import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
    )
    from transformers.models.efficientformer.modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
    )


if is_vision_available():
    from PIL import Image

    from transformers import EfficientFormerImageProcessor


class       lowerCamelCase_	:






    '''simple docstring'''






    def __init__(      self					,					__lowercase					,					__lowercase = 13					,					__lowercase = 64					,					__lowercase = 2					,					__lowercase = 3					,					__lowercase = 3					,					__lowercase = True					,					__lowercase = True					,					__lowercase = 128					,					__lowercase=[16, 32, 64, 128]					,					__lowercase = 7					,					__lowercase = 4					,					__lowercase = 37					,					__lowercase = "gelu"					,					__lowercase = 0.1					,					__lowercase = 0.1					,					__lowercase = 10					,					__lowercase = 0.02					,					__lowercase = 2					,					__lowercase = 1					,					__lowercase = 128					,					__lowercase = [2, 2, 2, 2]					,					__lowercase = 2					,					__lowercase = 2					,					)			->      int:
        __UpperCamelCase	:int         =						parent
        __UpperCamelCase	:Any         =						batch_size
        __UpperCamelCase	:Union[str, Any]         =						image_size
        __UpperCamelCase	:str         =						patch_size
        __UpperCamelCase	:List[Any]         =						num_channels
        __UpperCamelCase	:List[Any]         =						is_training
        __UpperCamelCase	:Optional[Any]         =						use_labels
        __UpperCamelCase	:Any         =						hidden_size
        __UpperCamelCase	:Tuple         =						num_hidden_layers
        __UpperCamelCase	:Dict         =						num_attention_heads
        __UpperCamelCase	:List[Any]         =						intermediate_size
        __UpperCamelCase	:int         =						hidden_act
        __UpperCamelCase	:Any         =						hidden_dropout_prob
        __UpperCamelCase	:Optional[Any]         =						attention_probs_dropout_prob
        __UpperCamelCase	:Dict         =						type_sequence_label_size
        __UpperCamelCase	:Dict         =						initializer_range
        __UpperCamelCase	:List[str]         =						encoder_stride
        __UpperCamelCase	:Any         =						num_attention_outputs
        __UpperCamelCase	:str         =						embed_dim
        __UpperCamelCase	:str         =						embed_dim + 1
        __UpperCamelCase	:Dict         =						resolution
        __UpperCamelCase	:Optional[Any]         =						depths
        __UpperCamelCase	:List[str]         =						hidden_sizes
        __UpperCamelCase	:str         =						dim
        __UpperCamelCase	:str         =						mlp_expansion_ratio







    def      UpperCamelCase__  (      self)			->      int:
        __UpperCamelCase	:List[Any]         =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        __UpperCamelCase	:List[Any]         =						None
        if self.use_labels:
            __UpperCamelCase	:List[Any]         =						ids_tensor([self.batch_size]					,					self.type_sequence_label_size)

        __UpperCamelCase	:Optional[Any]         =						self.get_config()

        return config, pixel_values, labels







    def      UpperCamelCase__  (      self)			->      Optional[int]:
        return EfficientFormerConfig(
            image_size=self.image_size					,					patch_size=self.patch_size					,					num_channels=self.num_channels					,					hidden_size=self.hidden_size					,					num_hidden_layers=self.num_hidden_layers					,					num_attention_heads=self.num_attention_heads					,					intermediate_size=self.intermediate_size					,					hidden_act=self.hidden_act					,					hidden_dropout_prob=self.hidden_dropout_prob					,					attention_probs_dropout_prob=self.attention_probs_dropout_prob					,					is_decoder=A_					,					initializer_range=self.initializer_range					,					encoder_stride=self.encoder_stride					,					resolution=self.resolution					,					depths=self.depths					,					hidden_sizes=self.hidden_sizes					,					dim=self.dim					,					mlp_expansion_ratio=self.mlp_expansion_ratio					,					)







