
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       Any				,       _snake_case		:       Optional[Any]				,       _snake_case		:       List[Any]=13				,       _snake_case		:       int=3				,       _snake_case		:       str=True				,       _snake_case		:       str=True				,       _snake_case		:       Any=0.1				,       _snake_case		:       Union[str, Any]=0.1				,       _snake_case		:       Union[str, Any]=224				,       _snake_case		:       Union[str, Any]=1000				,       _snake_case		:       Optional[int]=[3, 3, 6, 4]				,       _snake_case		:       Tuple=[48, 56, 112, 220]				,       ):
          __lowercase		:			Union[str, Any]	    =       parent
          __lowercase		:			str	    =       batch_size
          __lowercase		:			Dict	    =       num_channels
          __lowercase		:			List[Any]	    =       is_training
          __lowercase		:			int	    =       use_labels
          __lowercase		:			Tuple	    =       hidden_dropout_prob
          __lowercase		:			str	    =       attention_probs_dropout_prob
          __lowercase		:			Dict	    =       num_labels
          __lowercase		:			Union[str, Any]	    =       image_size
          __lowercase		:			Union[str, Any]	    =       layer_depths
          __lowercase		:			List[Any]	    =       embed_dims




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			int	    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

          __lowercase		:			Any	    =       None
          if self.use_labels:
              __lowercase		:			int	    =       ids_tensor([self.batch_size]				,       self.num_labels				)

          __lowercase		:			Tuple	    =       self.get_config()

          return config, pixel_values, labels




      def 		snake_case_			(      self		:       Tuple				):
          return SwiftFormerConfig(
              depths=self.layer_depths				,       embed_dims=self.embed_dims				,       mlp_ratio=4				,       downsamples=[True, True, True, True]				,       hidden_act='''gelu'''				,       num_labels=self.num_labels				,       down_patch_size=3				,       down_stride=2				,       down_pad=1				,       drop_rate=0.0				,       drop_path_rate=0.0				,       use_layer_scale=_UpperCAmelCase				,       layer_scale_init_value=1E-5				,       )




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Tuple				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       List[Any]				):
          __lowercase		:			List[Any]	    =       SwiftFormerModel(config=_UpperCAmelCase				)
          model.to(_UpperCAmelCase				)
          model.eval()
          __lowercase		:			List[str]	    =       model(_UpperCAmelCase				)
          self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.embed_dims[-1], 7, 7)				)




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       Optional[Any]				,       _snake_case		:       str				,       _snake_case		:       Optional[Any]				):
          __lowercase		:			Union[str, Any]	    =       self.num_labels
          __lowercase		:			Dict	    =       SwiftFormerForImageClassification(_UpperCAmelCase				)
          model.to(_UpperCAmelCase				)
          model.eval()
          __lowercase		:			str	    =       model(_UpperCAmelCase				,       labels=_UpperCAmelCase				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)				)

          __lowercase		:			Optional[int]	    =       SwiftFormerForImageClassification(_UpperCAmelCase				)
          model.to(_UpperCAmelCase				)
          model.eval()

          __lowercase		:			List[Any]	    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
          __lowercase		:			List[str]	    =       model(_UpperCAmelCase				)
          self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)				)




      def 		snake_case_			(      self		:       List[str]				):
          (__lowercase)		:			List[Any]	    =       self.prepare_config_and_inputs()
          __lowercase		:			int	    =       {'''pixel_values''': pixel_values}
          return config, inputs_dict






@require_torch
class 						__lowerCAmelCase	(	_UpperCamelCase , _UpperCamelCase , unittest.TestCase      ):



      """simple docstring"""







      A__	:			str        =    (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      A__	:			Union[str, Any]        =    (
          {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      A__	:			Union[str, Any]        =    False
      A__	:			Union[str, Any]        =    False
      A__	:			List[str]        =    False
      A__	:			Optional[Any]        =    False
      A__	:			Tuple        =    False




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       SwiftFormerModelTester(self				)
          __lowercase		:			List[Any]	    =       ConfigTester(
              self				,       config_class=_UpperCAmelCase				,       has_text_modality=_UpperCAmelCase				,       hidden_size=37				,       num_attention_heads=12				,       num_hidden_layers=12				,       )




      def 		snake_case_			(      self		:       str				):
          self.config_tester.run_common_tests()




      @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''				)
      def 		snake_case_			(      self		:       List[Any]				):
          pass




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Dict	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase		:			List[str]	    =       model_class(_UpperCAmelCase				)
              __lowercase		:			int	    =       model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_UpperCAmelCase				,       nn.Linear				)				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			str	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase		:			int	    =       model_class(_UpperCAmelCase				)
              __lowercase		:			Optional[Any]	    =       inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowercase		:			Union[str, Any]	    =       [*signature.parameters.keys()]

