pretrained('distilgpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[str]   	=				torch.ones((1, 5)			,						device=_A).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
						__snake_case    :							Tuple   	=				TextStreamer(_A			,						skip_special_tokens=_A)
						model.generate(_A			,						max_new_tokens=1			,						do_sample=_A			,						streamer=_A)

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






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[int]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[int]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							int   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							List[Any]   	=				TextIteratorStreamer(_A			,						timeout=0.001)
					__snake_case    :							str   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							str   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()

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





