from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.swin.modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
      )


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__					,   lowerCAmelCase__=13					,   lowerCAmelCase__=32					,   lowerCAmelCase__=2					,   lowerCAmelCase__=3					,   lowerCAmelCase__=16					,   lowerCAmelCase__=[1, 2, 1]					,   lowerCAmelCase__=[2, 2, 4]					,   lowerCAmelCase__=2					,   lowerCAmelCase__=2.0					,   lowerCAmelCase__=True					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=0.1					,   lowerCAmelCase__="gelu"					,   lowerCAmelCase__=False					,   lowerCAmelCase__=True					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=1E-5					,   lowerCAmelCase__=True					,   lowerCAmelCase__=None					,   lowerCAmelCase__=True					,   lowerCAmelCase__=10					,   lowerCAmelCase__=8					,   ):
            _A=						parent
            _A=						batch_size
            _A=						image_size
            _A=						patch_size
            _A=						num_channels
            _A=						embed_dim
            _A=						depths
            _A=						num_heads
            _A=						window_size
            _A=						mlp_ratio
            _A=						qkv_bias
            _A=						hidden_dropout_prob
            _A=						attention_probs_dropout_prob
            _A=						drop_path_rate
            _A=						hidden_act
            _A=						use_absolute_embeddings
            _A=						patch_norm
            _A=						layer_norm_eps
            _A=						initializer_range
            _A=						is_training
            _A=						scope
            _A=						use_labels
            _A=						type_sequence_label_size
            _A=						encoder_stride



     def       a__	(       self							):
            _A=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

            _A=						None
            if self.use_labels:
                   _A=						ids_tensor([self.batch_size]					,   self.type_sequence_label_size							)

            _A=						self.get_config()

            return config, pixel_values, labels



     def       a__	(       self							):
            return SwinConfig(
                image_size=self.image_size					,   patch_size=self.patch_size					,   num_channels=self.num_channels					,   embed_dim=self.embed_dim					,   depths=self.depths					,   num_heads=self.num_heads					,   window_size=self.window_size					,   mlp_ratio=self.mlp_ratio					,   qkv_bias=self.qkv_bias					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   drop_path_rate=self.drop_path_rate					,   hidden_act=self.hidden_act					,   use_absolute_embeddings=self.use_absolute_embeddings					,   path_norm=self.patch_norm					,   layer_norm_eps=self.layer_norm_eps					,   initializer_range=self.initializer_range					,   encoder_stride=self.encoder_stride					,   )



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A=						TFSwinModel(config=lowerCAmelCase__							)
            _A=						model(lowerCAmelCase__							)

            _A=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
            _A=						int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

            self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, expected_seq_len, expected_dim)							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A=						TFSwinForMaskedImageModeling(config=lowerCAmelCase__							)
            _A=						model(lowerCAmelCase__							)
            self.parent.assertEqual(
                result.logits.shape					,   (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

            # test greyscale images
            _A=						1
            _A=						TFSwinForMaskedImageModeling(lowerCAmelCase__							)

            _A=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
            _A=						model(lowerCAmelCase__							)
            self.parent.assertEqual(result.logits.shape					,   (self.batch_size, 1, self.image_size, self.image_size)							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A=						self.type_sequence_label_size
            _A=						TFSwinForImageClassification(lowerCAmelCase__							)
            _A=						model(lowerCAmelCase__					,   labels=lowerCAmelCase__							)
            self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)							)

            # test greyscale images
            _A=						1
            _A=						TFSwinForImageClassification(lowerCAmelCase__							)
            _A=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
            _A=						model(lowerCAmelCase__							)
            self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)							)





     def       a__	(       self							):
            _A=						self.prepare_config_and_inputs()
            _A,      _A,      _A=						config_and_inputs
            _A=						{'pixel_values': pixel_values}
            return config, inputs_dict





@require_tf
class   lowerCAmelCase    (       _a      ,		_a      ,		unittest.TestCase      ):
     _SCREAMING_SNAKE_CASE  :  str		=(
         (
             TFSwinModel,
             TFSwinForImageClassification,
             TFSwinForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE  :  str		=(
         {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
         if is_tf_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE  :  Dict		=False
     _SCREAMING_SNAKE_CASE  :  Any		=False
     _SCREAMING_SNAKE_CASE  :  Any		=False
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		=False



     def       a__	(       self							):
            _A=						TFSwinModelTester(self							)
            _A=						ConfigTester(self					,   config_class=lowerCAmelCase__					,   embed_dim=37							)



     def       a__	(       self							):
            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()



     def       a__	(       self							):
            return



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__							)



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__							)



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)



     @unittest.skip(reason='Swin does not use inputs_embeds'							)
     def       a__	(       self							):
            pass



     def       a__	(       self							):
            _A,      _A=						self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A=						model_class(lowerCAmelCase__							)
                   self.assertIsInstance(model.get_input_embeddings()					,   tf.keras.layers.Layer							)
                   _A=						model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(lowerCAmelCase__					,   tf.keras.layers.Dense							)							)



     def       a__	(       self							):
            _A,      _A=						self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _A=						model_class(lowerCAmelCase__							)
                   _A=						inspect.signature(model.call							)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   _A=						[*signature.parameters.keys()]

