
"""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			:			List[Any]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			int = 13    ,      _lowerCamelCase			:			int = 64    ,      _lowerCamelCase			:			int = 2    ,      _lowerCamelCase			:			int = 3    ,      _lowerCamelCase			:			int = 3    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			bool = True    ,      _lowerCamelCase			:			int = 128    ,      _lowerCamelCase			:			Any=[16, 32, 64, 128]    ,      _lowerCamelCase			:			int = 7    ,      _lowerCamelCase			:			int = 4    ,      _lowerCamelCase			:			int = 37    ,      _lowerCamelCase			:			str = "gelu"    ,      _lowerCamelCase			:			float = 0.1    ,      _lowerCamelCase			:			float = 0.1    ,      _lowerCamelCase			:			int = 10    ,      _lowerCamelCase			:			float = 0.0_2    ,      _lowerCamelCase			:			int = 2    ,      _lowerCamelCase			:			int = 1    ,      _lowerCamelCase			:			int = 128    ,      _lowerCamelCase			:			List[int] = [2, 2, 2, 2]    ,      _lowerCamelCase			:			int = 2    ,      _lowerCamelCase			:			int = 2    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       image_size
       _snake_case					     =       patch_size
       _snake_case					     =       num_channels
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_act
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       type_sequence_label_size
       _snake_case					     =       initializer_range
       _snake_case					     =       encoder_stride
       _snake_case					     =       num_attention_outputs
       _snake_case					     =       embed_dim
       _snake_case					     =       embed_dim + 1
       _snake_case					     =       resolution
       _snake_case					     =       depths
       _snake_case					     =       hidden_sizes
       _snake_case					     =       dim
       _snake_case					     =       mlp_expansion_ratio







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

       _snake_case					     =       self.get_config()

       return config, pixel_values, labels







     def      lowercase	(							self			:			Optional[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=_lowerCamelCase    ,      initializer_range=self.initializer_range    ,      encoder_stride=self.encoder_stride    ,      resolution=self.resolution    ,      depths=self.depths    ,      hidden_sizes=self.hidden_sizes    ,      dim=self.dim    ,      mlp_expansion_ratio=self.mlp_expansion_ratio    ,      )







     def      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Dict    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       TFEfficientFormerModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      training=_lowerCamelCase			)
       self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)			)







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			int			):
       _snake_case					     =       self.type_sequence_label_size
       _snake_case					     =       TFEfficientFormerForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase    ,      training=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)			)

       # test greyscale images
       _snake_case					     =       1
       _snake_case					     =       TFEfficientFormerForImageClassification(_lowerCamelCase			)

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







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.prepare_config_and_inputs()
       _snake_case     ,					_snake_case     ,					_snake_case					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(
         (
             TFEfficientFormerModel,
             TFEfficientFormerForImageClassificationWithTeacher,
             TFEfficientFormerForImageClassification,
         )
         if is_tf_available()
         else ()
     )
     __a						=					(
         {
             """feature-extraction""": TFEfficientFormerModel,
             """image-classification""": (
                 TFEfficientFormerForImageClassification,
                 TFEfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_tf_available()
         else {}
     )

     __a						=					False

     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       TFEfficientFormerModelTester(self			)
       _snake_case					     =       ConfigTester(
           self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase    ,      hidden_size=37			)







     def      lowercase	(							self			:			Tuple			):
       self.config_tester.run_common_tests()







     @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''			)
     def      lowercase	(							self			:			str			):
       pass







     @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''			)
     def      lowercase	(							self			:			Any			):
       pass







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       def check_hidden_states_output(_lowerCamelCase			:			str    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Optional[int]			):
         _snake_case					     =       model_class(_lowerCamelCase			)

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

         _snake_case					     =       getattr(
             self.model_tester    ,      '''expected_num_hidden_layers'''    ,      self.model_tester.num_hidden_layers + 1			)
         self.assertEqual(len(_lowerCamelCase			)    ,      _lowerCamelCase			)

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

           self.asseretIsInstance(_lowerCamelCase    ,      (list, tuple)			)
           self.assertEqual(len(_lowerCamelCase			)    ,      _lowerCamelCase			)
           _snake_case					     =       getattr(self.model_tester    ,      '''seq_length'''    ,      _lowerCamelCase			)
           _snake_case					     =       getattr(self.model_tester    ,      '''decoder_seq_length'''    ,      _lowerCamelCase			)

