
"""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 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




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

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

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

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




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

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

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

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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

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

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

											self.assertEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     AutoTokenizer.from_pretrained("""distilgpt2"""					)
											SCREAMING_SNAKE_CASE__		: List[Any]     =     AutoModelForCausalLM.from_pretrained("""distilgpt2"""					).to(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     -1

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

											# 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__		: str     =     tokenizer(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					)
											self.assertEqual(streamer_text_tokenized.input_ids.shape     , (1, 1)					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




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

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

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






"""simple docstring"""




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput



class 			lowerCAmelCase_		(nn.Module						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__ = 16     , SCREAMING_SNAKE_CASE__ = 88     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = 1     , SCREAMING_SNAKE_CASE__ = 0.0     , SCREAMING_SNAKE_CASE__ = 32     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = "geglu"     , SCREAMING_SNAKE_CASE__ = None     , )      ->	Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     nn.ModuleList(
											    [
											        TransformeraDModel(
											            num_attention_heads=SCREAMING_SNAKE_CASE__     , attention_head_dim=SCREAMING_SNAKE_CASE__     , in_channels=SCREAMING_SNAKE_CASE__     , num_layers=SCREAMING_SNAKE_CASE__     , dropout=SCREAMING_SNAKE_CASE__     , norm_num_groups=SCREAMING_SNAKE_CASE__     , cross_attention_dim=SCREAMING_SNAKE_CASE__     , attention_bias=SCREAMING_SNAKE_CASE__     , sample_size=SCREAMING_SNAKE_CASE__     , num_vector_embeds=SCREAMING_SNAKE_CASE__     , activation_fn=SCREAMING_SNAKE_CASE__     , num_embeds_ada_norm=SCREAMING_SNAKE_CASE__     , )
											        for _ in range(2					)
											    ]					)

											# Variables that can be set by a pipeline:

											# The ratio of transformer1 to transformer2's output states to be combined during inference
											SCREAMING_SNAKE_CASE__		: Tuple     =     0.5

											# The shape of `encoder_hidden_states` is expected to be
											# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [77, 2_57]

											# Which transformer to use to encode which condition.
											# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
											SCREAMING_SNAKE_CASE__		: int     =     [1, 0]



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     hidden_states

											SCREAMING_SNAKE_CASE__		: Tuple     =     []
											SCREAMING_SNAKE_CASE__		: List[str]     =     0
											# attention_mask is not used yet
											for i in range(2					):
																		# for each of the two transformers, pass the corresponding condition tokens
																		SCREAMING_SNAKE_CASE__		: str     =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																		SCREAMING_SNAKE_CASE__		: Any     =     self.transformer_index_for_condition[i]
																		SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.transformers[transformer_index](
																		    SCREAMING_SNAKE_CASE__     , encoder_hidden_states=SCREAMING_SNAKE_CASE__     , timestep=SCREAMING_SNAKE_CASE__     , cross_attention_kwargs=SCREAMING_SNAKE_CASE__     , return_dict=SCREAMING_SNAKE_CASE__     , )[0]
																		encoded_states.append(encoded_state - input_states					)
																		tokens_start += self.condition_lengths[i]

											SCREAMING_SNAKE_CASE__		: Tuple     =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     output_states + input_states

											if not return_dict:
																		return (output_states,)

											return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE__					)




