
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase					:			int            =		logging.get_logger(__name__)

__lowerCAmelCase					:			int            =		{
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}


class 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			str        =    '''swin'''

      A__	:			int        =    {
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }




      def __init__(      self		:       List[Any]				,       _snake_case		:       int=224				,       _snake_case		:       Union[str, Any]=4				,       _snake_case		:       str=3				,       _snake_case		:       List[Any]=96				,       _snake_case		:       Tuple=[2, 2, 6, 2]				,       _snake_case		:       int=[3, 6, 12, 24]				,       _snake_case		:       Dict=7				,       _snake_case		:       Optional[int]=4.0				,       _snake_case		:       Dict=True				,       _snake_case		:       Optional[int]=0.0				,       _snake_case		:       Optional[Any]=0.0				,       _snake_case		:       Optional[int]=0.1				,       _snake_case		:       str="gelu"				,       _snake_case		:       Any=False				,       _snake_case		:       Dict=0.02				,       _snake_case		:       Optional[int]=1E-5				,       _snake_case		:       List[Any]=32				,       _snake_case		:       List[Any]=None				,       _snake_case		:       Tuple=None				,       **_snake_case		:       Tuple				,       ):
          super().__init__(**_snake_case				)

          __lowercase		:			Tuple	    =       image_size
          __lowercase		:			List[str]	    =       patch_size
          __lowercase		:			Tuple	    =       num_channels
          __lowercase		:			Union[str, Any]	    =       embed_dim
          __lowercase		:			str	    =       depths
          __lowercase		:			List[Any]	    =       len(_snake_case				)
          __lowercase		:			int	    =       num_heads
          __lowercase		:			List[str]	    =       window_size
          __lowercase		:			str	    =       mlp_ratio
          __lowercase		:			Optional[Any]	    =       qkv_bias
          __lowercase		:			Optional[Any]	    =       hidden_dropout_prob
          __lowercase		:			str	    =       attention_probs_dropout_prob
          __lowercase		:			Optional[int]	    =       drop_path_rate
          __lowercase		:			List[Any]	    =       hidden_act
          __lowercase		:			Tuple	    =       use_absolute_embeddings
          __lowercase		:			List[str]	    =       layer_norm_eps
          __lowercase		:			Union[str, Any]	    =       initializer_range
          __lowercase		:			Tuple	    =       encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          __lowercase		:			List[str]	    =       int(embed_dim * 2 ** (len(_snake_case				) - 1)				)
          __lowercase		:			Union[str, Any]	    =       ['''stem'''] + [F'stage{idx}' for idx in range(1				,       len(_snake_case				) + 1				)]
          __lowercase		:			Tuple	    =       get_aligned_output_features_output_indices(
              out_features=_snake_case				,       out_indices=_snake_case				,       stage_names=self.stage_names				)






class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







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




      @property
      def 		snake_case_			(      self		:       int				):
          return OrderedDict(
              [
                  ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ]				)




      @property
      def 		snake_case_			(      self		:       int				):
          return 1E-4




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 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Any	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Optional[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[int]	    =       tokenizer.decode(greedy_ids[0]				)

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Optional[Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

          __lowercase		:			Tuple	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(greedy_ids[0]				)

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			int	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       -1

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

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       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
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''distilgpt2'''				)
          __lowercase		:			List[str]	    =       AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(_snake_case				)
          __lowercase		:			str	    =       -1

          __lowercase		:			Dict	    =       torch.ones((1, 5)				,       device=_snake_case				).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				,       skip_special_tokens=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=1				,       do_sample=_snake_case				,       streamer=_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
          __lowercase		:			Dict	    =       cs.out[:-1]  # Remove the final "\n"
          __lowercase		:			Any	    =       tokenizer(_snake_case				,       return_tensors='''pt'''				)
          self.assertEqual(streamer_text_tokenized.input_ids.shape				,       (1, 1)				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Union[str, Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			Dict	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       TextIteratorStreamer(_snake_case				,       timeout=0.0_01				)
          __lowercase		:			Tuple	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[int]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()

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


