





'''simple docstring'''
import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
    )
    from transformers.models.efficientformer.modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
    )


if is_vision_available():
    from PIL import Image

    from transformers import EfficientFormerImageProcessor

class 					snake_case					:




 """simple docstring"""



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






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

        __UpperCamelCase	=  self.get_config()
        return config, pixel_values, labels






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        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 	_lowerCamelCase (	self	:							str							,     __A	:							int							,     __A	:							Union[str, Any]							,     __A	:							Dict     ):
        __UpperCamelCase	=  EfficientFormerModel(config=__A     )
        model.to(__A     )
        model.eval()
        __UpperCamelCase	=  model(__A     )
        self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)     )






 def 	_lowerCamelCase (	self	:							Optional[Any]							,     __A	:							List[str]							,     __A	:							Optional[int]							,     __A	:							Optional[Any]     ):
        __UpperCamelCase	=  self.type_sequence_label_size
        __UpperCamelCase	=  EfficientFormerForImageClassification(__A     )
        model.to(__A     )
        model.eval()
        __UpperCamelCase	=  model(__A							,     labels=__A     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.type_sequence_label_size)     )

        # test greyscale images
        __UpperCamelCase	=  1
        __UpperCamelCase	=  EfficientFormerForImageClassification(__A     )
        model.to(__A     )
        model.eval()

        __UpperCamelCase	=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
        __UpperCamelCase	=  model(__A     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.type_sequence_label_size)     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						(
            __UpperCamelCase
        )	,						
        )	=  config_and_inputs
        __UpperCamelCase	=  {'pixel_values': pixel_values}
        return config, inputs_dict






@require_torch
class 					snake_case					(   __lowerCamelCase      ,	__lowerCamelCase      ,	unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=(
     (
         EfficientFormerModel,
         EfficientFormerForImageClassificationWithTeacher,
         EfficientFormerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_     : Tuple      		=(
     {
         "feature-extraction": EfficientFormerModel,
         "image-classification": (
             EfficientFormerForImageClassification,
             EfficientFormerForImageClassificationWithTeacher,
         ),
     }
     if is_torch_available()
     else {}
 )
 SCREAMING_SNAKE_CASE_     : Tuple      		=False

 SCREAMING_SNAKE_CASE_     : str      		=False
 SCREAMING_SNAKE_CASE_     : str      		=False
 SCREAMING_SNAKE_CASE_     : Any      		=False






 def 	_lowerCamelCase (	self	:							Dict     ):
        __UpperCamelCase	=  EfficientFormerModelTester(self     )
        __UpperCamelCase	=  ConfigTester(
            self							,     config_class=__A							,     has_text_modality=__A							,     hidden_size=3_7     )






 def 	_lowerCamelCase (	self	:							int     ):
        self.config_tester.run_common_tests()






 @unittest.skip(reason='EfficientFormer does not use inputs_embeds'     )
 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        pass






 @unittest.skip(reason='EfficientFormer does not support input and output embeddings'     )
 def 	_lowerCamelCase (	self	:							List[str]     ):
        pass






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

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

               __UpperCamelCase	=  ['pixel_values']
               self.assertListEqual(arg_names[:1]							,     __A     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        def check_hidden_states_output(__A	:							Optional[int]							,     __A	:							List[Any]							,     __A	:							Union[str, Any]     ):
               __UpperCamelCase	=  model_class(__A     )
               model.to(__A     )
               model.eval()

               with torch.no_grad():
                      __UpperCamelCase	=  model(**self._prepare_for_class(__A							,     __A     )     )

               __UpperCamelCase	=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               __UpperCamelCase	=  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'     ):
                      __UpperCamelCase	=  self.model_tester.encoder_seq_length
                      if hasattr(self.model_tester							,     'chunk_length'     ) and self.model_tester.chunk_length > 1:
                             __UpperCamelCase	=  seq_length * self.model_tester.chunk_length
               else:
                      __UpperCamelCase	=  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:
                      __UpperCamelCase	=  outputs.decoder_hidden_states

                      self.assertIsInstance(__A							,     (list, tuple)     )
                      self.assertEqual(len(__A     )							,     __A     )
                      __UpperCamelCase	=  getattr(self.model_tester							,     'seq_length'							,     __A     )
                      __UpperCamelCase	=  getattr(self.model_tester							,     'decoder_seq_length'							,     __A     )

