





import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase_  							=				'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_  							=				direct_transformers_import(PATH_TO_TRANSFORMERS)

UpperCAmelCase_  							=				transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase_  							=				{
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[Any]   )   ->			int:
    UpperCamelCase__							:       Optional[Any]      =   False
    for attribute in attributes:
        for modeling_source in source_strings:
            # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
            if (
                f"config.{attribute}" in modeling_source
                or f"getattr(config, \"{attribute}\"" in modeling_source
                or f"getattr(self.config, \"{attribute}\"" in modeling_source
            ):
                UpperCamelCase__							:       str      =   True
            # Deal with multi-line cases
            elif (
                re.search(
                    rf"getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""	,     __UpperCAmelCase	,     )
                is not None
            ):
                UpperCamelCase__							:       List[str]      =   True
            # `SequenceSummary` is called with `SequenceSummary(config)`
            elif attribute in [
                "summary_type",
                "summary_use_proj",
                "summary_activation",
                "summary_last_dropout",
                "summary_proj_to_labels",
                "summary_first_dropout",
            ]:
                if "SequenceSummary" in modeling_source:
                    UpperCamelCase__							:       Any      =   True
            if attribute_used:
                break
        if attribute_used:
            break

    # common and important attributes, even if they do not always appear in the modeling files
    UpperCamelCase__							:       Dict      =   [
        '''bos_index''',
        '''eos_index''',
        '''pad_index''',
        '''unk_index''',
        '''mask_index''',
        '''image_size''',
        '''use_cache''',
        '''out_features''',
        '''out_indices''',
    ]
    UpperCamelCase__							:       Dict      =   ['''encoder_no_repeat_ngram_size''']

    # Special cases to be allowed
    UpperCamelCase__							:       str      =   True
    if not attribute_used:
        UpperCamelCase__							:       Union[str, Any]      =   False
        for attribute in attributes:
            # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
            if attribute in ["is_encoder_decoder"] and default_value is True:
                UpperCamelCase__							:       Optional[Any]      =   True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                UpperCamelCase__							:       str      =   True

            # Allow cases without checking the default value in the configuration class
            elif attribute in attributes_to_allow + attributes_used_in_generation:
                UpperCamelCase__							:       Union[str, Any]      =   True
            elif attribute.endswith('''_token_id'''   ):
                UpperCamelCase__							:       List[str]      =   True

            # configuration class specific cases
            if not case_allowed:
                UpperCamelCase__							:       List[Any]      =   SPECIAL_CASES_TO_ALLOW.get(config_class.__name__	,     []   )
                UpperCamelCase__							:       Optional[int]      =   allowed_cases is True or attribute in allowed_cases

    return attribute_used or case_allowed




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			List[str]:
    UpperCamelCase__							:       int      =   dict(inspect.signature(config_class.__init__   ).parameters   )
    UpperCamelCase__							:       List[Any]      =   [x for x in list(signature.keys()   ) if x not in ['''self''', '''kwargs''']]
    UpperCamelCase__							:       int      =   [signature[param].default for param in parameter_names]

    # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
    # as one variant is used, the test should pass
    UpperCamelCase__							:       Any      =   {}
    if len(config_class.attribute_map   ) > 0:
        UpperCamelCase__							:       List[Any]      =   {v: k for k, v in config_class.attribute_map.items()}

    # Get the path to modeling source files
    UpperCamelCase__							:       Optional[int]      =   inspect.getsourcefile(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   os.path.dirname(__UpperCAmelCase   )
    # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
    UpperCamelCase__							:       Dict      =   [os.path.join(__UpperCAmelCase	,     __UpperCAmelCase   ) for fn in os.listdir(__UpperCAmelCase   ) if fn.startswith('''modeling_'''   )]

    # Get the source code strings
    UpperCamelCase__							:       List[str]      =   []
    for path in modeling_paths:
        if os.path.isfile(__UpperCAmelCase   ):
            with open(__UpperCAmelCase   ) as fp:
                modeling_sources.append(fp.read()   )

