






import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


__UpperCamelCase      :      List[Any]     						=						NewType("""DataClass""", Any)
__UpperCamelCase      :      str     						=						NewType("""DataClassType""", Any)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if isinstance(lowerCamelCase				,  lowerCamelCase					):
		return v
	if v.lower() in ("yes", "true", "t", "y", "1"):
		return True
	elif v.lower() in ("no", "false", "f", "n", "0"):
		return False
	else:
		raise ArgumentTypeError(
		    F'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'					)





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {str(lowerCamelCase					): choice for choice in choices}
	return lambda lowerCamelCase					: str_to_choice.get(lowerCamelCase				,  lowerCamelCase					)





def 				snake_case  (     *,
    lowerCamelCase = None				,  lowerCamelCase = None				,  lowerCamelCase = dataclasses.MISSING				,  lowerCamelCase = dataclasses.MISSING				,  lowerCamelCase = None				,  **lowerCamelCase				,  ):




	'''simple docstring'''







	if metadata is None:
		# Important, don't use as default param in function signature because dict is mutable and shared across function calls
		__lowercase             =  {}
	if aliases is not None:
		__lowercase             =  aliases
	if help is not None:
		__lowercase             =  help

	return dataclasses.field(metadata=lowerCamelCase				,  default=lowerCamelCase				,  default_factory=lowerCamelCase				,  **lowerCamelCase					)




class        __UpperCamelCase    (     _lowerCAmelCase   ):
	__snake_case	:Iterable[DataClassType]







	def __init__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Union[DataClassType, Iterable[DataClassType]]       ,				**_lowerCAmelCase      :							Optional[Any]			)		-> Union[str, Any]:
		"""simple docstring"""







		if "formatter_class" not in kwargs:
			__lowercase             =  ArgumentDefaultsHelpFormatter
		super().__init__(**_lowerCAmelCase			)
		if dataclasses.is_dataclass(_lowerCAmelCase			):
			__lowercase             =  [dataclass_types]
		__lowercase             =  list(_lowerCAmelCase			)
		for dtype in self.dataclass_types:
			self._add_dataclass_arguments(_lowerCAmelCase			)







	@staticmethod
	def  _a   (  _lowerCAmelCase      :							ArgumentParser       ,				_lowerCAmelCase      :							dataclasses.Field			)		-> int:
		"""simple docstring"""







		__lowercase             =  F'--{field.name}'
		__lowercase             =  field.metadata.copy()
		# field.metadata is not used at all by Data Classes,
		# it is provided as a third-party extension mechanism.
		if isinstance(field.type       ,				_lowerCAmelCase			):
			raise RuntimeError(
			    """Unresolved type detected, which should have been done with the help of """
			    """`typing.get_type_hints` method by default"""			)

		__lowercase             =  kwargs.pop("""aliases"""       ,				[]			)
		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			__lowercase             =  [aliases]

		__lowercase             =  getattr(field.type       ,				"""__origin__"""       ,				field.type			)
		if origin_type is Union or (hasattr(_lowerCAmelCase       ,				"""UnionType"""			) and isinstance(_lowerCAmelCase       ,				types.UnionType			)):
			if str not in field.type.__args__ and (
			    len(field.type.__args__			) != 2 or type(_lowerCAmelCase			) not in field.type.__args__
			):
				raise ValueError(
				    """Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
				    """ the argument parser only supports one type per argument."""
				    F' Problem encountered in field \'{field.name}\'.'			)
			if type(_lowerCAmelCase			) not in field.type.__args__:
				# filter `str` in Union
				__lowercase             =  field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
				__lowercase             =  getattr(field.type       ,				"""__origin__"""       ,				field.type			)
			elif bool not in field.type.__args__:
				# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
				__lowercase             =  (
				    field.type.__args__[0] if isinstance(_lowerCAmelCase       ,				field.type.__args__[1]			) else field.type.__args__[1]
				)
				__lowercase             =  getattr(field.type       ,				"""__origin__"""       ,				field.type			)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
		__lowercase             =  {}
		if origin_type is Literal or (isinstance(field.type       ,				_lowerCAmelCase			) and issubclass(field.type       ,				_lowerCAmelCase			)):
			if origin_type is Literal:
				__lowercase             =  field.type.__args__
			else:
				__lowercase             =  [x.value for x in field.type]

