"""simple docstring"""


import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 MaskFormerSwinBackbone
    from transformers.models.maskformer import MaskFormerSwinModel


class 							a					:


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	List[Any]=13 ,					__lowerCAmelCase						:	Union[str, Any]=32 ,					__lowerCAmelCase						:	str=2 ,					__lowerCAmelCase						:	Union[str, Any]=3 ,					__lowerCAmelCase						:	List[Any]=16 ,					__lowerCAmelCase						:	Dict=[1, 2, 1] ,					__lowerCAmelCase						:	List[str]=[2, 2, 4] ,					__lowerCAmelCase						:	str=2 ,					__lowerCAmelCase						:	List[Any]=2.0 ,					__lowerCAmelCase						:	str=True ,					__lowerCAmelCase						:	Tuple=0.0 ,					__lowerCAmelCase						:	List[str]=0.0 ,					__lowerCAmelCase						:	Tuple=0.1 ,					__lowerCAmelCase						:	Union[str, Any]="gelu" ,					__lowerCAmelCase						:	List[Any]=False ,					__lowerCAmelCase						:	Optional[int]=True ,					__lowerCAmelCase						:	int=0.02 ,					__lowerCAmelCase						:	Tuple=1e-5 ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	str=None ,					__lowerCAmelCase						:	Any=True ,					__lowerCAmelCase						:	Optional[int]=10 ,					__lowerCAmelCase						:	Optional[Any]=8 ,					__lowerCAmelCase						:	Union[str, Any]=["stage1", "stage2", "stage3"] ,					__lowerCAmelCase						:	List[str]=[1, 2, 3] ,					):
      _UpperCAmelCase    			=   parent
      _UpperCAmelCase    			=   batch_size
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   patch_size
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   embed_dim
      _UpperCAmelCase    			=   depths
      _UpperCAmelCase    			=   num_heads
      _UpperCAmelCase    			=   window_size
      _UpperCAmelCase    			=   mlp_ratio
      _UpperCAmelCase    			=   qkv_bias
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   drop_path_rate
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   use_absolute_embeddings
      _UpperCAmelCase    			=   patch_norm
      _UpperCAmelCase    			=   layer_norm_eps
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   is_training
      _UpperCAmelCase    			=   scope
      _UpperCAmelCase    			=   use_labels
      _UpperCAmelCase    			=   type_sequence_label_size
      _UpperCAmelCase    			=   encoder_stride
      _UpperCAmelCase    			=   out_features
      _UpperCAmelCase    			=   out_indices


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

      _UpperCAmelCase    			=   self.get_config()

      return config, pixel_values, labels


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      return MaskFormerSwinConfig(
          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 ,					out_features=self.out_features ,					out_indices=self.out_indices ,					)


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict       ):
      _UpperCAmelCase    			=   MaskFormerSwinModel(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )

      _UpperCAmelCase    			=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	int ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   MaskFormerSwinBackbone(config=__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()
      _UpperCAmelCase    			=   model(__lowerCAmelCase       )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps       ) ,					len(config.out_features       )       )
      self.parent.assertListEqual(list(result.feature_maps[0].shape       ) ,					[13, 16, 16, 16]       )

      # verify channels
      self.parent.assertEqual(len(model.channels       ) ,					len(config.out_features       )       )
      self.parent.assertListEqual(model.channels ,					[16, 32, 64]       )

      # verify ValueError
      with self.parent.assertRaises(__lowerCAmelCase       ):
        _UpperCAmelCase    			=   ["""stem"""]
        _UpperCAmelCase    			=   MaskFormerSwinBackbone(config=__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.prepare_config_and_inputs()
      _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   config_and_inputs
      _UpperCAmelCase    			=   {"""pixel_values""": pixel_values}
      return config, inputs_dict



@require_torch
class 							a					(		lowerCAmelCase_						,				lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	str  				=				(
        (
            MaskFormerSwinModel,
            MaskFormerSwinBackbone,
        )
        if is_torch_available()
        else ()
    )
    _snake_case  :	List[str]  				=				{'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
    _snake_case  :	Any  				=				False
    _snake_case  :	int  				=				False
    _snake_case  :	Optional[Any]  				=				False
    _snake_case  :	Optional[int]  				=				False
    _snake_case  :	Optional[Any]  				=				False


