



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        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A = 13				,							_A = 64				,							_A = 2				,							_A = 3				,							_A = 3				,							_A = True				,							_A = True				,							_A = 128				,							_A=[16, 32, 64, 128]				,							_A = 7				,							_A = 4				,							_A = 37				,							_A = "gelu"				,							_A = 0.1				,							_A = 0.1				,							_A = 10				,							_A = 0.0_2				,							_A = 2				,							_A = 1				,							_A = 128				,							_A = [2, 2, 2, 2]				,							_A = 2				,							_A = 2				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= parent
													__SCREAMING_SNAKE_CASE									= batch_size
													__SCREAMING_SNAKE_CASE									= image_size
													__SCREAMING_SNAKE_CASE									= patch_size
													__SCREAMING_SNAKE_CASE									= num_channels
													__SCREAMING_SNAKE_CASE									= is_training
													__SCREAMING_SNAKE_CASE									= use_labels
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= hidden_dropout_prob
													__SCREAMING_SNAKE_CASE									= attention_probs_dropout_prob
													__SCREAMING_SNAKE_CASE									= type_sequence_label_size
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= encoder_stride
													__SCREAMING_SNAKE_CASE									= num_attention_outputs
													__SCREAMING_SNAKE_CASE									= embed_dim
													__SCREAMING_SNAKE_CASE									= embed_dim + 1
													__SCREAMING_SNAKE_CASE									= resolution
													__SCREAMING_SNAKE_CASE									= depths
													__SCREAMING_SNAKE_CASE									= hidden_sizes
													__SCREAMING_SNAKE_CASE									= dim
													__SCREAMING_SNAKE_CASE									= mlp_expansion_ratio






						def 	_A     (   self		):







													'''simple docstring'''

													__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.type_sequence_label_size		)

													__SCREAMING_SNAKE_CASE									= self.get_config()

													return config, pixel_values, labels






						def 	_A     (   self		):







													'''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=_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 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= TFEfficientFormerModel(config=_A		)
													__SCREAMING_SNAKE_CASE									= model(_A				,							training=_A		)
													self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.hidden_size)		)






						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.type_sequence_label_size
													__SCREAMING_SNAKE_CASE									= TFEfficientFormerForImageClassification(_A		)
													__SCREAMING_SNAKE_CASE									= model(_A				,							labels=_A				,							training=_A		)
													self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.type_sequence_label_size)		)

													# test greyscale images
													__SCREAMING_SNAKE_CASE									= 1
													__SCREAMING_SNAKE_CASE									= TFEfficientFormerForImageClassification(_A		)

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






						def 	_A     (   self		):







													'''simple docstring'''

													__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        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			List[str]      =  (
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCamelCase__      :			Optional[Any]      =  (
						    {
						        '''feature-extraction''': TFEfficientFormerModel,
						        '''image-classification''': (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCamelCase__      :			List[str]      =  False

						UpperCamelCase__      :			List[str]      =  False
						UpperCamelCase__      :			Union[str, Any]      =  False
						UpperCamelCase__      :			Optional[int]      =  False
						UpperCamelCase__      :			int      =  False






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= TFEfficientFormerModelTester(self		)
													__SCREAMING_SNAKE_CASE									= ConfigTester(
													    self				,							config_class=_A				,							has_text_modality=_A				,							hidden_size=37		)






						def 	_A     (   self		):







													'''simple docstring'''

													self.config_tester.run_common_tests()






						@unittest.skip(reason='EfficientFormer does not use inputs_embeds'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass






						@unittest.skip(reason='EfficientFormer does not support input and output embeddings'		)
						def 	_A     (   self		):







													'''simple docstring'''

													pass






						def 	_A     (   self		):







													'''simple docstring'''

													__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(_A		)
																				__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]				,							_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													def check_hidden_states_output(_A				,							_A				,							_A		):
																				__SCREAMING_SNAKE_CASE									= model_class(_A		)

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

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

																											self.asseretIsInstance(_A				,							(list, tuple)		)
																											self.assertEqual(len(_A		)				,							_A		)
																											__SCREAMING_SNAKE_CASE									= getattr(self.model_tester				,							'seq_length'				,							_A		)
																											__SCREAMING_SNAKE_CASE									= getattr(self.model_tester				,							'decoder_seq_length'				,							_A		)