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

        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __UpperCamelCase	=  True
               check_hidden_states_output(__A							,     __A							,     __A     )

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

               check_hidden_states_output(__A							,     __A							,     __A     )






 def 	_lowerCamelCase (	self	:							Tuple							,     __A	:							Dict							,     __A	:							List[str]							,     __A	:							Union[str, Any]=False     ):
        __UpperCamelCase	=  super()._prepare_for_class(__A							,     __A							,     return_labels=__A     )

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

        return inputs_dict






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__A     )






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__A     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        if not self.model_tester.is_training:
               return

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

        for model_class in self.all_model_classes:
               # EfficientFormerForImageClassificationWithTeacher supports inference-only
               if (
                   model_class in get_values(__A     )
                   or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
               ):
                      continue
               __UpperCamelCase	=  model_class(__A     )
               model.to(__A     )
               model.train()
               __UpperCamelCase	=  self._prepare_for_class(__A							,     __A							,     return_labels=__A     )
               __UpperCamelCase	=  model(**__A     ).loss
               loss.backward()






 def 	_lowerCamelCase (	self	:							Dict     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase	=  [
            {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
            {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
            {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
        ]

        for model_class in self.all_model_classes:
               if (
                   model_class
                   not in [
                       *get_values(__A     ),
                   ]
                   or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
               ):
                      continue

               for problem_type in problem_types:
                      with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''     ):
                             __UpperCamelCase	=  problem_type['title']
                             __UpperCamelCase	=  problem_type['num_labels']

                             __UpperCamelCase	=  model_class(__A     )
                             model.to(__A     )
                             model.train()

                             __UpperCamelCase	=  self._prepare_for_class(__A							,     __A							,     return_labels=__A     )

                             if problem_type["num_labels"] > 1:
                                    __UpperCamelCase	=  inputs['labels'].unsqueeze(1     ).repeat(1							,     problem_type['num_labels']     )

                             __UpperCamelCase	=  inputs['labels'].to(problem_type['dtype']     )

                             # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                             # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                             # they have the same size." which is a symptom something in wrong for the regression problem.
                             # See https://github.com/huggingface/transformers/issues/11780
                             with warnings.catch_warnings(record=__A     ) as warning_list:
                                    __UpperCamelCase	=  model(**__A     ).loss
                             for w in warning_list:
                                    if "Using a target size that is different to the input size" in str(w.message     ):
                                           raise ValueError(
                                               f'''Something is going wrong in the regression problem: intercepted {w.message}'''     )

                             loss.backward()






 @slow
 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase	=  EfficientFormerModel.from_pretrained(__A     )
               self.assertIsNotNone(__A     )






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	,						__UpperCamelCase	=  self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCamelCase	=  True

        __UpperCamelCase	=  getattr(self.model_tester							,     'seq_length'							,     __A     )
        __UpperCamelCase	=  getattr(self.model_tester							,     'encoder_seq_length'							,     __A     )
        __UpperCamelCase	=  getattr(self.model_tester							,     'key_length'							,     __A     )
        __UpperCamelCase	=  getattr(self.model_tester							,     'chunk_length'							,     __A     )

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

        for model_class in self.all_model_classes:
               __UpperCamelCase	=  True
               __UpperCamelCase	=  False
               __UpperCamelCase	=  True
               __UpperCamelCase	=  model_class(__A     )
               model.to(__A     )
               model.eval()
               with torch.no_grad():
                      __UpperCamelCase	=  model(**self._prepare_for_class(__A							,     __A     )     )
               __UpperCamelCase	=  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"]
               __UpperCamelCase	=  True
               __UpperCamelCase	=  model_class(__A     )
               model.to(__A     )
               model.eval()
               with torch.no_grad():
                      __UpperCamelCase	=  model(**self._prepare_for_class(__A							,     __A     )     )
               __UpperCamelCase	=  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     lowercase__       (       )			->				Dict:



       """simple docstring"""






       __UpperCamelCase	=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image


@require_torch
@require_vision
class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 @cached_property
 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        return (
            EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'     )
            if is_vision_available()
            else None
        )