			__lowercase             =  make_choice_type_function(kwargs["""choices"""]			)

			if field.default is not dataclasses.MISSING:
				__lowercase             =  field.default
			else:
				__lowercase             =  True
		elif field.type is bool or field.type == Optional[bool]:
			# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
			# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
			__lowercase             =  copy(_lowerCAmelCase			)

			# Hack because type=bool in argparse does not behave as we want.
			__lowercase             =  string_to_bool
			if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
				# Default value is False if we have no default when of type bool.
				__lowercase             =  False if field.default is dataclasses.MISSING else field.default
				# This is the value that will get picked if we don't include --field_name in any way
				__lowercase             =  default
				# This tells argparse we accept 0 or 1 value after --field_name
				__lowercase             =  """?"""
				# This is the value that will get picked if we do --field_name (without value)
				__lowercase             =  True
		elif isclass(_lowerCAmelCase			) and issubclass(_lowerCAmelCase       ,				_lowerCAmelCase			):
			__lowercase             =  field.type.__args__[0]
			__lowercase             =  """+"""
			if field.default_factory is not dataclasses.MISSING:
				__lowercase             =  field.default_factory()
			elif field.default is dataclasses.MISSING:
				__lowercase             =  True
		else:
			__lowercase             =  field.type
			if field.default is not dataclasses.MISSING:
				__lowercase             =  field.default
			elif field.default_factory is not dataclasses.MISSING:
				__lowercase             =  field.default_factory()
			else:
				__lowercase             =  True
		parser.add_argument(_lowerCAmelCase       ,				*_lowerCAmelCase       ,				**_lowerCAmelCase			)

		# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
		# Order is important for arguments with the same destination!
		# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
		# here and we do not need those changes/additional keys.
		if field.default is True and (field.type is bool or field.type == Optional[bool]):
			__lowercase             =  False
			parser.add_argument(F'--no_{field.name}'       ,				action="""store_false"""       ,				dest=field.name       ,				**_lowerCAmelCase			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							DataClassType			)		-> List[Any]:
		"""simple docstring"""







		if hasattr(_lowerCAmelCase       ,				"""_argument_group_name"""			):
			__lowercase             =  self.add_argument_group(dtype._argument_group_name			)
		else:
			__lowercase             =  self

		try:
			__lowercase             =  get_type_hints(_lowerCAmelCase			)
		except NameError:
			raise RuntimeError(
			    F'Type resolution failed for {dtype}. Try declaring the class in global scope or '
			    """removing line of `from __future__ import annotations` which opts in Postponed """
			    """Evaluation of Annotations (PEP 563)"""			)
		except TypeError as ex:
			# Remove this block when we drop Python 3.9 support
			if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_lowerCAmelCase			):
				__lowercase             =  """.""".join(map(_lowerCAmelCase       ,				sys.version_info[:3]			)			)
				raise RuntimeError(
				    F'Type resolution failed for {dtype} on Python {python_version}. Try removing '
				    """line of `from __future__ import annotations` which opts in union types as """
				    """`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
				    """support Python versions that lower than 3.10, you need to use """
				    """`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
				    """`X | None`."""			) from ex
			raise

		for field in dataclasses.fields(_lowerCAmelCase			):
			if not field.init:
				continue
			__lowercase             =  type_hints[field.name]
			self._parse_dataclass_field(_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							str=None       ,				_lowerCAmelCase      :							Dict=False       ,				_lowerCAmelCase      :							Optional[int]=True       ,				_lowerCAmelCase      :							Optional[int]=None       ,				_lowerCAmelCase      :							int=None       ,				)		-> Tuple[DataClass, ...]:
		"""simple docstring"""







		if args_file_flag or args_filename or (look_for_args_file and len(sys.argv			)):
			__lowercase             =  []

			if args_filename:
				args_files.append(Path(_lowerCAmelCase			)			)
			elif look_for_args_file and len(sys.argv			):
				args_files.append(Path(sys.argv[0]			).with_suffix(""".args"""			)			)

			# args files specified via command line flag should overwrite default args files so we add them last
			if args_file_flag:
				# Create special parser just to extract the args_file_flag values
				__lowercase             =  ArgumentParser()
				args_file_parser.add_argument(_lowerCAmelCase       ,				type=_lowerCAmelCase       ,				action="""append"""			)