              __lowercase		:			Union[str, Any]	    =       ['''pixel_values''']
              self.assertListEqual(arg_names[:1]				,       _UpperCAmelCase				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Optional[Any]	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Dict	    =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase				)




      @slow
      def 		snake_case_			(      self		:       Dict				):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase		:			List[Any]	    =       SwiftFormerModel.from_pretrained(_UpperCAmelCase				)
              self.assertIsNotNone(_UpperCAmelCase				)




      @unittest.skip(reason='''SwiftFormer does not output attentions'''				)
      def 		snake_case_			(      self		:       List[Any]				):
          pass




      def 		snake_case_			(      self		:       List[Any]				):
          def check_hidden_states_output(_snake_case		:       str				,       _snake_case		:       int				,       _snake_case		:       Tuple				):
              __lowercase		:			Optional[Any]	    =       model_class(_UpperCAmelCase				)
              model.to(_UpperCAmelCase				)
              model.eval()

              with torch.no_grad():
                  __lowercase		:			List[Any]	    =       model(**self._prepare_for_class(_UpperCAmelCase				,       _UpperCAmelCase				)				)

              __lowercase		:			List[Any]	    =       outputs.hidden_states

              __lowercase		:			Any	    =       8
              self.assertEqual(len(_UpperCAmelCase				)				,       _UpperCAmelCase				)  # TODO

              # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
              # with the width and height being successively divided by 2, after every 2 blocks
              for i in range(len(_UpperCAmelCase				)				):
                  self.assertEqual(
                      hidden_states[i].shape				,       torch.Size(
                          [
                              self.model_tester.batch_size,
                              self.model_tester.embed_dims[i // 2],
                              (self.model_tester.image_size // 4) // 2 ** (i // 2),
                              (self.model_tester.image_size // 4) // 2 ** (i // 2),
                          ]				)				,       )

          __lowercase		:			List[Any]	    =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase		:			Tuple	    =       True
              check_hidden_states_output(_UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase				)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              __lowercase		:			Dict	    =       True

              check_hidden_states_output(_UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase				)




      def 		snake_case_			(      self		:       List[Any]				):
          def _config_zero_init(_snake_case		:       Tuple				):
              __lowercase		:			Any	    =       copy.deepcopy(_UpperCAmelCase				)
              for key in configs_no_init.__dict__.keys():
                  if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                      setattr(_UpperCAmelCase				,       _UpperCAmelCase				,       1E-1_0				)
                  if isinstance(getattr(_UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase				)				,       _UpperCAmelCase				):
                      __lowercase		:			Optional[int]	    =       _config_zero_init(getattr(_UpperCAmelCase				,       _UpperCAmelCase				)				)
                      setattr(_UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase				)
              return configs_no_init

          __lowercase		:			Union[str, Any]	    =       self.model_tester.prepare_config_and_inputs_for_common()

          __lowercase		:			Tuple	    =       _config_zero_init(_UpperCAmelCase				)
          for model_class in self.all_model_classes:
              __lowercase		:			List[str]	    =       model_class(config=_UpperCAmelCase				)
              for name, param in model.named_parameters():
                  if param.requires_grad:
                      self.assertIn(
                          ((param.data.mean() * 1E9) / 1E9).round().item()				,       [0.0, 1.0]				,       msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,       )




      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
      def 		snake_case_			(      self		:       List[Any]				):
          pass






def     UpperCAmelCase_  (						) ->						Union[str, Any]:
    __lowercase		:			Optional[Any]	    =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image





@require_torch
@require_vision
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @cached_property
      def 		snake_case_			(      self		:       int				):
          return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''				) if is_vision_available() else None




      @slow
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''				).to(_UpperCAmelCase				)

          __lowercase		:			List[Any]	    =       self.default_image_processor
          __lowercase		:			List[Any]	    =       prepare_img()
          __lowercase		:			Optional[Any]	    =       image_processor(images=_UpperCAmelCase				,       return_tensors='''pt'''				).to(_UpperCAmelCase				)

          # forward pass
          with torch.no_grad():
              __lowercase		:			Optional[Any]	    =       model(**_UpperCAmelCase				)

          # verify the logits
          __lowercase		:			Any	    =       torch.Size((1, 1000)				)
          self.assertEqual(outputs.logits.shape				,       _UpperCAmelCase				)

          __lowercase		:			str	    =       torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]				).to(_UpperCAmelCase				)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]				,       _UpperCAmelCase				,       atol=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


