


import inspect
import unittest

from transformers import BitConfig
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_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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image


class   a__	:

							def __init__(   self      :     int      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     int=3      ,							lowerCamelCase_      :     Dict=3_2      ,							lowerCamelCase_      :     Dict=3      ,							lowerCamelCase_      :     str=1_0      ,							lowerCamelCase_      :     Any=[8, 1_6, 3_2, 6_4]      ,							lowerCamelCase_      :     int=[1, 1, 2, 1]      ,							lowerCamelCase_      :     Union[str, Any]=True      ,							lowerCamelCase_      :     Union[str, Any]=True      ,							lowerCamelCase_      :     Tuple="relu"      ,							lowerCamelCase_      :     List[str]=3      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     Optional[int]=["stage2", "stage3", "stage4"]      ,							lowerCamelCase_      :     int=[2, 3, 4]      ,							lowerCamelCase_      :     Tuple=1      ,							):
												a_    :     Optional[int]										=    parent
												a_    :     List[Any]										=    batch_size
												a_    :     str										=    image_size
												a_    :     List[Any]										=    num_channels
												a_    :     Union[str, Any]										=    embeddings_size
												a_    :     str										=    hidden_sizes
												a_    :     str										=    depths
												a_    :     Any										=    is_training
												a_    :     int										=    use_labels
												a_    :     List[Any]										=    hidden_act
												a_    :     Any										=    num_labels
												a_    :     Optional[Any]										=    scope
												a_    :     Optional[Any]										=    len(lowerCamelCase_    )
												a_    :     Optional[Any]										=    out_features
												a_    :     Dict										=    out_indices
												a_    :     List[str]										=    num_groups

							def 						UpperCAmelCase(   self      :     List[Any]    ):
												a_    :     List[Any]										=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

												a_    :     Dict										=    None
												if self.use_labels:
																	a_    :     Optional[Any]										=    ids_tensor([self.batch_size]      ,							self.num_labels    )

												a_    :     Union[str, Any]										=    self.get_config()

												return config, pixel_values, labels

							def 						UpperCAmelCase(   self      :     Tuple    ):
												return BitConfig(
												    num_channels=self.num_channels      ,							embeddings_size=self.embeddings_size      ,							hidden_sizes=self.hidden_sizes      ,							depths=self.depths      ,							hidden_act=self.hidden_act      ,							num_labels=self.num_labels      ,							out_features=self.out_features      ,							out_indices=self.out_indices      ,							num_groups=self.num_groups      ,							)

							def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Any    ):
												a_    :     Union[str, Any]										=    BitModel(config=lowerCamelCase_    )
												model.to(lowerCamelCase_    )
												model.eval()
												a_    :     Dict										=    model(lowerCamelCase_    )
												self.parent.assertEqual(
												    result.last_hidden_state.shape      ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)      ,							)

							def 						UpperCAmelCase(   self      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     List[Any]    ):
												a_    :     Dict										=    self.num_labels
												a_    :     Dict										=    BitForImageClassification(lowerCamelCase_    )
												model.to(lowerCamelCase_    )
												model.eval()
												a_    :     List[Any]										=    model(lowerCamelCase_      ,							labels=lowerCamelCase_    )
												self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)    )

							def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Tuple    ):
												a_    :     Dict										=    BitBackbone(config=lowerCamelCase_    )
												model.to(lowerCamelCase_    )
												model.eval()
												a_    :     List[str]										=    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    )      ,							[self.batch_size, self.hidden_sizes[1], 4, 4]    )

												# verify channels
												self.parent.assertEqual(len(model.channels    )      ,							len(config.out_features    )    )
												self.parent.assertListEqual(model.channels      ,							config.hidden_sizes[1:]    )

												# verify backbone works with out_features=None
												a_    :     List[Any]										=    None
												a_    :     Union[str, Any]										=    BitBackbone(config=lowerCamelCase_    )
												model.to(lowerCamelCase_    )
												model.eval()
												a_    :     str										=    model(lowerCamelCase_    )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps    )      ,							1    )
												self.parent.assertListEqual(list(result.feature_maps[0].shape    )      ,							[self.batch_size, self.hidden_sizes[-1], 1, 1]    )

												# verify channels
												self.parent.assertEqual(len(model.channels    )      ,							1    )
												self.parent.assertListEqual(model.channels      ,							[config.hidden_sizes[-1]]    )

							def 						UpperCAmelCase(   self      :     str    ):
												a_    :     Dict										=    self.prepare_config_and_inputs()
												a_    :     Any										=    config_and_inputs
												a_    :     Dict										=    {'''pixel_values''': pixel_values}
												return config, inputs_dict


@require_torch
class   a__	(						__A						,       __A						,       unittest.TestCase    ):
							lowerCamelCase__:     Dict							    =     (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
							lowerCamelCase__:     str							    =     (
							    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
							    if is_torch_available()
							    else {}
							)

							lowerCamelCase__:     List[Any]							    =     False
							lowerCamelCase__:     str							    =     False
							lowerCamelCase__:     str							    =     False
							lowerCamelCase__:     Any							    =     False
							lowerCamelCase__:     Union[str, Any]							    =     False

							def 						UpperCAmelCase(   self      :     List[str]    ):
												a_    :     Dict										=    BitModelTester(self    )
												a_    :     List[Any]										=    ConfigTester(self      ,							config_class=lowerCamelCase_      ,							has_text_modality=lowerCamelCase_    )

							def 						UpperCAmelCase(   self      :     str    ):
												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 						UpperCAmelCase(   self      :     Optional[int]    ):
												return

