
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   from .features import FeatureType


lowercase_				,    lowercase_				,    lowercase_ 				=			False, False, False





@dataclass
class     __UpperCamelCase :






     """simple docstring"""







     lowerCAmelCase_							   =			None
     lowerCAmelCase_							   =			True
     lowerCAmelCase_							   =			True
     lowerCAmelCase_							   =			None
     # Automatically constructed
     lowerCAmelCase_							   =			"dict"
     lowerCAmelCase_							   =			pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}       )
     lowerCAmelCase_							   =			field(default='''Audio''' ,						init=lowerCAmelCase__ ,						repr=lowerCAmelCase__       )



     def __call__(				self							:							int ):







       """simple docstring"""




       return self.pa_type



     def  UpperCAmelCase__							(				self							:							str     ,						_A							:							Union[str, bytes, dict] ):







       """simple docstring"""




       try:
         import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
       except ImportError as err:
         raise ImportError('''To support encoding audio data, please install \'soundfile\'.''' ) from err
       if isinstance(_A     ,						_A ):
         return {"bytes": None, "path": value}
       elif isinstance(_A     ,						_A ):
         return {"bytes": value, "path": None}
       elif "array" in value:
         # convert the audio array to wav bytes
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		BytesIO()
         sf.write(_A     ,						value['''array''']     ,						value['''sampling_rate''']     ,						format='''wav''' )
         return {"bytes": buffer.getvalue(), "path": None}
       elif value.get('''path''' ) is not None and os.path.isfile(value['''path'''] ):
         # we set "bytes": None to not duplicate the data if they're already available locally
         if value["path"].endswith('''pcm''' ):
           # "PCM" only has raw audio bytes
           if value.get('''sampling_rate''' ) is None:
             # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
             raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''' )
           if value.get('''bytes''' ):
             # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
             __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		np.frombuffer(value['''bytes''']     ,						dtype=np.intaa ).astype(np.floataa ) / 3_2767
           else:
             __SCREAMING_SNAKE_CASE      :	Optional[int]					=		np.memmap(value['''path''']     ,						dtype='''h'''     ,						mode='''r''' ).astype(np.floataa ) / 3_2767

           __SCREAMING_SNAKE_CASE      :	str					=		BytesIO(bytes() )
           sf.write(_A     ,						_A     ,						value['''sampling_rate''']     ,						format='''wav''' )
           return {"bytes": buffer.getvalue(), "path": None}
         else:
           return {"bytes": None, "path": value.get('''path''' )}
       elif value.get('''bytes''' ) is not None or value.get('''path''' ) is not None:
         # store the audio bytes, and path is used to infer the audio format using the file extension
         return {"bytes": value.get('''bytes''' ), "path": value.get('''path''' )}
       else:
         raise ValueError(
             F'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							dict     ,						_A							:							Optional[Dict[str, Union[str, bool, None]]] = None ):







       """simple docstring"""




       if not self.decode:
         raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''' )

       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(value['''path'''], BytesIO(value['''bytes'''] )) if value['''bytes'''] is not None else (value['''path'''], None)
       if path is None and file is None:
         raise ValueError(F'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.''' )

       try:
         import librosa
         import soundfile as sf
       except ImportError as err:
         raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''' ) from err

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		xsplitext(_A )[1][1:].lower() if path is not None else None
       if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
         raise RuntimeError(
             '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
             '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )
       elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
         raise RuntimeError(
             '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
             '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''' )

       if file is None:
         __SCREAMING_SNAKE_CASE      :	str					=		token_per_repo_id or {}
         __SCREAMING_SNAKE_CASE      :	int					=		path.split('''::''' )[-1]
         try:
           __SCREAMING_SNAKE_CASE      :	int					=		string_to_dict(_A     ,						config.HUB_DATASETS_URL )['''repo_id''']
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		token_per_repo_id[repo_id]
         except (ValueError, KeyError):
           __SCREAMING_SNAKE_CASE      :	str					=		None

         with xopen(_A     ,						'''rb'''     ,						use_auth_token=_A ) as f:
           __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		sf.read(_A )

       else:
         __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	int					=		sf.read(_A )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		array.T
       if self.mono:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		librosa.to_mono(_A )
       if self.sampling_rate and self.sampling_rate != sampling_rate:
         __SCREAMING_SNAKE_CASE      :	Any					=		librosa.resample(_A     ,						orig_sr=_A     ,						target_sr=self.sampling_rate )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.sampling_rate

       return {"path": path, "array": array, "sampling_rate": sampling_rate}



     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       from .features import Value

       if self.decode:
         raise ValueError('''Cannot flatten a decoded Audio feature.''' )
       return {
           "bytes": Value('''binary''' ),
           "path": Value('''string''' ),
       }