    UpperCamelCase__							:       Union[str, Any]      =   []
    for config_param, default_value in zip(__UpperCAmelCase	,     __UpperCAmelCase   ):
        # `attributes` here is all the variant names for `config_param`
        UpperCamelCase__							:       int      =   [config_param]
        # some configuration classes have non-empty `attribute_map`, and both names could be used in the
        # corresponding modeling files. As long as one of them appears, it is fine.
        if config_param in reversed_attribute_map:
            attributes.append(reversed_attribute_map[config_param]   )

        if not check_attribute_being_used(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   ):
            unused_attributes.append(attributes[0]   )

    return sorted(__UpperCAmelCase   )




def 	lowerCAmelCase_					(						)   ->			List[str]:
    UpperCamelCase__							:       Tuple      =   {}
    for _config_class in list(CONFIG_MAPPING.values()   ):
        # Skip deprecated models
        if "models.deprecated" in _config_class.__module__:
            continue
        # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
        UpperCamelCase__							:       Optional[int]      =   [
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class   )	,     lambda __UpperCAmelCase   : inspect.isclass(__UpperCAmelCase   )
                and issubclass(__UpperCAmelCase	,     __UpperCAmelCase   )
                and inspect.getmodule(__UpperCAmelCase   ) == inspect.getmodule(_config_class   )	,     )
        ]
        for config_class in config_classes_in_module:
            UpperCamelCase__							:       List[str]      =   check_config_attributes_being_used(__UpperCAmelCase   )
            if len(__UpperCAmelCase   ) > 0:
                UpperCamelCase__							:       Tuple      =   unused_attributes

    if len(__UpperCAmelCase   ) > 0:
        UpperCamelCase__							:       Dict      =   '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
        for name, attributes in configs_with_unused_attributes.items():
            error += f"{name}: {attributes}\n"

        raise ValueError(__UpperCAmelCase   )


if __name__ == "__main__":
  check_config_attributes()








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 					lowercase__ :

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__ = 13,	__magic_name__ = 64,	__magic_name__ = 2,	__magic_name__ = 3,	__magic_name__ = 3,	__magic_name__ = True,	__magic_name__ = True,	__magic_name__ = 128,	__magic_name__=[16, 32, 64, 128],	__magic_name__ = 7,	__magic_name__ = 4,	__magic_name__ = 37,	__magic_name__ = "gelu",	__magic_name__ = 0.1,	__magic_name__ = 0.1,	__magic_name__ = 10,	__magic_name__ = 0.02,	__magic_name__ = 2,	__magic_name__ = 1,	__magic_name__ = 128,	__magic_name__ = [2, 2, 2, 2],	__magic_name__ = 2,	__magic_name__ = 2,	)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   parent
       UpperCamelCase__							:       Optional[int]      =   batch_size
       UpperCamelCase__							:       Optional[Any]      =   image_size
       UpperCamelCase__							:       Optional[int]      =   patch_size
       UpperCamelCase__							:       Any      =   num_channels
       UpperCamelCase__							:       int      =   is_training
       UpperCamelCase__							:       str      =   use_labels
       UpperCamelCase__							:       Optional[Any]      =   hidden_size
       UpperCamelCase__							:       Tuple      =   num_hidden_layers
       UpperCamelCase__							:       str      =   num_attention_heads
       UpperCamelCase__							:       Any      =   intermediate_size
       UpperCamelCase__							:       int      =   hidden_act
       UpperCamelCase__							:       Dict      =   hidden_dropout_prob
       UpperCamelCase__							:       str      =   attention_probs_dropout_prob
       UpperCamelCase__							:       Tuple      =   type_sequence_label_size
       UpperCamelCase__							:       Optional[Any]      =   initializer_range
       UpperCamelCase__							:       Optional[int]      =   encoder_stride
       UpperCamelCase__							:       Any      =   num_attention_outputs
       UpperCamelCase__							:       Dict      =   embed_dim
       UpperCamelCase__							:       str      =   embed_dim + 1
       UpperCamelCase__							:       int      =   resolution
       UpperCamelCase__							:       List[str]      =   depths
       UpperCamelCase__							:       str      =   hidden_sizes
       UpperCamelCase__							:       Tuple      =   dim
       UpperCamelCase__							:       Optional[int]      =   mlp_expansion_ratio




