

import argparse
import os
import re


a_						     =    'src/transformers'

# Pattern that looks at the indentation in a line.
a_						     =    re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
a_						     =    re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a_						     =    re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
a_						     =    re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a_						     =    re.compile(r'\[([^\]]+)\]')

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       _re_indent.search(_a)
	return "" if search is None else search.groups()[0]

def  lowerCamelCase__ (					_a							,			_a=""							,			_a=None							,			_a=None):
	SCREAMING_SNAKE_CASE   :      str				     =       0
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       code.split("\n")
	if start_prompt is not None:
		while not lines[index].startswith(_a):
			index += 1
		SCREAMING_SNAKE_CASE   :      Dict				     =       ["\n".join(lines[:index])]
	else:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       []

	# We split into blocks until we get to the `end_prompt` (or the end of the block).
	SCREAMING_SNAKE_CASE   :      Any				     =       [lines[index]]
	index += 1
	while index < len(_a) and (end_prompt is None or not lines[index].startswith(_a)):
		if len(lines[index]) > 0 and get_indent(lines[index]) == indent_level:
			if len(_a) > 0 and get_indent(current_block[-1]).startswith(indent_level + " "):
				current_block.append(lines[index])
				blocks.append("\n".join(_a))
				if index < len(_a) - 1:
					SCREAMING_SNAKE_CASE   :      str				     =       [lines[index + 1]]
					index += 1
				else:
					SCREAMING_SNAKE_CASE   :      int				     =       []
			else:
				blocks.append("\n".join(_a))
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [lines[index]]
		else:
			current_block.append(lines[index])
		index += 1

	# Adds current block if it's nonempty.
	if len(_a) > 0:
		blocks.append("\n".join(_a))

	# Add final block after end_prompt if provided.
	if end_prompt is not None and index < len(_a):
		blocks.append("\n".join(lines[index:]))

	return blocks

def  lowerCamelCase__ (					_a):

	def _inner(_a):
		return key(_a).lower().replace("_"							,			"")

	return _inner

def  lowerCamelCase__ (					_a							,			_a=None):

	# If no key is provided, we use a noop.
	def noop(_a):
		return x

	if key is None:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       noop
	# Constants are all uppercase, they go first.
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [obj for obj in objects if key(_a).isupper()]
	# Classes are not all uppercase but start with a capital, they go second.
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [obj for obj in objects if key(_a)[0].isupper() and not key(_a).isupper()]
	# Functions begin with a lowercase, they go last.
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       [obj for obj in objects if not key(_a)[0].isupper()]

	SCREAMING_SNAKE_CASE   :      Tuple				     =       ignore_underscore(_a)
	return sorted(_a							,			key=_a) + sorted(_a							,			key=_a) + sorted(_a							,			key=_a)

def  lowerCamelCase__ (					_a):

	# This inner function sort imports between [ ].
	def _replace(_a):
		SCREAMING_SNAKE_CASE   :      int				     =       match.groups()[0]
		if "," not in imports:
			return f"[{imports}]"
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [part.strip().replace("\""							,			"") for part in imports.split(",")]
		# We will have a final empty element if the line finished with a comma.
		if len(keys[-1]) == 0:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       keys[:-1]
		return "[" + ", ".join([f"\"{k}\"" for k in sort_objects(_a)]) + "]"

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       import_statement.split("\n")
	if len(_a) > 3:
		# Here we have to sort internal imports that are on several lines (one per name):
		# key: [
		#     "object1",
		#     "object2",
		#     ...
		# ]

		# We may have to ignore one or two lines on each side.
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       2 if lines[1].strip() == "[" else 1
		SCREAMING_SNAKE_CASE   :      Any				     =       [(i, _re_strip_line.search(_a).groups()[0]) for i, line in enumerate(lines[idx:-idx])]
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       sort_objects(_a							,			key=lambda _a: x[1])
		SCREAMING_SNAKE_CASE   :      Dict				     =       [lines[x[0] + idx] for x in sorted_indices]
		return "\n".join(lines[:idx] + sorted_lines + lines[-idx:])
	elif len(_a) == 3:
		# Here we have to sort internal imports that are on one separate line:
		# key: [
		#     "object1", "object2", ...
		# ]
		if _re_bracket_content.search(lines[1]) is not None:
			SCREAMING_SNAKE_CASE   :      Any				     =       _re_bracket_content.sub(_replace							,			lines[1])
		else:
			SCREAMING_SNAKE_CASE   :      Tuple				     =       [part.strip().replace("\""							,			"") for part in lines[1].split(",")]
			# We will have a final empty element if the line finished with a comma.
			if len(keys[-1]) == 0:
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       keys[:-1]
			SCREAMING_SNAKE_CASE   :      Dict				     =       get_indent(lines[1]) + ", ".join([f"\"{k}\"" for k in sort_objects(_a)])
		return "\n".join(_a)
	else:
		# Finally we have to deal with imports fitting on one line
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       _re_bracket_content.sub(_replace							,			_a)
		return import_statement

def  lowerCamelCase__ (					_a							,			_a=True):
	with open(_a							,			encoding="utf-8") as f:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	SCREAMING_SNAKE_CASE   :      str				     =       split_code_in_indented_blocks(
	    _a							,			start_prompt="_import_structure = {"							,			end_prompt="if TYPE_CHECKING:")

	# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
	for block_idx in range(1							,			len(_a) - 1):
		# Check if the block contains some `_import_structure`s thingy to sort.
		SCREAMING_SNAKE_CASE   :      Dict				     =       main_blocks[block_idx]
		SCREAMING_SNAKE_CASE   :      str				     =       block.split("\n")

		# Get to the start of the imports.
		SCREAMING_SNAKE_CASE   :      Any				     =       0
		while line_idx < len(_a) and "_import_structure" not in block_lines[line_idx]:
			# Skip dummy import blocks
			if "import dummy" in block_lines[line_idx]:
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       len(_a)
			else:
				line_idx += 1
		if line_idx >= len(_a):
			continue

		# Ignore beginning and last line: they don't contain anything.
		SCREAMING_SNAKE_CASE   :      Dict				     =       "\n".join(block_lines[line_idx:-1])
		SCREAMING_SNAKE_CASE   :      str				     =       get_indent(block_lines[1])
		# Slit the internal block into blocks of indent level 1.
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       split_code_in_indented_blocks(_a							,			indent_level=_a)
		# We have two categories of import key: list or _import_structure[key].append/extend
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       _re_direct_key if "_import_structure = {" in block_lines[0] else _re_indirect_key
		# Grab the keys, but there is a trap: some lines are empty or just comments.
		SCREAMING_SNAKE_CASE   :      Any				     =       [(pattern.search(_a).groups()[0] if pattern.search(_a) is not None else None) for b in internal_blocks]
		# We only sort the lines with a key.
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       [(i, key) for i, key in enumerate(_a) if key is not None]
		SCREAMING_SNAKE_CASE   :      Tuple				     =       [x[0] for x in sorted(_a							,			key=lambda _a: x[1])]

		# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       0
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       []
		for i in range(len(_a)):
			if keys[i] is None:
				reorderded_blocks.append(internal_blocks[i])
			else:
				SCREAMING_SNAKE_CASE   :      List[str]				     =       sort_objects_in_import(internal_blocks[sorted_indices[count]])
				reorderded_blocks.append(_a)
				count += 1

        # And we put our main block back together with its first and last line.
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "\n".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]])

	if code != "\n".join(_a):
		if check_only:
			return True
		else:
			print(f"Overwriting {file}.")
			with open(_a							,			"w"							,			encoding="utf-8") as f:
				f.write("\n".join(_a))

def  lowerCamelCase__ (					_a=True):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
	for root, _, files in os.walk(_a):
		if "__init__.py" in files:
			SCREAMING_SNAKE_CASE   :      int				     =       sort_imports(os.path.join(_a							,			"__init__.py")							,			check_only=_a)
			if result:
				SCREAMING_SNAKE_CASE   :      int				     =       [os.path.join(_a							,			"__init__.py")]
	if len(_a) > 0:
		raise ValueError(f"Would overwrite {len(_a)} files, run `make style`.")


if __name__ == "__main__":
			a_						     =    argparse.ArgumentParser()
			parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
			a_						     =    parser.parse_args()

			sort_imports_in_all_inits(check_only=args.check_only)


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							: int      ,      a							: Optional[Any]      ,      a							: int = 13      ,      a							: int = 64      ,      a							: int = 2      ,      a							: int = 3      ,      a							: int = 3      ,      a							: bool = True      ,      a							: bool = True      ,      a							: int = 128      ,      a							: Any=[16, 32, 64, 128]      ,      a							: int = 7      ,      a							: int = 4      ,      a							: int = 37      ,      a							: str = "gelu"      ,      a							: float = 0.1      ,      a							: float = 0.1      ,      a							: int = 10      ,      a							: float = 0.02      ,      a							: int = 2      ,      a							: int = 1      ,      a							: int = 128      ,      a							: List[int] = [2, 2, 2, 2]      ,      a							: int = 2      ,      a							: int = 2      ,      )							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       parent
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      str				     =       image_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       patch_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_channels
				SCREAMING_SNAKE_CASE   :      int				     =       is_training
				SCREAMING_SNAKE_CASE   :      str				     =       use_labels
				SCREAMING_SNAKE_CASE   :      Tuple				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      Tuple				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      int				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      str				     =       encoder_stride
				SCREAMING_SNAKE_CASE   :      Dict				     =       num_attention_outputs
				SCREAMING_SNAKE_CASE   :      Any				     =       embed_dim
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       embed_dim + 1
				SCREAMING_SNAKE_CASE   :      Dict				     =       resolution
				SCREAMING_SNAKE_CASE   :      str				     =       depths
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hidden_sizes
				SCREAMING_SNAKE_CASE   :      str				     =       dim
				SCREAMING_SNAKE_CASE   :      List[str]				     =       mlp_expansion_ratio
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				SCREAMING_SNAKE_CASE   :      int				     =       None
				if self.use_labels:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ids_tensor([self.batch_size]      ,      self.type_sequence_label_size	)

