

import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


a_						     =    Mapping[str, np.ndarray]
a_						     =    Mapping[str, Any]  # Is a nested dict.
a_						     =    0.01






@dataclasses.dataclass(frozen=__A   )
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =42  # [num_res, num_atom_type, 3]

			# Amino-acid type for each residue represented as an integer between 0 and
			# 20, where 20 is 'X'.
			lowerCamelCase__           =42  # [num_res]

			# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
			# is present and 0.0 if not. This should be used for loss masking.
			lowerCamelCase__           =42  # [num_res, num_atom_type]

			# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
			lowerCamelCase__           =42  # [num_res]

			# B-factors, or temperature factors, of each residue (in sq. angstroms units),
			# representing the displacement of the residue from its ground truth mean
			# value.
			lowerCamelCase__           =42  # [num_res, num_atom_type]

			# Chain indices for multi-chain predictions
			lowerCamelCase__           =None

			# Optional remark about the protein. Included as a comment in output PDB
			# files
			lowerCamelCase__           =None

			# Templates used to generate this protein (prediction-only)
			lowerCamelCase__           =None

			# Chain corresponding to each parent
			lowerCamelCase__           =None

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       r"(\[[A-Z]+\]\n)"
	SCREAMING_SNAKE_CASE   :      List[str]				     =       [tag.strip() for tag in re.split(_a							,			_a) if len(_a) > 0]
	SCREAMING_SNAKE_CASE   :      Iterator[Tuple[str, List[str]]]				     =       zip(tags[0::2]							,			[l.split("\n") for l in tags[1::2]])

	SCREAMING_SNAKE_CASE   :      List[str]				     =       ["N", "CA", "C"]
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       None
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       None
	SCREAMING_SNAKE_CASE   :      Any				     =       None
	for g in groups:
		if "[PRIMARY]" == g[0]:
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       g[1][0].strip()
			for i in range(len(_a)):
				if seq[i] not in residue_constants.restypes:
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "X"  # FIXME: strings are immutable
			SCREAMING_SNAKE_CASE   :      str				     =       np.array(
			    [residue_constants.restype_order.get(_a							,			residue_constants.restype_num) for res_symbol in seq])
		elif "[TERTIARY]" == g[0]:
			SCREAMING_SNAKE_CASE   :      List[List[float]]				     =       []
			for axis in range(3):
				tertiary.append(list(map(_a							,			g[1][axis].split())))
			SCREAMING_SNAKE_CASE   :      str				     =       np.array(_a)
			SCREAMING_SNAKE_CASE   :      int				     =       np.zeros((len(tertiary[0]) // 3, residue_constants.atom_type_num, 3)).astype(np.floataa)
			for i, atom in enumerate(_a):
				SCREAMING_SNAKE_CASE   :      Tuple				     =       np.transpose(tertiary_np[:, i::3])
			atom_positions *= PICO_TO_ANGSTROM
		elif "[MASK]" == g[0]:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       np.array(list(map({"-": 0, "+": 1}.get							,			g[1][0].strip())))
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(
			    (
			        len(_a),
			        residue_constants.atom_type_num,
			    )).astype(np.floataa)
			for i, atom in enumerate(_a):
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       1
			atom_mask *= mask[..., None]

	assert aatype is not None

	return Protein(
	    atom_positions=_a							,			atom_mask=_a							,			aatype=_a							,			residue_index=np.arange(len(_a))							,			b_factors=_a							,			)

def  lowerCamelCase__ (					_a							,			_a = 0):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       []

	SCREAMING_SNAKE_CASE   :      Tuple				     =       prot.remark
	if remark is not None:
		pdb_headers.append(f"REMARK {remark}")

	SCREAMING_SNAKE_CASE   :      Any				     =       prot.parents
	SCREAMING_SNAKE_CASE   :      Tuple				     =       prot.parents_chain_index
	if parents is not None and parents_chain_index is not None:
		SCREAMING_SNAKE_CASE   :      int				     =       [p for i, p in zip(_a							,			_a) if i == chain_id]

	if parents is None or len(_a) == 0:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       ["N/A"]

	pdb_headers.append(f"PARENT {' '.join(_a)}")

	return pdb_headers

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[str]				     =       []
	SCREAMING_SNAKE_CASE   :      Dict				     =       pdb_str.split("\n")

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       prot.remark
	if remark is not None:
		out_pdb_lines.append(f"REMARK {remark}")

	SCREAMING_SNAKE_CASE   :      List[List[str]]
	if prot.parents is not None and len(prot.parents) > 0:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       []
		if prot.parents_chain_index is not None:
			SCREAMING_SNAKE_CASE   :      Dict[str, List[str]]				     =       {}
			for p, i in zip(prot.parents							,			prot.parents_chain_index):
				parent_dict.setdefault(str(_a)							,			[])
				parent_dict[str(_a)].append(_a)

