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 					snake_case	:

   """simple docstring"""







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





   def 				snake_case__							(    self	):
     __lowercase					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

     __lowercase					=		self.get_config()

     return config, pixel_values, labels





   def 				snake_case__							(    self	):
     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=__snake_case					,	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 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_	):
     __lowercase					=		TFEfficientFormerModel(config=__snake_case	)
     __lowercase					=		model(__snake_case					,	training=__snake_case	)
     self.parent.assertEqual(result.last_hidden_state.shape					,	(self.batch_size, self.seq_length, self.hidden_size)	)





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_	):
     __lowercase					=		self.type_sequence_label_size
     __lowercase					=		TFEfficientFormerForImageClassification(__snake_case	)
     __lowercase					=		model(__snake_case					,	labels=__snake_case					,	training=__snake_case	)
     self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.type_sequence_label_size)	)

     # test greyscale images
     __lowercase					=		1
     __lowercase					=		TFEfficientFormerForImageClassification(__snake_case	)

     __lowercase					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
     __lowercase					=		model(__snake_case					,	labels=__snake_case	)
     self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.type_sequence_label_size)	)





   def 				snake_case__							(    self	):
     __lowercase					=		self.prepare_config_and_inputs()
     __lowercase					,     __lowercase					,     __lowercase					=		config_and_inputs
     __lowercase					=		{"pixel_values": pixel_values}
     return config, inputs_dict







@require_tf
class 					snake_case	(     A__							,A__							,unittest.TestCase   ):

   """simple docstring"""







   __lowerCAmelCase   						=      (
       (
           TFEfficientFormerModel,
           TFEfficientFormerForImageClassificationWithTeacher,
           TFEfficientFormerForImageClassification,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase   						=      (
       {
           """feature-extraction""": TFEfficientFormerModel,
           """image-classification""": (
               TFEfficientFormerForImageClassification,
               TFEfficientFormerForImageClassificationWithTeacher,
           ),
       }
       if is_tf_available()
       else {}
   )

   __lowerCAmelCase   						=      False

   __lowerCAmelCase   						=      False
   __lowerCAmelCase   						=      False
   __lowerCAmelCase   						=      False
   __lowerCAmelCase   						=      False





   def 				snake_case__							(    self	):
     __lowercase					=		TFEfficientFormerModelTester(self	)
     __lowercase					=		ConfigTester(
         self					,	config_class=__snake_case					,	has_text_modality=__snake_case					,	hidden_size=37	)





   def 				snake_case__							(    self	):
     self.config_tester.run_common_tests()





   @unittest.skip(reason="EfficientFormer does not use inputs_embeds"	)
   def 				snake_case__							(    self	):
     pass





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





   def 				snake_case__							(    self	):
     __lowercase					,     __lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase					=		["pixel_values"]
       self.assertListEqual(arg_names[:1]					,	__snake_case	)





   def 				snake_case__							(    self	):
     def check_hidden_states_output(lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_	):
       __lowercase					=		model_class(__snake_case	)

       __lowercase					=		model(**self._prepare_for_class(__snake_case					,	__snake_case	)					,	training=__snake_case	)
       __lowercase					=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __lowercase					=		getattr(
           self.model_tester					,	"expected_num_hidden_layers"					,	self.model_tester.num_hidden_layers + 1	)
       self.assertEqual(len(__snake_case	)					,	__snake_case	)

       if hasattr(self.model_tester					,	"encoder_seq_length"	):
         __lowercase					=		self.model_tester.encoder_seq_length
         if hasattr(self.model_tester					,	"chunk_length"	) and self.model_tester.chunk_length > 1:
           __lowercase					=		seq_length * self.model_tester.chunk_length
       else:
         __lowercase					=		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:
         __lowercase					=		outputs.decoder_hidden_states

         self.asseretIsInstance(__snake_case					,	(list, tuple)	)
         self.assertEqual(len(__snake_case	)					,	__snake_case	)
         __lowercase					=		getattr(self.model_tester					,	"seq_length"					,	__snake_case	)
         __lowercase					=		getattr(self.model_tester					,	"decoder_seq_length"					,	__snake_case	)

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

     __lowercase					,     __lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __lowercase					=		True
       check_hidden_states_output(__snake_case					,	__snake_case					,	__snake_case	)

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

       check_hidden_states_output(__snake_case					,	__snake_case					,	__snake_case	)





   def 				snake_case__							(    self					,	lowerCAmelCase_					,	lowerCAmelCase_					,	lowerCAmelCase_=False	):
     __lowercase					=		super()._prepare_for_class(__snake_case					,	__snake_case					,	return_labels=__snake_case	)

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

     return inputs_dict





   def 				snake_case__							(    self	):
     __lowercase					=		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__snake_case	)





   @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"	)
   def 				snake_case__							(    self	):
     __lowercase					=		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case	)





   def 				snake_case__							(    self	):
     __lowercase					=		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__snake_case	)