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							Union[pa.StringArray, pa.StructArray] ):







       """simple docstring"""




       if pa.types.is_string(storage.type ):
         __SCREAMING_SNAKE_CASE      :	str					=		pa.array([None] * len(_A )     ,						type=pa.binary() )
         __SCREAMING_SNAKE_CASE      :	int					=		pa.StructArray.from_arrays([bytes_array, storage]     ,						['''bytes''', '''path''']     ,						mask=storage.is_null() )
       elif pa.types.is_binary(storage.type ):
         __SCREAMING_SNAKE_CASE      :	Dict					=		pa.array([None] * len(_A )     ,						type=pa.string() )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		pa.StructArray.from_arrays([storage, path_array]     ,						['''bytes''', '''path''']     ,						mask=storage.is_null() )
       elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices('''array''' ):
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		pa.array([Audio().encode_example(_A ) if x is not None else None for x in storage.to_pylist()] )
       elif pa.types.is_struct(storage.type ):
         if storage.type.get_field_index('''bytes''' ) >= 0:
           __SCREAMING_SNAKE_CASE      :	List[str]					=		storage.field('''bytes''' )
         else:
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		pa.array([None] * len(_A )     ,						type=pa.binary() )
         if storage.type.get_field_index('''path''' ) >= 0:
           __SCREAMING_SNAKE_CASE      :	Tuple					=		storage.field('''path''' )
         else:
           __SCREAMING_SNAKE_CASE      :	int					=		pa.array([None] * len(_A )     ,						type=pa.string() )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		pa.StructArray.from_arrays([bytes_array, path_array]     ,						['''bytes''', '''path''']     ,						mask=storage.is_null() )
       return array_cast(_A     ,						self.pa_type )




     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							pa.StructArray ):







       """simple docstring"""




       @no_op_if_value_is_null
       def path_to_bytes(_A							:							Tuple ):
         with xopen(_A     ,						'''rb''' ) as f:
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		f.read()
         return bytes_

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		pa.array(
           [
               (path_to_bytes(x['''path'''] ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
               for x in storage.to_pylist()
           ]     ,						type=pa.binary()     ,						)
       __SCREAMING_SNAKE_CASE      :	Tuple					=		pa.array(
           [os.path.basename(_A ) if path is not None else None for path in storage.field('''path''' ).to_pylist()]     ,						type=pa.string()     ,						)
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		pa.StructArray.from_arrays([bytes_array, path_array]     ,						['''bytes''', '''path''']     ,						mask=bytes_array.is_null() )
       return array_cast(_A     ,						self.pa_type )








"""simple docstring"""
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						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 1_3   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 3   ,				lowerCAmelCase_ = 3   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = 1_2_8   ,				lowerCAmelCase_=[1_6, 3_2, 6_4, 1_2_8]   ,				lowerCAmelCase_ = 7   ,				lowerCAmelCase_ = 4   ,				lowerCAmelCase_ = 3_7   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = 1_2_8   ,				lowerCAmelCase_ = [2, 2, 2, 2]   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    parent
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    image_size
										_SCREAMING_SNAKE_CASE   :		int       		=    patch_size
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    num_channels
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    is_training
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		Dict       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Any       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		str       		=    hidden_act
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    type_sequence_label_size
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    initializer_range
										_SCREAMING_SNAKE_CASE   :		int       		=    encoder_stride
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_attention_outputs
										_SCREAMING_SNAKE_CASE   :		Any       		=    embed_dim
										_SCREAMING_SNAKE_CASE   :		str       		=    embed_dim + 1
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    resolution
										_SCREAMING_SNAKE_CASE   :		int       		=    depths
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    hidden_sizes
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    dim
										_SCREAMING_SNAKE_CASE   :		Any       		=    mlp_expansion_ratio







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.get_config()

										return config, pixel_values, labels







						def 			A      (    self							)		->							List[str]:
										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=lowerCAmelCase_   ,				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   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    TFEfficientFormerModel(config=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.type_sequence_label_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    TFEfficientFormerForImageClassification(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(lowerCAmelCase_   ,				labels=lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)							)

										# test greyscale images
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    1
										_SCREAMING_SNAKE_CASE   :		Any       		=    TFEfficientFormerForImageClassification(lowerCAmelCase_							)

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







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.prepare_config_and_inputs()
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    config_and_inputs
										_SCREAMING_SNAKE_CASE   :		Dict       		=    {'pixel_values': pixel_values}
										return config, inputs_dict