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

       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case					     =       True
         check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)

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

         check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]=False			):
       _snake_case					     =       super()._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase    ,      return_labels=_lowerCamelCase			)

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

       return inputs_dict







     def      lowercase	(							self			:			Optional[int]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''			)
     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			Optional[Any]			):
       for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       TFEfficientFormerModel.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)







     def      lowercase	(							self			:			Any			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()
       _snake_case					     =       True

       _snake_case					     =       getattr(self.model_tester    ,      '''seq_length'''    ,      _lowerCamelCase			)
       _snake_case					     =       getattr(self.model_tester    ,      '''encoder_seq_length'''    ,      _lowerCamelCase			)
       _snake_case					     =       getattr(self.model_tester    ,      '''key_length'''    ,      _lowerCamelCase			)
       _snake_case					     =       getattr(self.model_tester    ,      '''chunk_length'''    ,      _lowerCamelCase			)

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

       for model_class in self.all_model_classes:
         _snake_case					     =       True
         _snake_case					     =       False
         _snake_case					     =       True
         _snake_case					     =       model_class(_lowerCamelCase			)

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

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

         _snake_case					     =       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
         self.assertEqual(len(_lowerCamelCase			)    ,      self.model_tester.num_attention_outputs			)

         if chunk_length is not None:
           self.assertListEqual(
               list(attentions[0].shape[-4:]			)    ,      [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]    ,      )
         else:
           self.assertListEqual(
               list(attentions[0].shape[-3:]			)    ,      [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]    ,      )







     def      lowercase	(							self			:			Any			):
       # 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
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

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






def 					_UpperCAmelCase      ( )     ->					List[str]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

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







     @cached_property
     def      lowercase	(							self			:			Union[str, Any]			):
       return (
           EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''			)
           if is_vision_available()
           else None
       )







     @slow
     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       TFEfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''			)
       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)
       # forward pass
       _snake_case					     =       model(**_lowerCamelCase    ,      training=_lowerCamelCase			)
       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)
       _snake_case					     =       tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]			)
       self.assertTrue(np.allclose(outputs.logits[0, :3]    ,      _lowerCamelCase    ,      atol=1e-4			)			)







     @slow
     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
           '''snap-research/efficientformer-l1-300'''			)
       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)
       # forward pass
       _snake_case					     =       model(**_lowerCamelCase    ,      training=_lowerCamelCase			)
       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)
       _snake_case					     =       tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]			)
       self.assertTrue(np.allclose(outputs.logits[0, :3]    ,      _lowerCamelCase    ,      atol=1e-4			)			)





"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

UpperCAmelCase__          = {
    'google/rembert': 256,
}

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]=False    ,      _lowerCamelCase			:			Any=True    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			int="[CLS]"    ,      _lowerCamelCase			:			Optional[int]="[SEP]"    ,      _lowerCamelCase			:			Optional[int]="[UNK]"    ,      _lowerCamelCase			:			Optional[Any]="[SEP]"    ,      _lowerCamelCase			:			str="[PAD]"    ,      _lowerCamelCase			:			List[Any]="[CLS]"    ,      _lowerCamelCase			:			Any="[MASK]"    ,      **_lowerCamelCase			:			Optional[int]    ,      ):
       super().__init__(
           do_lower_case=_lowerCamelCase    ,      remove_space=_lowerCamelCase    ,      keep_accents=_lowerCamelCase    ,      bos_token=_lowerCamelCase    ,      eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      sep_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      cls_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      **_lowerCamelCase    ,      )

       _snake_case					     =       do_lower_case
       _snake_case					     =       remove_space
       _snake_case					     =       keep_accents
       _snake_case					     =       vocab_file

       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			int			):
       return len(self.sp_model			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def __getstate__(							self			:			List[str]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			List[str]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       d
       _snake_case					     =       spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Tuple=False			):
       _snake_case					     =       self.sp_model.EncodeAsPieces(_lowerCamelCase			)
       return pieces







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str			):
       return self.sp_model.PieceToId(_lowerCamelCase			)







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			int			):
       return self.sp_model.IdToPiece(_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       self.sp_model.decode_pieces(_lowerCamelCase			)
       return out_string







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''			)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]

       if token_ids_a is None:
         return len(cls + token_ids_a + sep			) * [0]
       return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_lowerCamelCase			)			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			):
         copyfile(self.vocab_file    ,      _lowerCamelCase			)

       return (out_vocab_file,)