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

       UpperCamelCase__							:       int      =   self.get_config()

       return config, pixel_values, labels




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       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=__magic_name__,	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,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   TFEfficientFormerModel(config=__magic_name__							)
       UpperCamelCase__							:       str      =   model(__magic_name__,	training=__magic_name__							)
       self.parent.assertEqual(result.last_hidden_state.shape,	(self.batch_size, self.seq_length, self.hidden_size)							)




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






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.type_sequence_label_size
       UpperCamelCase__							:       Dict      =   TFEfficientFormerForImageClassification(__magic_name__							)
       UpperCamelCase__							:       Any      =   model(__magic_name__,	labels=__magic_name__,	training=__magic_name__							)
       self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.type_sequence_label_size)							)

       # test greyscale images
       UpperCamelCase__							:       Optional[Any]      =   1
       UpperCamelCase__							:       List[str]      =   TFEfficientFormerForImageClassification(__magic_name__							)

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




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






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   self.prepare_config_and_inputs()
       UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   config_and_inputs
       UpperCamelCase__							:       Any      =   {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


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

   a		:							Any     	=	False

   a		:							Tuple     	=	False
   a		:							Any     	=	False
   a		:							int     	=	False
   a		:							Tuple     	=	False




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   TFEfficientFormerModelTester(self							)
       UpperCamelCase__							:       int      =   ConfigTester(
           self,	config_class=__magic_name__,	has_text_modality=__magic_name__,	hidden_size=37							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       self.config_tester.run_common_tests()




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






       """simple docstring"""
       pass




   @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''							)
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Any      =   self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase__							:       int      =   ['''pixel_values''']
           self.assertListEqual(arg_names[:1],	__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       def check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Union[str, Any]      =   model_class(__magic_name__							)

           UpperCamelCase__							:       str      =   model(**self._prepare_for_class(__magic_name__,	__magic_name__							),	training=__magic_name__							)
           UpperCamelCase__							:       Tuple      =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCamelCase__							:       Optional[int]      =   getattr(
               self.model_tester,	'''expected_num_hidden_layers''',	self.model_tester.num_hidden_layers + 1							)
           self.assertEqual(len(__magic_name__							),	__magic_name__							)

           if hasattr(self.model_tester,	'''encoder_seq_length'''							):
               UpperCamelCase__							:       Dict      =   self.model_tester.encoder_seq_length
               if hasattr(self.model_tester,	'''chunk_length'''							) and self.model_tester.chunk_length > 1:
                   UpperCamelCase__							:       Tuple      =   seq_length * self.model_tester.chunk_length
           else:
               UpperCamelCase__							:       Optional[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__							:       List[str]      =   outputs.decoder_hidden_states

               self.asseretIsInstance(__magic_name__,	(list, tuple)							)
               self.assertEqual(len(__magic_name__							),	__magic_name__							)
               UpperCamelCase__							:       str      =   getattr(self.model_tester,	'''seq_length''',	__magic_name__							)
               UpperCamelCase__							:       Optional[Any]      =   getattr(self.model_tester,	'''decoder_seq_length''',	__magic_name__							)

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

       UpperCamelCase__     ,UpperCamelCase__							:       str      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__							:       List[str]      =   True
           check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							)

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

           check_hidden_states_output(__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__=False							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   super()._prepare_for_class(__magic_name__,	__magic_name__,	return_labels=__magic_name__							)

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

       return inputs_dict




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






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__							)




   @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''							)
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__							)