@require_tf
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(
						    {
						        'feature-extraction': TFEfficientFormerModel,
						        'image-classification': (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						SCREAMING_SNAKE_CASE_:					List[Any] 					=				False

						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				False
						SCREAMING_SNAKE_CASE_:					List[str] 					=				False
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				False
						SCREAMING_SNAKE_CASE_:					str 					=				False







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    TFEfficientFormerModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ConfigTester(
										    self   ,				config_class=lowerCAmelCase_   ,				has_text_modality=lowerCAmelCase_   ,				hidden_size=3_7							)







						def 			A      (    self							)		->							Tuple:
										self.config_tester.run_common_tests()







						@unittest.skip(reason='EfficientFormer does not use inputs_embeds'							)
						def 			A      (    self							)		->							Dict:
										pass







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







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.prepare_config_and_inputs_for_common()

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

														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ['pixel_values']
														self.assertListEqual(arg_names[:1]   ,				lowerCAmelCase_							)







						def 			A      (    self							)		->							Union[str, Any]:
										def check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    model_class(lowerCAmelCase_							)

														_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(**self._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_							)   ,				training=lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    getattr(
														    self.model_tester   ,				'expected_num_hidden_layers'   ,				self.model_tester.num_hidden_layers + 1							)
														self.assertEqual(len(lowerCAmelCase_							)   ,				lowerCAmelCase_							)

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

																		self.asseretIsInstance(lowerCAmelCase_   ,				(list, tuple)							)
																		self.assertEqual(len(lowerCAmelCase_							)   ,				lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    getattr(self.model_tester   ,				'seq_length'   ,				lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Tuple       		=    getattr(self.model_tester   ,				'decoder_seq_length'   ,				lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		str       		=    True
														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)

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

														check_hidden_states_output(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=False							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    super()._prepare_for_class(lowerCAmelCase_   ,				lowerCAmelCase_   ,				return_labels=lowerCAmelCase_							)

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

										return inputs_dict







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase_							)







						@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'							)
						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_							)







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)







						@slow
						def 			A      (    self							)		->							List[str]:
										for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE   :		str       		=    TFEfficientFormerModel.from_pretrained(lowerCAmelCase_							)
														self.assertIsNotNone(lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.model_tester.prepare_config_and_inputs_for_common()
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    True

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    getattr(self.model_tester   ,				'seq_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    getattr(self.model_tester   ,				'encoder_seq_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    getattr(self.model_tester   ,				'key_length'   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    getattr(self.model_tester   ,				'chunk_length'   ,				lowerCAmelCase_							)

										if chunk_length is not None and hasattr(self.model_tester   ,				'num_hashes'							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    encoder_seq_length * self.model_tester.num_hashes

										for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE   :		str       		=    True
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    False
														_SCREAMING_SNAKE_CASE   :		Dict       		=    True
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model_class(lowerCAmelCase_							)

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

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

														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
														self.assertEqual(len(lowerCAmelCase_							)   ,				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							)		->							Any:
										# 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
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														# Prepare our model
														_SCREAMING_SNAKE_CASE   :		int       		=    model_class(lowerCAmelCase_							)
														# 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   :		Optional[Any]       		=    {
														    key: tf.keras.Input(shape=val.shape[1:]   ,				dtype=val.dtype   ,				name=lowerCAmelCase_							)
														    for key, val in model.input_signature.items()
														    if key in model.dummy_inputs
														}
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(lowerCAmelCase_							)
														self.assertTrue(outputs_dict is not None							)







def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				return image






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







						@cached_property
						def 			A      (    self							)		->							str:
										return (
										    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'							)
										    if is_vision_available()
										    else None
										)







						@slow
						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.default_image_processor
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		int       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='tf'							)
										# forward pass
										_SCREAMING_SNAKE_CASE   :		Dict       		=    model(**lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										# verify the logits
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.TensorShape((1, 1_0_0_0)							)
										self.assertEqual(outputs.logits.shape   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.constant([-0.0_555, 0.4_825, -0.0_852]							)
										self.assertTrue(np.allclose(outputs.logits[0, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)







						@slow
						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
										    'snap-research/efficientformer-l1-300'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.default_image_processor
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prepare_img()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    image_processor(images=lowerCAmelCase_   ,				return_tensors='tf'							)
										# forward pass
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    model(**lowerCAmelCase_   ,				training=lowerCAmelCase_							)
										# verify the logits
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.TensorShape((1, 1_0_0_0)							)
										self.assertEqual(outputs.logits.shape   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.constant([-0.1_312, 0.4_353, -1.0_499]							)
										self.assertTrue(np.allclose(outputs.logits[0, :3]   ,				lowerCAmelCase_   ,				atol=1e-4							)							)