   @slow
   def 				snake_case__							(    self	):
     for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase					=		TFEfficientFormerModel.from_pretrained(__snake_case	)
       self.assertIsNotNone(__snake_case	)





   def 				snake_case__							(    self	):
     __lowercase					,     __lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()
     __lowercase					=		True

     __lowercase					=		getattr(self.model_tester					,	"seq_length"					,	__snake_case	)
     __lowercase					=		getattr(self.model_tester					,	"encoder_seq_length"					,	__snake_case	)
     __lowercase					=		getattr(self.model_tester					,	"key_length"					,	__snake_case	)
     __lowercase					=		getattr(self.model_tester					,	"chunk_length"					,	__snake_case	)

     if chunk_length is not None and hasattr(self.model_tester					,	"num_hashes"	):
       __lowercase					=		encoder_seq_length * self.model_tester.num_hashes

     for model_class in self.all_model_classes:
       __lowercase					=		True
       __lowercase					=		False
       __lowercase					=		True
       __lowercase					=		model_class(__snake_case	)

       __lowercase					=		model(**self._prepare_for_class(__snake_case					,	__snake_case	)					,	training=__snake_case	)
       __lowercase					=		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(__snake_case	)					,	self.model_tester.num_attention_outputs	)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __lowercase					=		True
       __lowercase					=		model_class(__snake_case	)
       __lowercase					=		model(**self._prepare_for_class(__snake_case					,	__snake_case	)					,	training=__snake_case	)

       __lowercase					=		outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(__snake_case	)					,	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 				snake_case__							(    self	):
     # 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
     __lowercase					,     __lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

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







def      __lowercase				(      )      ->     str:






  '''simple docstring'''

  __lowercase					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
  return image




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

   """simple docstring"""







   @cached_property
   def 				snake_case__							(    self	):
     return (
         EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"	)
         if is_vision_available()
         else None
     )





   @slow
   def 				snake_case__							(    self	):
     __lowercase					=		TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"	)
     __lowercase					=		self.default_image_processor
     __lowercase					=		prepare_img()
     __lowercase					=		image_processor(images=__snake_case					,	return_tensors="tf"	)
     # forward pass
     __lowercase					=		model(**__snake_case					,	training=__snake_case	)
     # verify the logits
     __lowercase					=		tf.TensorShape((1, 1000)	)
     self.assertEqual(outputs.logits.shape					,	__snake_case	)
     __lowercase					=		tf.constant([-0.05_55, 0.48_25, -0.08_52]	)
     self.assertTrue(np.allclose(outputs.logits[0, :3]					,	__snake_case					,	atol=1E-4	)	)





   @slow
   def 				snake_case__							(    self	):
     __lowercase					=		TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
         "snap-research/efficientformer-l1-300"	)
     __lowercase					=		self.default_image_processor
     __lowercase					=		prepare_img()
     __lowercase					=		image_processor(images=__snake_case					,	return_tensors="tf"	)
     # forward pass
     __lowercase					=		model(**__snake_case					,	training=__snake_case	)
     # verify the logits
     __lowercase					=		tf.TensorShape((1, 1000)	)
     self.assertEqual(outputs.logits.shape					,	__snake_case	)
     __lowercase					=		tf.constant([-0.13_12, 0.43_53, -1.04_99]	)
     self.assertTrue(np.allclose(outputs.logits[0, :3]					,	__snake_case					,	atol=1E-4	)	)








'''simple docstring'''




import logging

from transformers.configuration_utils import PretrainedConfig


_SCREAMING_SNAKE_CASE							     =			logging.getLogger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "masked_bert"



     def __init__(   self							:			Any   ,       __snake_case							:			Optional[Any]=3_05_22   ,       __snake_case							:			List[Any]=7_68   ,       __snake_case							:			int=12   ,       __snake_case							:			List[str]=12   ,       __snake_case							:			int=30_72   ,       __snake_case							:			Optional[int]="gelu"   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Tuple=2   ,       __snake_case							:			Tuple=0.02   ,       __snake_case							:			Tuple=1e-12   ,       __snake_case							:			Union[str, Any]=0   ,       __snake_case							:			int="topK"   ,       __snake_case							:			int="constant"   ,       __snake_case							:			List[str]=0.0   ,       **__snake_case							:			Any   ,       )->	Union[str, Any]:
        super().__init__(pad_token_id=__snake_case   ,       **__snake_case						)

        snake_case 						=   vocab_size
        snake_case 						=   hidden_size
        snake_case 						=   num_hidden_layers
        snake_case 						=   num_attention_heads
        snake_case 						=   hidden_act
        snake_case 						=   intermediate_size
        snake_case 						=   hidden_dropout_prob
        snake_case 						=   attention_probs_dropout_prob
        snake_case 						=   max_position_embeddings
        snake_case 						=   type_vocab_size
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   pruning_method
        snake_case 						=   mask_init
        snake_case 						=   mask_scale