    def      UpperCamelCase__  (      self					,					__lowercase					,					__lowercase					,					__lowercase)			->      int:
        __UpperCamelCase	:Optional[Any]         =						TFEfficientFormerModel(config=A_)
        __UpperCamelCase	:Any         =						model(A_					,					training=A_)
        self.parent.assertEqual(result.last_hidden_state.shape					,					(self.batch_size, self.seq_length, self.hidden_size))







    def      UpperCamelCase__  (      self					,					__lowercase					,					__lowercase					,					__lowercase)			->      Optional[Any]:
        __UpperCamelCase	:Dict         =						self.type_sequence_label_size
        __UpperCamelCase	:Optional[Any]         =						TFEfficientFormerForImageClassification(A_)
        __UpperCamelCase	:Union[str, Any]         =						model(A_					,					labels=A_					,					training=A_)
        self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.type_sequence_label_size))

        # test greyscale images
        __UpperCamelCase	:Optional[Any]         =						1
        __UpperCamelCase	:Any         =						TFEfficientFormerForImageClassification(A_)

        __UpperCamelCase	:List[str]         =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        __UpperCamelCase	:Any         =						model(A_					,					labels=A_)
        self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.type_sequence_label_size))







    def      UpperCamelCase__  (      self)			->      Union[str, Any]:
        __UpperCamelCase	:List[Any]         =						self.prepare_config_and_inputs()
        __UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase	:Optional[Any]         =						config_and_inputs
        __UpperCamelCase	:Optional[int]         =						{'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_tf
class       lowerCamelCase_	(  snake_case_		,      snake_case_		,      unittest.TestCase					):






    '''simple docstring'''






    a__      :		Any         = (
        (
            TFEfficientFormerModel,
            TFEfficientFormerForImageClassificationWithTeacher,
            TFEfficientFormerForImageClassification,
        )
        if is_tf_available()
        else ()
    )
    a__      :		Optional[int]         = (
        {
            """feature-extraction""": TFEfficientFormerModel,
            """image-classification""": (
                TFEfficientFormerForImageClassification,
                TFEfficientFormerForImageClassificationWithTeacher,
            ),
        }
        if is_tf_available()
        else {}
    )

    a__      :		Any         = False

    a__      :		Tuple         = False
    a__      :		List[Any]         = False
    a__      :		List[str]         = False
    a__      :		List[str]         = False







    def      UpperCamelCase__  (      self)			->      Any:
        __UpperCamelCase	:Optional[Any]         =						TFEfficientFormerModelTester(self)
        __UpperCamelCase	:Optional[int]         =						ConfigTester(
            self					,					config_class=A_					,					has_text_modality=A_					,					hidden_size=37)







    def      UpperCamelCase__  (      self)			->      Optional[Any]:
        self.config_tester.run_common_tests()







    @unittest.skip(reason='''EfficientFormer does not use inputs_embeds''')
    def      UpperCamelCase__  (      self)			->      List[Any]:
        pass







    @unittest.skip(reason='''EfficientFormer does not support input and output embeddings''')
    def      UpperCamelCase__  (      self)			->      int:
        pass







    def      UpperCamelCase__  (      self)			->      Optional[int]:
        __UpperCamelCase    ,			__UpperCamelCase	:Optional[int]         =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase	:List[Any]         =						model_class(A_)
            __UpperCamelCase	:Tuple         =						inspect.signature(model.call)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __UpperCamelCase	:List[str]         =						[*signature.parameters.keys()]

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







    def      UpperCamelCase__  (      self)			->      Optional[int]:






        def check_hidden_states_output(__lowercase					,					__lowercase					,					__lowercase):
            __UpperCamelCase	:Union[str, Any]         =						model_class(A_)

