
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       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
				__magic_name__		:		Optional[Any]	   =     AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_a		)
				__magic_name__		:		str	   =     -1

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

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

				self.assertEqual(_a						,   _a		)







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

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

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

				self.assertEqual(_a						,   _a		)







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

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

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

				self.assertEqual(_a						,   _a		)







			def 			SCREAMING_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
				__magic_name__		:		Tuple	   =     AutoTokenizer.from_pretrained("distilgpt2"		)
				__magic_name__		:		List[Any]	   =     AutoModelForCausalLM.from_pretrained("distilgpt2"		).to(_a		)
				__magic_name__		:		List[str]	   =     -1

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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"		)
				__magic_name__		:		Optional[int]	   =     AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"		).to(_a		)
				__magic_name__		:		Optional[Any]	   =     -1

				__magic_name__		:		int	   =     ids_tensor((1, 5)						,   vocab_size=model.config.vocab_size		).to(_a		)
				__magic_name__		:		List[str]	   =     TextIteratorStreamer(_a						,   timeout=0.0_01		)
				__magic_name__		:		Optional[int]	   =     {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
				__magic_name__		:		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		):
					__magic_name__		:		str	   =     ""
					for new_text in streamer:
						streamer_text += new_text


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'convbert'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=1						,   _a=0						,   _a=2						,   _a=768						,   _a=2						,   _a=9						,   _a=1						,   _a=None						,   **_a						,   ):
				super().__init__(
				    pad_token_id=_a						,   bos_token_id=_a						,   eos_token_id=_a						,   **_a						,   )

				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     hidden_size
				__magic_name__		:		Union[str, Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		str	   =     intermediate_size
				__magic_name__		:		Any	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     max_position_embeddings
				__magic_name__		:		str	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     initializer_range
				__magic_name__		:		Tuple	   =     layer_norm_eps
				__magic_name__		:		List[Any]	   =     embedding_size
				__magic_name__		:		List[Any]	   =     head_ratio
				__magic_name__		:		str	   =     conv_kernel_size
				__magic_name__		:		Dict	   =     num_groups
				__magic_name__		:		str	   =     classifier_dropout







class       _snake_case					(     snake_case				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		Dict	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Dict	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)
