

import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


A_							:			Optional[Any]											= logging.get_logger(__name__)

A_							:			int											= list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A_							:			List[str]											= tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		List[Any]							=							field(
			    default=A__			,metadata={'help': 'Model type selected in the list: ' + ', '.join(A__			)}			)
			lowerCamelCase__   :		List[Any]							=							field(
			    default=A__			,metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}			)
			lowerCamelCase__   :		Any							=							field(
			    default=1_2_8			,metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }			,)
			lowerCamelCase__   :		str							=							field(
			    default=1_2_8			,metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}			,)
			lowerCamelCase__   :		int							=							field(
			    default=6_4			,metadata={
			        'help': (
			            'The maximum number of tokens for the question. Questions longer than this will '
			            'be truncated to this length.'
			        )
			    }			,)
			lowerCamelCase__   :		Tuple							=							field(
			    default=3_0			,metadata={
			        'help': (
			            'The maximum length of an answer that can be generated. This is needed because the start '
			            'and end predictions are not conditioned on one another.'
			        )
			    }			,)
			lowerCamelCase__   :		List[str]							=							field(
			    default=A__			,metadata={'help': 'Overwrite the cached training and evaluation sets'}			)
			lowerCamelCase__   :		str							=							field(
			    default=A__			,metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}			)
			lowerCamelCase__   :		Dict							=							field(
			    default=0.0			,metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}			)
			lowerCamelCase__   :		Optional[Any]							=							field(
			    default=2_0			,metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}			)
			lowerCamelCase__   :		Tuple							=							field(
			    default=0			,metadata={
			        'help': (
			            'language id of input for language-specific xlm models (see'
			            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
			        )
			    }			,)
			lowerCamelCase__   :		Optional[int]							=							field(default=1			,metadata={'help': 'multiple threads for converting example to features'}			)





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Optional[Any]							=							'train'
			lowerCamelCase__   :		Optional[Any]							=							'dev'





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							4_2
			lowerCamelCase__   :		Any							=							4_2
			lowerCamelCase__   :		List[Any]							=							4_2
			lowerCamelCase__   :		List[str]							=							4_2

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						SquadDataTrainingArguments    ,				__UpperCAmelCase				:						PreTrainedTokenizer    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Union[str, Split] = Split.train    ,				__UpperCAmelCase				:						Optional[bool] = False    ,				__UpperCAmelCase				:						Optional[str] = None    ,				__UpperCAmelCase				:						Optional[str] = "pt"    ,				)   ->    Any:
						SCREAMING_SNAKE_CASE__								=      args
						SCREAMING_SNAKE_CASE__								=      is_language_sensitive
						SCREAMING_SNAKE_CASE__								=      SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									try:
												SCREAMING_SNAKE_CASE__								=      Split[mode]
									except KeyError:
												raise KeyError("""mode is not a valid split name"""       )
						SCREAMING_SNAKE_CASE__								=      mode
						# Load data features from cache or dataset file
						SCREAMING_SNAKE_CASE__								=      'v2' if args.version_2_with_negative else 'v1'
						SCREAMING_SNAKE_CASE__								=      os.path.join(
						    cache_dir if cache_dir is not None else args.data_dir    ,				F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""    ,				)

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						SCREAMING_SNAKE_CASE__								=      cached_features_file + '.lock'
						with FileLock(__UpperCAmelCase       ):
									if os.path.exists(__UpperCAmelCase       ) and not args.overwrite_cache:
												SCREAMING_SNAKE_CASE__								=      time.time()
												SCREAMING_SNAKE_CASE__								=      torch.load(__UpperCAmelCase       )

												# Legacy cache files have only features, while new cache files
												# will have dataset and examples also.
												SCREAMING_SNAKE_CASE__								=      self.old_features['features']
												SCREAMING_SNAKE_CASE__								=      self.old_features.get("""dataset"""    ,				__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      self.old_features.get("""examples"""    ,				__UpperCAmelCase       )
												logger.info(
												    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""    ,				time.time() - start       )

												if self.dataset is None or self.examples is None:
															logger.warning(
															    F"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
															    """ future run"""       )
									else:
												if mode == Split.dev:
															SCREAMING_SNAKE_CASE__								=      self.processor.get_dev_examples(args.data_dir       )
												else:
															SCREAMING_SNAKE_CASE__								=      self.processor.get_train_examples(args.data_dir       )

												SCREAMING_SNAKE_CASE__								=      squad_convert_examples_to_features(
												    examples=self.examples    ,				tokenizer=__UpperCAmelCase    ,				max_seq_length=args.max_seq_length    ,				doc_stride=args.doc_stride    ,				max_query_length=args.max_query_length    ,				is_training=mode == Split.train    ,				threads=args.threads    ,				return_dataset=__UpperCAmelCase    ,				)

												SCREAMING_SNAKE_CASE__								=      time.time()
												torch.save(
												    {"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples}    ,				__UpperCAmelCase    ,				)
												# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
												logger.info(
												    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""       )

			def __len__(					self				:						Union[str, Any]       )   ->    int:
						return len(self.features       )





			def __getitem__(					self				:						str    ,				__UpperCAmelCase				:						int       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.features[i]

						SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.input_ids    ,				dtype=torch.long       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.attention_mask    ,				dtype=torch.long       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.token_type_ids    ,				dtype=torch.long       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.cls_index    ,				dtype=torch.long       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.p_mask    ,				dtype=torch.float       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.is_impossible    ,				dtype=torch.float       )

						SCREAMING_SNAKE_CASE__								=      {
						    'input_ids': input_ids,
						    'attention_mask': attention_mask,
						    'token_type_ids': token_type_ids,
						}

						if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
									del inputs["token_type_ids"]

						if self.args.model_type in ["xlnet", "xlm"]:
									inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask}       )
									if self.args.version_2_with_negative:
												inputs.update({"""is_impossible""": is_impossible}       )
									if self.is_language_sensitive:
												inputs.update({"""langs""": (torch.ones(input_ids.shape    ,				dtype=torch.intaa       ) * self.args.lang_id)}       )

						if self.mode == Split.train:
									SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.start_position    ,				dtype=torch.long       )
									SCREAMING_SNAKE_CASE__								=      torch.tensor(feature.end_position    ,				dtype=torch.long       )
									inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions}       )

						return inputs





"""simple docstring"""




import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFEfficientFormerForImageClassification,
						    TFEfficientFormerForImageClassificationWithTeacher,
						    TFEfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_tf_efficientformer import (
						    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor





class    lowerCamelCase						:

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						SCREAMING_SNAKE_CASE__								=      None
						if self.use_labels:
									SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size       )

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Any       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassification(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase    ,				training=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerForImageClassification(__UpperCAmelCase       )

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_tf
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Any							=							(
			    (
			        TFEfficientFormerModel,
			        TFEfficientFormerForImageClassificationWithTeacher,
			        TFEfficientFormerForImageClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			lowerCamelCase__   :		List[Any]							=							(
			    {
			        'feature-extraction': TFEfficientFormerModel,
			        'image-classification': (
			            TFEfficientFormerForImageClassification,
			            TFEfficientFormerForImageClassificationWithTeacher,
			        ),
			    }
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__   :		Dict							=							False

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      TFEfficientFormerModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(
						    self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Any:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
						pass

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    str:
						def check_hidden_states_output(__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int       ):
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )

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

									SCREAMING_SNAKE_CASE__								=      getattr(
									    self.model_tester    ,				"""expected_num_hidden_layers"""    ,				self.model_tester.num_hidden_layers + 1       )
									self.assertEqual(len(__UpperCAmelCase       )    ,				__UpperCAmelCase       )

									if hasattr(self.model_tester    ,				"""encoder_seq_length"""       ):
												SCREAMING_SNAKE_CASE__								=      self.model_tester.encoder_seq_length
												if hasattr(self.model_tester    ,				"""chunk_length"""       ) and self.model_tester.chunk_length > 1:
															SCREAMING_SNAKE_CASE__								=      seq_length * self.model_tester.chunk_length
									else:
												SCREAMING_SNAKE_CASE__								=      self.model_tester.seq_length

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

									if config.is_encoder_decoder:
												SCREAMING_SNAKE_CASE__								=      outputs.decoder_hidden_states

												self.asseretIsInstance(__UpperCAmelCase    ,				(list, tuple)       )
												self.assertEqual(len(__UpperCAmelCase       )    ,				__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""seq_length"""    ,				__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""decoder_seq_length"""    ,				__UpperCAmelCase       )

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

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

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

									check_hidden_states_output(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Any=False       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

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

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
						for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      TFEfficientFormerModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__								=      True

						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""seq_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""encoder_seq_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""key_length"""    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      getattr(self.model_tester    ,				"""chunk_length"""    ,				__UpperCAmelCase       )

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

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      False
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )

									SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase       )    ,				training=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase       )    ,				self.model_tester.num_attention_outputs       )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE__								=      True
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase       )    ,				training=__UpperCAmelCase       )

									SCREAMING_SNAKE_CASE__								=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
									self.assertEqual(len(__UpperCAmelCase       )    ,				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 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						# We use a simplified version of this test for EfficientFormer because it requires training=False
						# and Keras refuses to let us force that during functional construction
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									# Prepare our model
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									# These are maximally general inputs for the model, with multiple None dimensions
									# Hopefully this will catch any conditionals that fail for flexible shapes
									SCREAMING_SNAKE_CASE__								=      {
									    key: tf.keras.Input(shape=val.shape[1:]    ,				dtype=val.dtype    ,				name=__UpperCAmelCase       )
									    for key, val in model.input_signature.items()
									    if key in model.dummy_inputs
									}
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
									self.assertTrue(outputs_dict is not None       )

def 	A       (     ):




			'''simple docstring'''


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





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Dict:
						return (
						    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""       )
						    if is_vision_available()
						    else None
						)

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





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