				# Use only remaining args for further parsing (remove the args_file_flag)
				__lowercase       ,						__lowercase             =  args_file_parser.parse_known_args(args=_lowerCAmelCase			)
				__lowercase             =  vars(_lowerCAmelCase			).get(args_file_flag.lstrip("""-"""			)       ,				_lowerCAmelCase			)

				if cmd_args_file_paths:
					args_files.extend([Path(_lowerCAmelCase			) for p in cmd_args_file_paths]			)

			__lowercase             =  []
			for args_file in args_files:
				if args_file.exists():
					file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
			__lowercase             =  file_args + args if args is not None else file_args + sys.argv[1:]
		__lowercase       ,						__lowercase             =  self.parse_known_args(args=_lowerCAmelCase			)
		__lowercase             =  []
		for dtype in self.dataclass_types:
			__lowercase             =  {f.name for f in dataclasses.fields(_lowerCAmelCase			) if f.init}
			__lowercase             =  {k: v for k, v in vars(_lowerCAmelCase			).items() if k in keys}
			for k in keys:
				delattr(_lowerCAmelCase       ,				_lowerCAmelCase			)
			__lowercase             =  dtype(**_lowerCAmelCase			)
			outputs.append(_lowerCAmelCase			)
		if len(namespace.__dict__			) > 0:
			# additional namespace.
			outputs.append(_lowerCAmelCase			)
		if return_remaining_strings:
			return (*outputs, remaining_args)
		else:
			if remaining_args:
				raise ValueError(F'Some specified arguments are not used by the HfArgumentParser: {remaining_args}'			)

			return (*outputs,)







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Dict[str, Any]       ,				_lowerCAmelCase      :							bool = False			)		-> Tuple[DataClass, ...]:
		"""simple docstring"""







		__lowercase             =  set(args.keys()			)
		__lowercase             =  []
		for dtype in self.dataclass_types:
			__lowercase             =  {f.name for f in dataclasses.fields(_lowerCAmelCase			) if f.init}
			__lowercase             =  {k: v for k, v in args.items() if k in keys}
			unused_keys.difference_update(inputs.keys()			)
			__lowercase             =  dtype(**_lowerCAmelCase			)
			outputs.append(_lowerCAmelCase			)
		if not allow_extra_keys and unused_keys:
			raise ValueError(F'Some keys are not used by the HfArgumentParser: {sorted(_lowerCAmelCase			)}'			)
		return tuple(_lowerCAmelCase			)







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							bool = False			)		-> Tuple[DataClass, ...]:
		"""simple docstring"""







		with open(Path(_lowerCAmelCase			)       ,				encoding="""utf-8"""			) as open_json_file:
			__lowercase             =  json.loads(open_json_file.read()			)
		__lowercase             =  self.parse_dict(_lowerCAmelCase       ,				allow_extra_keys=_lowerCAmelCase			)
		return tuple(_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							bool = False			)		-> Tuple[DataClass, ...]:
		"""simple docstring"""







		__lowercase             =  self.parse_dict(yaml.safe_load(Path(_lowerCAmelCase			).read_text()			)       ,				allow_extra_keys=_lowerCAmelCase			)
		return tuple(_lowerCAmelCase			)









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    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							int = 13       ,				_lowerCAmelCase      :							int = 64       ,				_lowerCAmelCase      :							int = 2       ,				_lowerCAmelCase      :							int = 3       ,				_lowerCAmelCase      :							int = 3       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							bool = True       ,				_lowerCAmelCase      :							int = 128       ,				_lowerCAmelCase      :							Optional[int]=[16, 32, 64, 128]       ,				_lowerCAmelCase      :							int = 7       ,				_lowerCAmelCase      :							int = 4       ,				_lowerCAmelCase      :							int = 37       ,				_lowerCAmelCase      :							str = "gelu"       ,				_lowerCAmelCase      :							float = 0.1       ,				_lowerCAmelCase      :							float = 0.1       ,				_lowerCAmelCase      :							int = 10       ,				_lowerCAmelCase      :							float = 0.02       ,				_lowerCAmelCase      :							int = 2       ,				_lowerCAmelCase      :							int = 1       ,				_lowerCAmelCase      :							int = 128       ,				_lowerCAmelCase      :							List[int] = [2, 2, 2, 2]       ,				_lowerCAmelCase      :							int = 2       ,				_lowerCAmelCase      :							int = 2       ,				)		-> Tuple:
		"""simple docstring"""