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   MaskFormerSwinModelTester(self       )
      _UpperCAmelCase    			=   ConfigTester(self ,					config_class=__lowerCAmelCase ,					embed_dim=37       )


    @require_torch_multi_gpu
    @unittest.skip(
        reason=(
            """`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
            """ `nn.DataParallel`"""
        )       )
    def   lowerCAmelCase_	(       self						:	str       ):
      pass


    def   lowerCAmelCase_	(       self						:	int       ):
      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   lowerCAmelCase_	(       self						:	Any       ):
      return


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__lowerCAmelCase       )


    @unittest.skip("""Swin does not use inputs_embeds"""       )
    def   lowerCAmelCase_	(       self						:	Dict       ):
      pass


    @unittest.skip("""Swin does not support feedforward chunking"""       )
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      pass


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   model_class(__lowerCAmelCase       )
        self.assertIsInstance(model.get_input_embeddings() ,					(nn.Module)       )
        _UpperCAmelCase    			=   model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__lowerCAmelCase ,					nn.Linear       )       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

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

        _UpperCAmelCase    			=   ["""pixel_values"""]
        self.assertListEqual(arg_names[:1] ,					__lowerCAmelCase       )


    @unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions"""       )
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      pass


    @unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone"""       )
    def   lowerCAmelCase_	(       self						:	str       ):
      pass


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Tuple       ):
      _UpperCAmelCase    			=   model_class(__lowerCAmelCase       )
      model.to(__lowerCAmelCase       )
      model.eval()

      with torch.no_grad():
        _UpperCAmelCase    			=   model(**self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase       )       )

      _UpperCAmelCase    			=   outputs.hidden_states

      _UpperCAmelCase    			=   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
      _UpperCAmelCase    			=   (
          config.patch_size
          if isinstance(config.patch_size ,					collections.abc.Iterable       )
          else (config.patch_size, config.patch_size)
      )

      _UpperCAmelCase    			=   (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] ,					)


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase    			=   (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size ,					collections.abc.Iterable       )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   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"]
        _UpperCAmelCase    			=   True

        self.check_hidden_states_output(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()
      _UpperCAmelCase    			=   3

      _UpperCAmelCase    			=   (
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size ,					collections.abc.Iterable       )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      _UpperCAmelCase    			=   (
          config.patch_size
          if isinstance(config.patch_size ,					collections.abc.Iterable       )
          else (config.patch_size, config.patch_size)
      )

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

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   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"]
        _UpperCAmelCase    			=   True
        self.check_hidden_states_output(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					(padded_height, padded_width)       )


    @unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints"""       )
    def   lowerCAmelCase_	(       self						:	str       ):
      pass


    @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""       )
    def   lowerCAmelCase_	(       self						:	int       ):
      pass


    @unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin"""       )
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      pass


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()

      def set_nan_tensor_to_zero(__lowerCAmelCase						:	Optional[Any]       ):
        _UpperCAmelCase    			=   0
        return t

      def check_equivalence(__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	int={}       ):
        with torch.no_grad():
          _UpperCAmelCase    			=   model(**__lowerCAmelCase ,					return_dict=__lowerCAmelCase ,					**__lowerCAmelCase       )
          _UpperCAmelCase    			=   model(**__lowerCAmelCase ,					return_dict=__lowerCAmelCase ,					**__lowerCAmelCase       ).to_tuple()

          def recursive_check(__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Optional[int]       ):
            if isinstance(__lowerCAmelCase ,					(List, Tuple)       ):
              for tuple_iterable_value, dict_iterable_value in zip(__lowerCAmelCase ,					__lowerCAmelCase       ):
                recursive_check(__lowerCAmelCase ,					__lowerCAmelCase       )
            elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
              for tuple_iterable_value, dict_iterable_value in zip(
                  tuple_object.values() ,					dict_object.values()       ):
                recursive_check(__lowerCAmelCase ,					__lowerCAmelCase       )
            elif tuple_object is None:
              return
            else:
              self.assertTrue(
                  torch.allclose(
                      set_nan_tensor_to_zero(__lowerCAmelCase       ) ,					set_nan_tensor_to_zero(__lowerCAmelCase       ) ,					atol=1e-5       ) ,					msg=(
                      """Tuple and dict output are not equal. Difference:"""
                      f''' {torch.max(torch.abs(tuple_object - dict_object       )       )}. Tuple has `nan`:'''
                      f''' {torch.isnan(__lowerCAmelCase       ).any()} and `inf`: {torch.isinf(__lowerCAmelCase       )}. Dict has'''
                      f''' `nan`: {torch.isnan(__lowerCAmelCase       ).any()} and `inf`: {torch.isinf(__lowerCAmelCase       )}.'''
                  ) ,					)