                   _A=						['pixel_values']
                   self.assertListEqual(arg_names[:1]					,   lowerCAmelCase__							)



     def       a__	(       self							):
            _A,      _A=						self.model_tester.prepare_config_and_inputs_for_common()
            _A=						True

            for model_class in self.all_model_classes:
                   _A=						True
                   _A=						False
                   _A=						True
                   _A=						model_class(lowerCAmelCase__							)
                   _A=						model(**self._prepare_for_class(lowerCAmelCase__					,   lowerCAmelCase__							)							)
                   _A=						outputs.attentions
                   _A=						len(self.model_tester.depths							)
                   self.assertEqual(len(lowerCAmelCase__							)					,   lowerCAmelCase__							)

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   _A=						True
                   _A=						config.window_size**2
                   _A=						model_class(lowerCAmelCase__							)
                   _A=						model(**self._prepare_for_class(lowerCAmelCase__					,   lowerCAmelCase__							)							)
                   _A=						outputs.attentions
                   self.assertEqual(len(lowerCAmelCase__							)					,   lowerCAmelCase__							)

                   self.assertListEqual(
                       list(attentions[0].shape[-3:]							)					,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,   )
                   _A=						len(lowerCAmelCase__							)

                   # Check attention is always last and order is fine
                   _A=						True
                   _A=						True
                   _A=						model_class(lowerCAmelCase__							)
                   _A=						model(**self._prepare_for_class(lowerCAmelCase__					,   lowerCAmelCase__							)							)

                   if hasattr(self.model_tester					,   'num_hidden_states_types'							):
                          _A=						self.model_tester.num_hidden_states_types
                   else:
                          # also another +1 for reshaped_hidden_states
                          _A=						2
                   self.assertEqual(out_len + added_hidden_states					,   len(lowerCAmelCase__							)							)

                   _A=						outputs.attentions

                   self.assertEqual(len(lowerCAmelCase__							)					,   lowerCAmelCase__							)

                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]							)					,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,   )



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A=						model_class(lowerCAmelCase__							)
            _A=						model(**self._prepare_for_class(lowerCAmelCase__					,   lowerCAmelCase__							)							)
            _A=						outputs.hidden_states

            _A=						getattr(
                self.model_tester					,   'expected_num_hidden_layers'					,   len(self.model_tester.depths							) + 1							)
            self.assertEqual(len(lowerCAmelCase__							)					,   lowerCAmelCase__							)

            # Swin has a different seq_length
            _A=						to_atuple(config.patch_size							)

            _A=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]							)					,   [num_patches, self.model_tester.embed_dim]					,   )

            _A=						outputs.reshaped_hidden_states
            self.assertEqual(len(lowerCAmelCase__							)					,   lowerCAmelCase__							)

            _A,      _A,      _A,      _A=						reshaped_hidden_states[0].shape

            _A=						tf.reshape(reshaped_hidden_states[0]					,   (batch_size, num_channels, height * width)							)
            _A=						tf.transpose(lowerCAmelCase__					,   (0, 2, 1)							)

            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]							)					,   [num_patches, self.model_tester.embed_dim]					,   )



     def       a__	(       self							):
            _A,      _A=						self.model_tester.prepare_config_and_inputs_for_common()

            _A=						to_atuple(self.model_tester.image_size							)

            for model_class in self.all_model_classes:
                   _A=						True
                   self.check_hidden_states_output(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _A=						True

                   self.check_hidden_states_output(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							)



     def       a__	(       self							):
            _A,      _A=						self.model_tester.prepare_config_and_inputs_for_common()
            _A=						3

            _A=						to_atuple(self.model_tester.image_size							)
            _A=						to_atuple(config.patch_size							)

            _A=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            _A=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                   _A=						True
                   self.check_hidden_states_output(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   (padded_height, padded_width)							)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   _A=						True
                   self.check_hidden_states_output(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   (padded_height, padded_width)							)





     @slow
     def       a__	(       self							):
            for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=						TFSwinModel.from_pretrained(lowerCAmelCase__							)
                   self.assertIsNotNone(lowerCAmelCase__							)





@require_vision
@require_tf
class   lowerCAmelCase    (       unittest.TestCase      ):



     @cached_property
     def       a__	(       self							):
            return (
                AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
                if is_vision_available()
                else None
            )





     @slow
     def       a__	(       self							):
            _A=						TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
            _A=						self.default_image_processor

            _A=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
            _A=						image_processor(images=lowerCAmelCase__					,   return_tensors='tf'							)

            # forward pass
            _A=						model(lowerCAmelCase__							)

            # verify the logits
            _A=						tf.TensorShape((1, 1000)							)
            self.assertEqual(outputs.logits.shape					,   lowerCAmelCase__							)
            _A=						tf.constant([-0.0948, -0.6454, -0.0921]							)
            self.assertTrue(np.allclose(outputs.logits[0, :3]					,   lowerCAmelCase__					,   atol=1E-4							)							)
import heapq

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Dict:




       '''simple docstring'''






       _A=						[(0, start)]  # cost from start node,end node
       _A=						set()
       while heap:
              ((_A),      (_A))=						heapq.heappop(lowerCAmelCase_ )
              if u in visited:
                     continue
              visited.add(lowerCAmelCase_ )
              if u == end:
                     return cost
              for v, c in graph[u]:
                     if v in visited:
                            continue
                     _A=						cost + c
                     heapq.heappush(lowerCAmelCase_							,       (next_item, v) )
       return -1


UpperCAmelCase_    		=				{
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

UpperCAmelCase_    		=				dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()