				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_config()

				return config, pixel_values, labels
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Any:

				"""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 							__UpperCamelCase (			self							: Any      ,      a							: Union[str, Any]      ,      a							: Tuple      ,      a							: Union[str, Any]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       TFEfficientFormerModel(config=a	)
				SCREAMING_SNAKE_CASE   :      int				     =       model(a      ,      training=a	)
				self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: str      ,      a							: Union[str, Any]      ,      a							: Dict	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      Tuple				     =       TFEfficientFormerForImageClassification(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       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   :      Dict				     =       1
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       TFEfficientFormerForImageClassification(a	)

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






			def 							__UpperCamelCase (			self							: List[str]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       config_and_inputs
				SCREAMING_SNAKE_CASE   :      Tuple				     =       {"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__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 							__UpperCamelCase (			self							: List[Any]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       TFEfficientFormerModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       ConfigTester(
				    self      ,      config_class=a      ,      has_text_modality=a      ,      hidden_size=37	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			@unittest.skip(reason="EfficientFormer does not use inputs_embeds"	)
			def 							__UpperCamelCase (			self							: List[str]	)							->       str:

				"""simple docstring"""

				pass
			@unittest.skip(reason="EfficientFormer does not support input and output embeddings"	)
			def 							__UpperCamelCase (			self							: Any	)							->       Dict:

				"""simple docstring"""

				pass
			def 							__UpperCamelCase (			self							: Tuple	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

					SCREAMING_SNAKE_CASE   :      Tuple				     =       ["pixel_values"]
					self.assertListEqual(arg_names[:1]      ,      a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Optional[Any]:

				"""simple docstring"""

				def check_hidden_states_output(a							: Optional[int]      ,      a							: Any      ,      a							: List[Any]	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      str				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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   :      Any				     =       self.model_tester.encoder_seq_length
						if hasattr(self.model_tester      ,      "chunk_length"	) and self.model_tester.chunk_length > 1:
							SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       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   :      Optional[Any]				     =       outputs.decoder_hidden_states

						self.asseretIsInstance(a      ,      (list, tuple)	)
						self.assertEqual(len(a	)      ,      a	)
						SCREAMING_SNAKE_CASE   :      Tuple				     =       getattr(self.model_tester      ,      "seq_length"      ,      a	)
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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   :      Any				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      int				     =       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   :      str				     =       True

					check_hidden_states_output(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: Optional[int]      ,      a							: List[str]      ,      a							: Optional[Any]=False	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       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 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       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 							__UpperCamelCase (			self							: Any	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*a	)
			def 							__UpperCamelCase (			self							: Dict	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a	)
			@slow
			def 							__UpperCamelCase (			self							: int	)							->       Any:

				"""simple docstring"""

				for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       TFEfficientFormerModel.from_pretrained(a	)
					self.assertIsNotNone(a	)
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True

				SCREAMING_SNAKE_CASE   :      str				     =       getattr(self.model_tester      ,      "seq_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       getattr(self.model_tester      ,      "encoder_seq_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       getattr(self.model_tester      ,      "key_length"      ,      a	)
				SCREAMING_SNAKE_CASE   :      int				     =       getattr(self.model_tester      ,      "chunk_length"      ,      a	)

				if chunk_length is not None and hasattr(self.model_tester      ,      "num_hashes"	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       encoder_seq_length * self.model_tester.num_hashes

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Any				     =       True
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       False
					SCREAMING_SNAKE_CASE   :      Tuple				     =       True
					SCREAMING_SNAKE_CASE   :      Any				     =       model_class(a	)

					SCREAMING_SNAKE_CASE   :      Tuple				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       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   :      Union[str, Any]				     =       True
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class(a	)
					SCREAMING_SNAKE_CASE   :      Any				     =       model(**self._prepare_for_class(a      ,      a	)      ,      training=a	)

					SCREAMING_SNAKE_CASE   :      List[Any]				     =       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 							__UpperCamelCase (			self							: Optional[int]	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					# Prepare our model
					SCREAMING_SNAKE_CASE   :      str				     =       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   :      Any				     =       {
					    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   :      Optional[int]				     =       model(a	)
					self.assertTrue(outputs_dict is not None	)

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@cached_property
			def 							__UpperCamelCase (			self							: Dict	)							->       Dict:

				"""simple docstring"""

				return (
				    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"	)
				    if is_vision_available()
				    else None
				)
			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       List[str]:

				"""simple docstring"""

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






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

				"""simple docstring"""

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