          recursive_check(__lowerCAmelCase ,					__lowerCAmelCase       )

      for model_class in self.all_model_classes:
        _UpperCAmelCase    			=   model_class(__lowerCAmelCase       )
        model.to(__lowerCAmelCase       )
        model.eval()

        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase       )
        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase       )
        check_equivalence(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase ,					return_labels=__lowerCAmelCase       )
        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase ,					return_labels=__lowerCAmelCase       )
        check_equivalence(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase       )

        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase       )
        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase       )
        check_equivalence(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					{"""output_hidden_states""": True}       )

        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase ,					return_labels=__lowerCAmelCase       )
        _UpperCAmelCase    			=   self._prepare_for_class(__lowerCAmelCase ,					__lowerCAmelCase ,					return_labels=__lowerCAmelCase       )
        check_equivalence(__lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase ,					{"""output_hidden_states""": True}       )



@require_torch
class 							a					(		unittest.TestCase						,				lowerCAmelCase_ ):
    _snake_case  :	List[Any]  				=				(MaskFormerSwinBackbone,) if is_torch_available() else ()
    _snake_case  :	List[str]  				=				MaskFormerSwinConfig


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   MaskFormerSwinModelTester(self       )


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.model_tester.prepare_config_and_inputs_for_common()
      _UpperCAmelCase    			=   inputs_dict["""pixel_values"""].shape[0]

      for backbone_class in self.all_model_classes:
        _UpperCAmelCase    			=   backbone_class(__lowerCAmelCase       )
        backbone.to(__lowerCAmelCase       )
        backbone.eval()

        _UpperCAmelCase    			=   backbone(**__lowerCAmelCase       )

        # Test default outputs and verify feature maps
        self.assertIsInstance(outputs.feature_maps ,					__lowerCAmelCase       )
        self.assertTrue(len(outputs.feature_maps       ) == len(backbone.channels       )       )
        for feature_map, n_channels in zip(outputs.feature_maps ,					backbone.channels       ):
          self.assertTrue(feature_map.shape[:2] ,					(batch_size, n_channels)       )
        self.assertIsNone(outputs.hidden_states       )
        self.assertIsNone(outputs.attentions       )

        # Test output_hidden_states=True
        _UpperCAmelCase    			=   backbone(**__lowerCAmelCase ,					output_hidden_states=__lowerCAmelCase       )
        self.assertIsNotNone(outputs.hidden_states       )
        self.assertTrue(len(outputs.hidden_states       ) ,					len(backbone.stage_names       )       )
        # We skip the stem layer
        for hidden_states, n_channels in zip(outputs.hidden_states[1:] ,					backbone.channels       ):
          for hidden_state in hidden_states:
            # Hidden states are in the format (batch_size, (height * width), n_channels)
            _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   hidden_state.shape
            self.assertTrue((h_batch_size, h_n_channels) ,					(batch_size, n_channels)       )

            # Test output_attentions=True
        if self.has_attentions:
          _UpperCAmelCase    			=   backbone(**__lowerCAmelCase ,					output_attentions=__lowerCAmelCase       )
          self.assertIsNotNone(outputs.attentions       )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__			=    {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPTBigCodeForSequenceClassification""",
        """GPTBigCodeForTokenClassification""",
        """GPTBigCodeForCausalLM""",
        """GPTBigCodeModel""",
        """GPTBigCodePreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_bigcode import (
            GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTBigCodeForCausalLM,
            GPTBigCodeForSequenceClassification,
            GPTBigCodeForTokenClassification,
            GPTBigCodeModel,
            GPTBigCodePreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