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






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__magic_name__							)




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






       """simple docstring"""
       for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__							:       Dict      =   TFEfficientFormerModel.from_pretrained(__magic_name__							)
           self.assertIsNotNone(__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       str      =   self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase__							:       Dict      =   True

       UpperCamelCase__							:       Optional[Any]      =   getattr(self.model_tester,	'''seq_length''',	__magic_name__							)
       UpperCamelCase__							:       Any      =   getattr(self.model_tester,	'''encoder_seq_length''',	__magic_name__							)
       UpperCamelCase__							:       Tuple      =   getattr(self.model_tester,	'''key_length''',	__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   getattr(self.model_tester,	'''chunk_length''',	__magic_name__							)

       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__							:       Dict      =   True
           UpperCamelCase__							:       Optional[int]      =   False
           UpperCamelCase__							:       Any      =   True
           UpperCamelCase__							:       str      =   model_class(__magic_name__							)

           UpperCamelCase__							:       Optional[int]      =   model(**self._prepare_for_class(__magic_name__,	__magic_name__							),	training=__magic_name__							)
           UpperCamelCase__							:       List[str]      =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(__magic_name__							),	self.model_tester.num_attention_outputs							)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           UpperCamelCase__							:       Optional[int]      =   True
           UpperCamelCase__							:       int      =   model_class(__magic_name__							)
           UpperCamelCase__							:       str      =   model(**self._prepare_for_class(__magic_name__,	__magic_name__							),	training=__magic_name__							)

           UpperCamelCase__							:       List[str]      =   outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
           self.assertEqual(len(__magic_name__							),	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							)				->   Dict:






       """simple docstring"""
       # We use a simplified version of this test for EfficientFormer because it requires training=False
       # and Keras refuses to let us force that during functional construction
       UpperCamelCase__     ,UpperCamelCase__							:       str      =   self.model_tester.prepare_config_and_inputs_for_common()

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




def 	lowerCAmelCase_					(						)   ->			List[str]:
    UpperCamelCase__							:       Any      =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
    return image






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

   '''simple docstring'''


   @cached_property
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       return (
           EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''							)
           if is_vision_available()
           else None
       )




   @slow
   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   TFEfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''							)
       UpperCamelCase__							:       Dict      =   self.default_image_processor
       UpperCamelCase__							:       List[str]      =   prepare_img()
       UpperCamelCase__							:       str      =   image_processor(images=__magic_name__,	return_tensors='''tf'''							)
       # forward pass
       UpperCamelCase__							:       Dict      =   model(**__magic_name__,	training=__magic_name__							)
       # verify the logits
       UpperCamelCase__							:       Tuple      =   tf.TensorShape((1, 1000)							)
       self.assertEqual(outputs.logits.shape,	__magic_name__							)
       UpperCamelCase__							:       List[str]      =   tf.constant([-0.0555, 0.4825, -0.0852]							)
       self.assertTrue(np.allclose(outputs.logits[0, :3],	__magic_name__,	atol=1E-4							)							)




   @slow
   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       str      =   TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
           '''snap-research/efficientformer-l1-300'''							)
       UpperCamelCase__							:       List[str]      =   self.default_image_processor
       UpperCamelCase__							:       Union[str, Any]      =   prepare_img()
       UpperCamelCase__							:       int      =   image_processor(images=__magic_name__,	return_tensors='''tf'''							)
       # forward pass
       UpperCamelCase__							:       Tuple      =   model(**__magic_name__,	training=__magic_name__							)
       # verify the logits
       UpperCamelCase__							:       Tuple      =   tf.TensorShape((1, 1000)							)
       self.assertEqual(outputs.logits.shape,	__magic_name__							)
       UpperCamelCase__							:       Optional[int]      =   tf.constant([-0.1312, 0.4353, -1.0499]							)
       self.assertTrue(np.allclose(outputs.logits[0, :3],	__magic_name__,	atol=1E-4							)							)

