

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TFResNetForImageClassification, TFResNetModel
    from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[Any]=3     , __lowerCamelCase					:			Any=32     , __lowerCamelCase					:			Tuple=3     , __lowerCamelCase					:			Optional[int]=10     , __lowerCamelCase					:			int=[10, 20, 30, 40]     , __lowerCamelCase					:			str=[1, 1, 2, 1]     , __lowerCamelCase					:			str=True     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			List[Any]="relu"     , __lowerCamelCase					:			List[Any]=3     , __lowerCamelCase					:			Union[str, Any]=None     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   embeddings_size
       SCREAMING_SNAKE_CASE										=   hidden_sizes
       SCREAMING_SNAKE_CASE										=   depths
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   num_labels
       SCREAMING_SNAKE_CASE										=   scope
       SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			)





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       SCREAMING_SNAKE_CASE										=   None
       if self.use_labels:
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size]     , self.num_labels			)

       SCREAMING_SNAKE_CASE										=   self.get_config()

       return config, pixel_values, labels





   def      _snake_case		(       self					:			List[str]			):
       return ResNetConfig(
           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     , image_size=self.image_size     , )





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   TFResNetModel(config=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape     , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     , )





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.num_labels
       SCREAMING_SNAKE_CASE										=   TFResNetForImageClassification(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , labels=__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"pixel_values": pixel_values}
       return config, inputs_dict





@require_tf
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
   lowerCamelCase__				=      (
       {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
       if is_tf_available()
       else {}
   )

   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   TFResNetModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(self     , config_class=__lowerCamelCase     , has_text_modality=__lowerCamelCase			)





   def      _snake_case		(       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      _snake_case		(       self					:			List[str]			):
       return





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





   @unittest.skip(reason="ResNet does not support input and output embeddings"			)
   def      _snake_case		(       self					:			Any			):
       pass





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()

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

           SCREAMING_SNAKE_CASE										=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Any			):
       def check_hidden_states_output(__lowerCamelCase					:			Any     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Union[str, Any]			):
           SCREAMING_SNAKE_CASE										=   model_class(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   model(**self._prepare_for_class(__lowerCamelCase     , __lowerCamelCase			)			)

           SCREAMING_SNAKE_CASE										=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           SCREAMING_SNAKE_CASE										=   self.model_tester.num_stages
           self.assertEqual(len(__lowerCamelCase			)     , expected_num_stages + 1			)

           # ResNet'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]     , )

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE										=   ["basic", "bottleneck"]
       for model_class in self.all_model_classes:
           for layer_type in layers_type:
               SCREAMING_SNAKE_CASE										=   layer_type
               SCREAMING_SNAKE_CASE										=   True
               check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)

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

               check_hidden_states_output(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[int]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			int			):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE										=   TFResNetModel.from_pretrained(__lowerCamelCase			)
           self.assertIsNotNone(__lowerCamelCase			)





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image





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







   '''simple docstring'''




   @cached_property
   def      _snake_case		(       self					:			Optional[int]			):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )





   @slow
   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

       SCREAMING_SNAKE_CASE										=   self.default_image_processor
       SCREAMING_SNAKE_CASE										=   prepare_img()
       SCREAMING_SNAKE_CASE										=   image_processor(images=__lowerCamelCase     , return_tensors="tf"			)

       # forward pass
       SCREAMING_SNAKE_CASE										=   model(**__lowerCamelCase			)

       # verify the logits
       SCREAMING_SNAKE_CASE										=   tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape     , __lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   tf.constant([-11.1_069, -9.7_877, -8.3_777]			)

       self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()     , __lowerCamelCase     , atol=1e-4			)			)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)