			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max([int(_a) for chain_idx in parent_dict])
			for i in range(max_idx + 1):
				SCREAMING_SNAKE_CASE   :      Any				     =       parent_dict.get(str(_a)							,			["N/A"])
				parents_per_chain.append(_a)
		else:
			parents_per_chain.append(list(prot.parents))
	else:
		SCREAMING_SNAKE_CASE   :      str				     =       [["N/A"]]

	def make_parent_line(_a) -> str:
		return f"PARENT {' '.join(_a)}"

	out_pdb_lines.append(make_parent_line(parents_per_chain[0]))

	SCREAMING_SNAKE_CASE   :      List[Any]				     =       0
	for i, l in enumerate(_a):
		if "PARENT" not in l and "REMARK" not in l:
			out_pdb_lines.append(_a)
		if "TER" in l and "END" not in lines[i + 1]:
			chain_counter += 1
			if not chain_counter >= len(_a):
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       parents_per_chain[chain_counter]
			else:
				SCREAMING_SNAKE_CASE   :      str				     =       ["N/A"]

			out_pdb_lines.append(make_parent_line(_a))

	return "\n".join(_a)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       residue_constants.restypes + ["X"]

	def res_atoa(_a) -> str:
		return residue_constants.restype_atoa.get(restypes[r]							,			"UNK")

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       residue_constants.atom_types

	SCREAMING_SNAKE_CASE   :      List[str]				     =       []

	SCREAMING_SNAKE_CASE   :      Tuple				     =       prot.atom_mask
	SCREAMING_SNAKE_CASE   :      Dict				     =       prot.aatype
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       prot.atom_positions
	SCREAMING_SNAKE_CASE   :      List[str]				     =       prot.residue_index.astype(np.intaa)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       prot.b_factors
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       prot.chain_index

	if np.any(aatype > residue_constants.restype_num):
		raise ValueError("Invalid aatypes.")

	SCREAMING_SNAKE_CASE   :      int				     =       get_pdb_headers(_a)
	if len(_a) > 0:
		pdb_lines.extend(_a)

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       aatype.shape[0]
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       1
	SCREAMING_SNAKE_CASE   :      Dict				     =       0
	SCREAMING_SNAKE_CASE   :      Any				     =       string.ascii_uppercase
	SCREAMING_SNAKE_CASE   :      Dict				     =       None
	# Add all atom sites.
	for i in range(_a):
		SCREAMING_SNAKE_CASE   :      Any				     =       res_atoa(aatype[i])
		for atom_name, pos, mask, b_factor in zip(_a							,			atom_positions[i]							,			atom_mask[i]							,			b_factors[i]):
			if mask < 0.5:
				continue

			SCREAMING_SNAKE_CASE   :      Dict				     =       "ATOM"
			SCREAMING_SNAKE_CASE   :      List[str]				     =       atom_name if len(_a) == 4 else f" {atom_name}"
			SCREAMING_SNAKE_CASE   :      Tuple				     =       ""
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       ""
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       1.00
			SCREAMING_SNAKE_CASE   :      int				     =       atom_name[0]  # Protein supports only C, N, O, S, this works.
			SCREAMING_SNAKE_CASE   :      Dict				     =       ""

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "A"
			if chain_index is not None:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       chain_tags[chain_index[i]]

			# PDB is a columnar format, every space matters here!
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       (
			    f"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
			    f"{res_name_a:>3} {chain_tag:>1}"
			    f"{residue_index[i]:>4}{insertion_code:>1}   "
			    f"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
			    f"{occupancy:>6.2f}{b_factor:>6.2f}          "
			    f"{element:>2}{charge:>2}"
			)
			pdb_lines.append(_a)
			atom_index += 1

		SCREAMING_SNAKE_CASE   :      int				     =       i == n - 1
		if chain_index is not None:
			if i != n - 1 and chain_index[i + 1] != prev_chain_index:
				SCREAMING_SNAKE_CASE   :      Any				     =       True
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       chain_index[i + 1]

		if should_terminate:
			# Close the chain.
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "TER"
			SCREAMING_SNAKE_CASE   :      Tuple				     =       (
			    f"{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]):>3} {chain_tag:>1}{residue_index[i]:>4}"
			)
			pdb_lines.append(_a)
			atom_index += 1

			if i != n - 1:
				# "prev" is a misnomer here. This happens at the beginning of
				# each new chain.
				pdb_lines.extend(get_pdb_headers(_a							,			_a))

	pdb_lines.append("END")
	pdb_lines.append("")
	return "\n".join(_a)

def  lowerCamelCase__ (					_a):
	return residue_constants.STANDARD_ATOM_MASK[prot.aatype]


def  lowerCamelCase__ (					_a							,			_a							,			_a = None							,			_a = None							,			_a = None							,			_a = None							,			_a = None							,			):
	return Protein(
	    aatype=features["aatype"]							,			atom_positions=result["final_atom_positions"]							,			atom_mask=result["final_atom_mask"]							,			residue_index=features["residue_index"] + 1							,			b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"])							,			chain_index=_a							,			remark=_a							,			parents=_a							,			parents_chain_index=_a							,			)


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	)	)