							@unittest.skip(reason="""Bit does not output attentions"""    )
							def 						UpperCAmelCase(   self      :     int    ):
												pass

							@unittest.skip(reason="""Bit does not use inputs_embeds"""    )
							def 						UpperCAmelCase(   self      :     List[str]    ):
												pass

							@unittest.skip(reason="""Bit does not support input and output embeddings"""    )
							def 						UpperCAmelCase(   self      :     Tuple    ):
												pass

							def 						UpperCAmelCase(   self      :     str    ):
												a_    :     str										=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a_    :     Optional[int]										=    model_class(lowerCamelCase_    )
																	a_    :     Any										=    inspect.signature(model.forward    )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	a_    :     Tuple										=    [*signature.parameters.keys()]

																	a_    :     Optional[int]										=    ['''pixel_values''']
																	self.assertListEqual(arg_names[:1]      ,							lowerCamelCase_    )

							def 						UpperCAmelCase(   self      :     Any    ):
												a_    :     str										=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*lowerCamelCase_    )

							def 						UpperCAmelCase(   self      :     List[Any]    ):
												a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*lowerCamelCase_    )

							def 						UpperCAmelCase(   self      :     Optional[Any]    ):
												a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a_    :     Optional[Any]										=    model_class(config=lowerCamelCase_    )
																	for name, module in model.named_modules():
																						if isinstance(lowerCamelCase_      ,							(nn.BatchNormad, nn.GroupNorm)    ):
																											self.assertTrue(
																											    torch.all(module.weight == 1    )      ,							msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,							)
																											self.assertTrue(
																											    torch.all(module.bias == 0    )      ,							msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''      ,							)

							def 						UpperCAmelCase(   self      :     Optional[Any]    ):

												def check_hidden_states_output(lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Optional[int]    ):
																	a_    :     Dict										=    model_class(lowerCamelCase_    )
																	model.to(lowerCamelCase_    )
																	model.eval()

																	with torch.no_grad():
																						a_    :     Tuple										=    model(**self._prepare_for_class(lowerCamelCase_      ,							lowerCamelCase_    )    )

																	a_    :     List[Any]										=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	a_    :     Optional[Any]										=    self.model_tester.num_stages
																	self.assertEqual(len(lowerCamelCase_    )      ,							expected_num_stages + 1    )

																	# Bit's feature maps are of shape (batch_size, num_channels, height, width)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]    )      ,							[self.model_tester.image_size // 4, self.model_tester.image_size // 4]      ,							)

												a_    :     Tuple										=    self.model_tester.prepare_config_and_inputs_for_common()
												a_    :     str										=    ['''preactivation''', '''bottleneck''']
												for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																						a_    :     Union[str, Any]										=    layer_type
																						a_    :     List[Any]										=    True
																						check_hidden_states_output(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

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

																						check_hidden_states_output(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

							@unittest.skip(reason="""Bit does not use feedforward chunking"""    )
							def 						UpperCAmelCase(   self      :     Optional[Any]    ):
												pass

							def 						UpperCAmelCase(   self      :     Any    ):
												a_    :     Any										=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_    )

							@slow
							def 						UpperCAmelCase(   self      :     Any    ):

												for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	a_    :     Union[str, Any]										=    BitModel.from_pretrained(lowerCamelCase_    )
																	self.assertIsNotNone(lowerCamelCase_    )



def  _a			(						):
					a_    :     Dict										=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
					return image


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

							@cached_property
							def 						UpperCAmelCase(   self      :     Optional[Any]    ):
												return (
												    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ) if is_vision_available() else None
												)

							@slow
							def 						UpperCAmelCase(   self      :     int    ):
												a_    :     Tuple										=    BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(lowerCamelCase_    )

												a_    :     Union[str, Any]										=    self.default_image_processor
												a_    :     int										=    prepare_img()
												a_    :     Union[str, Any]										=    image_processor(images=lowerCamelCase_      ,							return_tensors="""pt"""    ).to(lowerCamelCase_    )

												# forward pass
												with torch.no_grad():
																	a_    :     Tuple										=    model(**lowerCamelCase_    )

												# verify the logits
												a_    :     Optional[Any]										=    torch.Size((1, 1_0_0_0)    )
												self.assertEqual(outputs.logits.shape      ,							lowerCamelCase_    )

												a_    :     List[Any]										=    torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]    ).to(lowerCamelCase_    )

												self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,							lowerCamelCase_      ,							atol=1E-4    )    )


@require_torch
class   a__	(						__A						,       unittest.TestCase    ):
							lowerCamelCase__:     Tuple							    =     (BitBackbone,) if is_torch_available() else ()
							lowerCamelCase__:     int							    =     BitConfig

							lowerCamelCase__:     List[str]							    =     False

							def 						UpperCAmelCase(   self      :     int    ):
												a_    :     Tuple										=    BitModelTester(self    )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase        =     {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PegasusXForConditionalGeneration''',
           '''PegasusXModel''',
           '''PegasusXPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_pegasus_x import (
                  PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PegasusXForConditionalGeneration,
                  PegasusXModel,
                  PegasusXPreTrainedModel,
              )


else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