		__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  _a   (  self      :							List[Any]			)		-> Optional[int]:
		"""simple docstring"""







		__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  _a   (  self      :							Optional[Any]			)		-> 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=_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      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Optional[int]			)		-> Optional[int]:
		"""simple docstring"""







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







	def  _a   (  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Dict       ,				_lowerCAmelCase      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







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

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

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







	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







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


@require_tf
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Any   =       (
	    (
	        TFEfficientFormerModel,
	        TFEfficientFormerForImageClassificationWithTeacher,
	        TFEfficientFormerForImageClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	__snake_case	:Any   =       (
	    {
	        'feature-extraction': TFEfficientFormerModel,
	        'image-classification': (
	            TFEfficientFormerForImageClassification,
	            TFEfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_tf_available()
	    else {}
	)

	__snake_case	:int   =       False

	__snake_case	:Optional[int]   =       False
	__snake_case	:int   =       False
	__snake_case	:Any   =       False
	__snake_case	:Any   =       False







	def  _a   (  self      :							Tuple			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  TFEfficientFormerModelTester(self			)
		__lowercase             =  ConfigTester(
		    self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase       ,				hidden_size=37			)







	def  _a   (  self      :							Optional[int]			)		-> int:
		"""simple docstring"""







		self.config_tester.run_common_tests()







	@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""			)
	def  _a   (  self      :							Optional[int]			)		-> Any:
		"""simple docstring"""







		pass







	@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""			)
	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Union[str, Any]			)		-> List[str]:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__lowercase             =  model_class(_lowerCAmelCase			)
			__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]       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Union[str, Any]			)		-> Dict:
		"""simple docstring"""







		def check_hidden_states_output(_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							List[Any]			):
			__lowercase             =  model_class(_lowerCAmelCase			)

			__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				training=_lowerCAmelCase			)
			__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(_lowerCAmelCase			)       ,				_lowerCAmelCase			)

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

				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(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)

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

			check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)







	def  _a   (  self      :							Optional[Any]       ,				_lowerCAmelCase      :							Any       ,				_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[Any]=False			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  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      :							int			)		-> int:
		"""simple docstring"""







		__lowercase             =  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      :							Union[str, Any]			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]			)		-> Union[str, Any]:
		"""simple docstring"""







		__lowercase             =  self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase			)







	@slow
	def  _a   (  self      :							List[str]			)		-> List[Any]:
		"""simple docstring"""







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







	def  _a   (  self      :							Any			)		-> List[str]:
		"""simple docstring"""







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

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

		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(_lowerCAmelCase			)

			__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				training=_lowerCAmelCase			)
			__lowercase             =  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"]
			__lowercase             =  True
			__lowercase             =  model_class(_lowerCAmelCase			)
			__lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				training=_lowerCAmelCase			)

			__lowercase             =  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      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		__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(_lowerCAmelCase			)
			# 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=_lowerCAmelCase			)
			    for key, val in model.input_signature.items()
			    if key in model.dummy_inputs
			}
			__lowercase             =  model(_lowerCAmelCase			)
			self.assertTrue(outputs_dict is not None			)





def 				snake_case  (     ):




	'''simple docstring'''







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




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







	@cached_property
	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







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







	@slow
	def  _a   (  self      :							Optional[Any]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""			)
		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""tf"""			)
		# forward pass
		__lowercase             =  model(**_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		# verify the logits
		__lowercase             =  tf.TensorShape((1, 1000)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)
		__lowercase             =  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]			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
		    """snap-research/efficientformer-l1-300"""			)
		__lowercase             =  self.default_image_processor
		__lowercase             =  prepare_img()
		__lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""tf"""			)
		# forward pass
		__lowercase             =  model(**_lowerCAmelCase       ,				training=_lowerCAmelCase			)
		# verify the logits
		__lowercase             =  tf.TensorShape((1, 1000)			)
		self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)
		__lowercase             =  tf.constant([-0.1_312, 0.4_353, -1.0_499]			)
		self.assertTrue(np.allclose(outputs.logits[0, :3]       ,				_lowerCAmelCase       ,				atol=1e-4			)			)