 @slow
 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  EfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'     ).to(
            __A     )

        __UpperCamelCase	=  self.default_image_processor
        __UpperCamelCase	=  prepare_img()
        __UpperCamelCase	=  image_processor(images=__A							,     return_tensors='pt'     ).to(__A     )

        # forward pass
        with torch.no_grad():
               __UpperCamelCase	=  model(**__A     )

        # verify the logits
        __UpperCamelCase	=  (1, 1_0_0_0)
        self.assertEqual(outputs.logits.shape							,     __A     )

        __UpperCamelCase	=  torch.tensor([-0.0555, 0.4825, -0.0852]     ).to(__A     )
        self.assertTrue(torch.allclose(outputs.logits[0][:3]							,     __A							,     atol=1e-4     )     )






 @slow
 def 	_lowerCamelCase (	self	:							int     ):
        __UpperCamelCase	=  EfficientFormerForImageClassificationWithTeacher.from_pretrained(
            'snap-research/efficientformer-l1-300'     ).to(__A     )

        __UpperCamelCase	=  self.default_image_processor
        __UpperCamelCase	=  prepare_img()
        __UpperCamelCase	=  image_processor(images=__A							,     return_tensors='pt'     ).to(__A     )

        # forward pass
        with torch.no_grad():
               __UpperCamelCase	=  model(**__A     )

        # verify the logits
        __UpperCamelCase	=  (1, 1_0_0_0)
        self.assertEqual(outputs.logits.shape							,     __A     )

        __UpperCamelCase	=  torch.tensor([-0.1312, 0.4353, -1.0499]     ).to(__A     )
        self.assertTrue(torch.allclose(outputs.logits[0][:3]							,     __A							,     atol=1e-4     )     )







'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


a__							:							Optional[Any]			=datasets.utils.logging.get_logger(__name__)

@dataclass
class 					snake_case					(   datasets.BuilderConfig	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[datasets.Features]      		=None






class 					snake_case					(   datasets.ArrowBasedBuilder	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : int      		=ArrowConfig






 def 	_lowerCamelCase (	self	:							List[str]     ):
        return datasets.DatasetInfo(features=self.config.features     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]							,     __A	:							Dict     ):
        if not self.config.data_files:
               raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''     )
        __UpperCamelCase	=  dl_manager.download_and_extract(self.config.data_files     )
        if isinstance(__A							,     (str, list, tuple)     ):
               __UpperCamelCase	=  data_files
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,     gen_kwargs={'files': files}     )]
        __UpperCamelCase	=  []
        for split_name, files in data_files.items():
               if isinstance(__A							,     __A     ):
                      __UpperCamelCase	=  [files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               __UpperCamelCase	=  [dl_manager.iter_files(__A     ) for file in files]
               # Infer features is they are stoed in the arrow schema
               if self.info.features is None:
                      for file in itertools.chain.from_iterable(__A     ):
                             with open(__A							,     'rb'     ) as f:
                                    __UpperCamelCase	=  datasets.Features.from_arrow_schema(pa.ipc.open_stream(__A     ).schema     )
                             break
               splits.append(datasets.SplitGenerator(name=__A							,     gen_kwargs={'files': files}     )     )
        return splits






 def 	_lowerCamelCase (	self	:							Any							,     __A	:							pa.Table     ):
        if self.info.features is not None:
               # more expensive cast to support nested features with keys in a different order
               # allows str <-> int/float or str to Audio for example
               __UpperCamelCase	=  table_cast(__A							,     self.info.features.arrow_schema     )
        return pa_table






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Optional[Any]     ):
        for file_idx, file in enumerate(itertools.chain.from_iterable(__A     )     ):
               with open(__A							,     'rb'     ) as f:
                      try:
                             for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__A     )     ):
                                    __UpperCamelCase	=  pa.Table.from_batches([record_batch]     )
                                    # Uncomment for debugging (will print the Arrow table size and elements)
                                    # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                    # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                    yield f'''{file_idx}_{batch_idx}''', self._cast_table(__A     )
                      except ValueError as e:
                             logger.error(f'''Failed to read file \'{file}\' with error {type(__A     )}: {e}'''     )
                             raise
