






'''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      ):




		'''simple docstring'''





		def       UpperCamelCase_       (   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       UpperCamelCase_       (   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       UpperCamelCase_       (   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       UpperCamelCase_       (   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       UpperCamelCase_       (   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=0.001							)
								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




"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Union[str, Any]    =						'beit'






       def __init__(   self			,					snake_case=8_192			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.0			,					snake_case=0.0			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=224			,					snake_case=16			,					snake_case=3			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=True			,					snake_case=[3, 5, 7, 11]			,					snake_case=[1, 2, 3, 6]			,					snake_case=True			,					snake_case=0.4			,					snake_case=256			,					snake_case=1			,					snake_case=False			,					snake_case=255			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Union[str, Any] 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				str 			=				num_hidden_layers
        lowerCAmelCase__   :				Any 			=				num_attention_heads
        lowerCAmelCase__   :				List[str] 			=				intermediate_size
        lowerCAmelCase__   :				int 			=				hidden_act
        lowerCAmelCase__   :				Optional[Any] 			=				hidden_dropout_prob
        lowerCAmelCase__   :				str 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				Optional[int] 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps

        lowerCAmelCase__   :				int 			=				image_size
        lowerCAmelCase__   :				Union[str, Any] 			=				patch_size
        lowerCAmelCase__   :				Dict 			=				num_channels
        lowerCAmelCase__   :				Optional[Any] 			=				use_mask_token
        lowerCAmelCase__   :				Dict 			=				use_absolute_position_embeddings
        lowerCAmelCase__   :				Any 			=				use_relative_position_bias
        lowerCAmelCase__   :				List[Any] 			=				use_shared_relative_position_bias
        lowerCAmelCase__   :				Dict 			=				layer_scale_init_value
        lowerCAmelCase__   :				Optional[int] 			=				drop_path_rate
        lowerCAmelCase__   :				Optional[Any] 			=				use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowerCAmelCase__   :				Optional[int] 			=				out_indices
        lowerCAmelCase__   :				List[Any] 			=				pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowerCAmelCase__   :				List[Any] 			=				use_auxiliary_head
        lowerCAmelCase__   :				Optional[int] 			=				auxiliary_loss_weight
        lowerCAmelCase__   :				List[str] 			=				auxiliary_channels
        lowerCAmelCase__   :				Optional[Any] 			=				auxiliary_num_convs
        lowerCAmelCase__   :				Union[str, Any] 			=				auxiliary_concat_input
        lowerCAmelCase__   :				List[str] 			=				semantic_loss_ignore_index






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[str]    =						version.parse('1.11'       )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]   )






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return 1e-4