            __UpperCamelCase	:Any         =						model(**self._prepare_for_class(A_					,					A_)					,					training=A_)
            __UpperCamelCase	:Any         =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            __UpperCamelCase	:List[str]         =						getattr(
                self.model_tester					,					'''expected_num_hidden_layers'''					,					self.model_tester.num_hidden_layers + 1)
            self.assertEqual(len(A_)					,					A_)

            if hasattr(self.model_tester					,					'''encoder_seq_length'''):
                __UpperCamelCase	:Union[str, Any]         =						self.model_tester.encoder_seq_length
                if hasattr(self.model_tester					,					'''chunk_length''') and self.model_tester.chunk_length > 1:
                    __UpperCamelCase	:List[Any]         =						seq_length * self.model_tester.chunk_length
            else:
                __UpperCamelCase	:List[Any]         =						self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[-1].shape[-2:])					,					[seq_length, self.model_tester.hidden_size]					,					)

            if config.is_encoder_decoder:
                __UpperCamelCase	:Dict         =						outputs.decoder_hidden_states

                self.asseretIsInstance(A_					,					(list, tuple))
                self.assertEqual(len(A_)					,					A_)
                __UpperCamelCase	:str         =						getattr(self.model_tester					,					'''seq_length'''					,					A_)
                __UpperCamelCase	:int         =						getattr(self.model_tester					,					'''decoder_seq_length'''					,					A_)

                self.assertListEqual(
                    list(hidden_states[-1].shape[-2:])					,					[decoder_seq_length, self.model_tester.hidden_size]					,					)

        __UpperCamelCase    ,			__UpperCamelCase	:Any         =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase	:Optional[Any]         =						True
            check_hidden_states_output(A_					,					A_					,					A_)

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

            check_hidden_states_output(A_					,					A_					,					A_)







    def      UpperCamelCase__  (      self					,					__lowercase					,					__lowercase					,					__lowercase=False)			->      int:
        __UpperCamelCase	:List[str]         =						super()._prepare_for_class(A_					,					A_					,					return_labels=A_)

        if return_labels:
            if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
                del inputs_dict["labels"]

        return inputs_dict







    def      UpperCamelCase__  (      self)			->      Optional[int]:
        __UpperCamelCase	:Optional[Any]         =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A_)







    @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet''')
    def      UpperCamelCase__  (      self)			->      List[Any]:
        __UpperCamelCase	:Optional[Any]         =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*A_)







    def      UpperCamelCase__  (      self)			->      Optional[int]:
        __UpperCamelCase	:Optional[Any]         =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A_)







    @slow
    def      UpperCamelCase__  (      self)			->      Any:






        for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase	:Optional[int]         =						TFEfficientFormerModel.from_pretrained(A_)
            self.assertIsNotNone(A_)







    def      UpperCamelCase__  (      self)			->      List[Any]:
        __UpperCamelCase    ,			__UpperCamelCase	:List[str]         =						self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase	:int         =						True

        __UpperCamelCase	:List[Any]         =						getattr(self.model_tester					,					'''seq_length'''					,					A_)
        __UpperCamelCase	:Optional[int]         =						getattr(self.model_tester					,					'''encoder_seq_length'''					,					A_)
        __UpperCamelCase	:Optional[int]         =						getattr(self.model_tester					,					'''key_length'''					,					A_)
        __UpperCamelCase	:Optional[int]         =						getattr(self.model_tester					,					'''chunk_length'''					,					A_)

        if chunk_length is not None and hasattr(self.model_tester					,					'''num_hashes'''):
            __UpperCamelCase	:Dict         =						encoder_seq_length * self.model_tester.num_hashes

        for model_class in self.all_model_classes:
            __UpperCamelCase	:List[Any]         =						True
            __UpperCamelCase	:List[str]         =						False
            __UpperCamelCase	:int         =						True
            __UpperCamelCase	:List[str]         =						model_class(A_)

            __UpperCamelCase	:Optional[Any]         =						model(**self._prepare_for_class(A_					,					A_)					,					training=A_)
            __UpperCamelCase	:str         =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(A_)					,					self.model_tester.num_attention_outputs)