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

													__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									= True
																				check_hidden_states_output(_A				,							_A				,							_A		)

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

																				check_hidden_states_output(_A				,							_A				,							_A		)






						def 	_A     (   self				,							_A				,							_A				,							_A=False		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_image_modeling(*_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*_A		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__SCREAMING_SNAKE_CASE									= TFEfficientFormerModel.from_pretrained(_A		)
																				self.assertIsNotNone(_A		)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs_for_common()
													__SCREAMING_SNAKE_CASE									= True

													__SCREAMING_SNAKE_CASE									= getattr(self.model_tester				,							'seq_length'				,							_A		)
													__SCREAMING_SNAKE_CASE									= getattr(self.model_tester				,							'encoder_seq_length'				,							_A		)
													__SCREAMING_SNAKE_CASE									= getattr(self.model_tester				,							'key_length'				,							_A		)
													__SCREAMING_SNAKE_CASE									= getattr(self.model_tester				,							'chunk_length'				,							_A		)

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

													for model_class in self.all_model_classes:
																				__SCREAMING_SNAKE_CASE									= True
																				__SCREAMING_SNAKE_CASE									= False
																				__SCREAMING_SNAKE_CASE									= True
																				__SCREAMING_SNAKE_CASE									= model_class(_A		)

																				__SCREAMING_SNAKE_CASE									= model(**self._prepare_for_class(_A				,							_A		)				,							training=_A		)
																				__SCREAMING_SNAKE_CASE									= 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"]
																				__SCREAMING_SNAKE_CASE									= True
																				__SCREAMING_SNAKE_CASE									= model_class(_A		)
																				__SCREAMING_SNAKE_CASE									= model(**self._prepare_for_class(_A				,							_A		)				,							training=_A		)

																				__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				# Prepare our model
																				__SCREAMING_SNAKE_CASE									= 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
																				__SCREAMING_SNAKE_CASE									= {
																				    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
																				}
																				__SCREAMING_SNAKE_CASE									= model(_A		)
																				self.assertTrue(outputs_dict is not None		)







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

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

						'''simple docstring'''




						@cached_property
						def 	_A     (   self		):







													'''simple docstring'''

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






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'		)
													__SCREAMING_SNAKE_CASE									= self.default_image_processor
													__SCREAMING_SNAKE_CASE									= prepare_img()
													__SCREAMING_SNAKE_CASE									= image_processor(images=_A				,							return_tensors='tf'		)
													# forward pass
													__SCREAMING_SNAKE_CASE									= model(**_A				,							training=_A		)
													# verify the logits
													__SCREAMING_SNAKE_CASE									= tf.TensorShape((1, 1_000)		)
													self.assertEqual(outputs.logits.shape				,							_A		)
													__SCREAMING_SNAKE_CASE									= tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]		)
													self.assertTrue(np.allclose(outputs.logits[0, :3]				,							_A				,							atol=1e-4		)		)






						@slow
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
													    'snap-research/efficientformer-l1-300'		)
													__SCREAMING_SNAKE_CASE									= self.default_image_processor
													__SCREAMING_SNAKE_CASE									= prepare_img()
													__SCREAMING_SNAKE_CASE									= image_processor(images=_A				,							return_tensors='tf'		)
													# forward pass
													__SCREAMING_SNAKE_CASE									= model(**_A				,							training=_A		)
													# verify the logits
													__SCREAMING_SNAKE_CASE									= tf.TensorShape((1, 1_000)		)
													self.assertEqual(outputs.logits.shape				,							_A		)
													__SCREAMING_SNAKE_CASE									= tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]		)
													self.assertTrue(np.allclose(outputs.logits[0, :3]				,							_A				,							atol=1e-4		)		)











from ..utils import DummyObject, requires_backends

class        UpperCAmelCase_  (       metaclass=UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ['''speech''']






						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													requires_backends(self				,							['speech']		)







class        UpperCAmelCase_  (       metaclass=UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  ['''speech''']






						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													requires_backends(self				,							['speech']		)