            # check that output_attentions also work using config
            del inputs_dict["output_attentions"]
            __UpperCamelCase	:str         =						True
            __UpperCamelCase	:str         =						model_class(A_)
            __UpperCamelCase	:Union[str, Any]         =						model(**self._prepare_for_class(A_					,					A_)					,					training=A_)

            __UpperCamelCase	:Union[str, Any]         =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
            self.assertEqual(len(A_)					,					self.model_tester.num_attention_outputs)

            if chunk_length is not None:
                self.assertListEqual(
                    list(attentions[0].shape[-4:])					,					[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]					,					)
            else:
                self.assertListEqual(
                    list(attentions[0].shape[-3:])					,					[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]					,					)







    def      UpperCamelCase__  (      self)			->      List[str]:
        __UpperCamelCase    ,			__UpperCamelCase	:Tuple         =						self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            # Prepare our model
            __UpperCamelCase	:List[str]         =						model_class(A_)
            # These are maximally general inputs for the model, with multiple None dimensions
            # Hopefully this will catch any conditionals that fail for flexible shapes
            __UpperCamelCase	:List[Any]         =						{
                key: tf.keras.Input(shape=val.shape[1:]					,					dtype=val.dtype					,					name=A_)
                for key, val in model.input_signature.items()
                if key in model.dummy_inputs
            }
            __UpperCamelCase	:Optional[int]         =						model(A_)
            self.assertTrue(outputs_dict is not None)



def lowerCamelCase    (  ):
    '''simple docstring'''







    __UpperCamelCase	:Optional[Any]         =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    return image


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






    '''simple docstring'''






    @cached_property
    def      UpperCamelCase__  (      self)			->      Optional[int]:
        return (
            EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300''')
            if is_vision_available()
            else None
        )







    @slow
    def      UpperCamelCase__  (      self)			->      Dict:
        __UpperCamelCase	:Optional[Any]         =						TFEfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300''')
        __UpperCamelCase	:Optional[Any]         =						self.default_image_processor
        __UpperCamelCase	:Union[str, Any]         =						prepare_img()
        __UpperCamelCase	:Any         =						image_processor(images=A_					,					return_tensors='''tf''')
        # forward pass
        __UpperCamelCase	:List[str]         =						model(**A_					,					training=A_)
        # verify the logits
        __UpperCamelCase	:str         =						tf.TensorShape((1, 1_000))
        self.assertEqual(outputs.logits.shape					,					A_)
        __UpperCamelCase	:List[Any]         =						tf.constant([-0.05_55, 0.48_25, -0.08_52])
        self.assertTrue(np.allclose(outputs.logits[0, :3]					,					A_					,					atol=1E-4))







    @slow
    def      UpperCamelCase__  (      self)			->      Optional[int]:
        __UpperCamelCase	:Optional[int]         =						TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
            '''snap-research/efficientformer-l1-300''')
        __UpperCamelCase	:Optional[Any]         =						self.default_image_processor
        __UpperCamelCase	:List[Any]         =						prepare_img()
        __UpperCamelCase	:Optional[Any]         =						image_processor(images=A_					,					return_tensors='''tf''')
        # forward pass
        __UpperCamelCase	:int         =						model(**A_					,					training=A_)
        # verify the logits
        __UpperCamelCase	:Tuple         =						tf.TensorShape((1, 1_000))
        self.assertEqual(outputs.logits.shape					,					A_)
        __UpperCamelCase	:Any         =						tf.constant([-0.13_12, 0.43_53, -1.04_99])
        self.assertTrue(np.allclose(outputs.logits[0, :3]					,					A_					,					atol=1E-4))







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:    Union[str, Any]           = {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    List[Any]           = [
						    'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'GitForCausalLM',
						    'GitModel',
						    'GitPreTrainedModel',
						    'GitVisionModel',
						]

if TYPE_CHECKING:
						from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
						from .processing_git import GitProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_git import (
												    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GitForCausalLM,
												    GitModel,
												    GitPreTrainedModel,
												    GitVisionModel,
												)

else:
						import sys

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




