


'''simple docstring'''



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=2     ,	)							->	List[str]:
         __lowerCamelCase  :      Optional[int]														=						parent
         __lowerCamelCase  :      str														=						batch_size
         __lowerCamelCase  :      List[Any]														=						image_size
         __lowerCamelCase  :      Any														=						patch_size
         __lowerCamelCase  :      Tuple														=						num_channels
         __lowerCamelCase  :      Dict														=						is_training
         __lowerCamelCase  :      List[str]														=						use_labels
         __lowerCamelCase  :      Optional[Any]														=						hidden_size
         __lowerCamelCase  :      int														=						num_hidden_layers
         __lowerCamelCase  :      int														=						num_attention_heads
         __lowerCamelCase  :      str														=						intermediate_size
         __lowerCamelCase  :      Optional[int]														=						hidden_act
         __lowerCamelCase  :      List[str]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[Any]														=						type_sequence_label_size
         __lowerCamelCase  :      List[str]														=						initializer_range
         __lowerCamelCase  :      Union[str, Any]														=						scope
         __lowerCamelCase  :      Union[str, Any]														=						encoder_stride

         # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
         __lowerCamelCase  :      List[Any]														=						(image_size // patch_size) ** 2
         __lowerCamelCase  :      Optional[int]														=						num_patches + 2







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      str														=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

         __lowerCamelCase  :      Dict														=						self.get_config()

         return config, pixel_values, labels







    def  lowercase_							(				self      )							->	Tuple:
         return DeiTConfig(
             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=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	encoder_stride=self.encoder_stride     ,	)







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      Union[str, Any]														=						TFDeiTModel(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[str]														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      Tuple														=						TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(
             result.reconstruction.shape     ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

         # test greyscale images
         __lowerCamelCase  :      List[str]														=						1
         __lowerCamelCase  :      List[Any]														=						TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      Any														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.reconstruction.shape     ,	(self.batch_size, 1, self.image_size, self.image_size)      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         __lowerCamelCase  :      Dict														=						self.type_sequence_label_size
         __lowerCamelCase  :      Optional[Any]														=						TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						model(SCREAMING_SNAKE_CASE_     ,	labels=SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         __lowerCamelCase  :      Tuple														=						1
         __lowerCamelCase  :      List[Any]														=						TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      str														=						model(SCREAMING_SNAKE_CASE_     ,	labels=SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						self.prepare_config_and_inputs()
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						config_and_inputs
         __lowerCamelCase  :      Union[str, Any]														=						{'pixel_values': pixel_values}
         return config, inputs_dict







@require_tf
class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Any					=     (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase    :   List[str]					=     (
        {
            'feature-extraction': TFDeiTModel,
            'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    lowerCamelCase    :   Optional[int]					=     False
    lowerCamelCase    :   List[Any]					=     False
    lowerCamelCase    :   List[str]					=     False
    lowerCamelCase    :   Any					=     False







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Any														=						TFDeiTModelTester(self      )
         __lowerCamelCase  :      Any														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37      )







    def  lowercase_							(				self      )							->	List[Any]:
         self.config_tester.run_common_tests()







    @unittest.skip(reason='DeiT does not use inputs_embeds'      )
    def  lowercase_							(				self      )							->	Union[str, Any]:
         pass







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __lowerCamelCase  :      Any														=						model_class(SCREAMING_SNAKE_CASE_      )
              self.assertIsInstance(model.get_input_embeddings()     ,	(tf.keras.layers.Layer)      )
              __lowerCamelCase  :      Tuple														=						model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_     ,	tf.keras.layers.Dense      )      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						self.model_tester.prepare_config_and_inputs_for_common()

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

              __lowerCamelCase  :      Optional[int]														=						['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Tuple														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False      )							->	Dict:
         __lowerCamelCase  :      Optional[int]														=						super()._prepare_for_class(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	return_labels=SCREAMING_SNAKE_CASE_      )

         if return_labels:
              if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
                   del inputs_dict["labels"]

         return inputs_dict







    @slow
    def  lowercase_							(				self      )							->	Dict:
         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase  :      Tuple														=						TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )







def      UpperCAmelCase__   (      )	->						Optional[int]:
     __lowerCamelCase  :      int														=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image





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





    """simple docstring"""






    @cached_property
    def  lowercase_							(				self      )							->	str:
         return (
             DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'      )
             if is_vision_available()
             else None
         )







    @slow
    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'      )

         __lowerCamelCase  :      Union[str, Any]														=						self.default_image_processor
         __lowerCamelCase  :      Dict														=						prepare_img()
         __lowerCamelCase  :      Dict														=						image_processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='tf'      )

         # forward pass
         __lowerCamelCase  :      Dict														=						model(**SCREAMING_SNAKE_CASE_      )

         # verify the logits
         __lowerCamelCase  :      Union[str, Any]														=						tf.TensorShape((1, 10_00)      )
         self.assertEqual(outputs.logits.shape     ,	SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[str]														=						tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]      )

         self.assertTrue(np.allclose(outputs.logits[0, :3]     ,	SCREAMING_SNAKE_CASE_     ,	atol=1E-4      )      )










'''simple docstring'''



from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__						:   List[str]				=    logging.get_logger(__name__)

# TODO Update this
A__						:   Tuple				=    {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     'esm'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10_26     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_="absolute"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	List[str]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_     ,	mask_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      str														=						num_hidden_layers
         __lowerCamelCase  :      List[str]														=						num_attention_heads
         __lowerCamelCase  :      Any														=						intermediate_size
         __lowerCamelCase  :      Optional[Any]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[int]														=						max_position_embeddings
         __lowerCamelCase  :      str														=						initializer_range
         __lowerCamelCase  :      Optional[int]														=						layer_norm_eps
         __lowerCamelCase  :      List[str]														=						position_embedding_type
         __lowerCamelCase  :      int														=						use_cache
         __lowerCamelCase  :      Optional[Any]														=						emb_layer_norm_before
         __lowerCamelCase  :      Optional[Any]														=						token_dropout
         __lowerCamelCase  :      str														=						is_folding_model
         if is_folding_model:
              if esmfold_config is None:
                   logger.info('No esmfold_config supplied for folding model, using default values.'      )
                   __lowerCamelCase  :      Dict														=						EsmFoldConfig()
              elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      Optional[int]														=						EsmFoldConfig(**SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						esmfold_config
              if vocab_list is None:
                   logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'      )
                   __lowerCamelCase  :      List[str]														=						get_default_vocab_list()
              else:
                   __lowerCamelCase  :      Optional[Any]														=						vocab_list
         else:
              __lowerCamelCase  :      Dict														=						None
              __lowerCamelCase  :      Optional[Any]														=						None
         if self.esmfold_config is not None and getattr(self.esmfold_config     ,	'use_esm_attn_map'     ,	SCREAMING_SNAKE_CASE_      ):
              raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Any														=						super().to_dict()
         if isinstance(self.esmfold_config     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      int														=						self.esmfold_config.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str					=     None
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   float					=     0

    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False

    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   "TrunkConfig"					=     None







    def  lowercase_							(				self      )							->	Any:
         if self.trunk is None:
              __lowerCamelCase  :      List[str]														=						TrunkConfig()
         elif isinstance(self.trunk     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Any														=						TrunkConfig(**self.trunk      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[int]														=						asdict(self      )
         __lowerCamelCase  :      str														=						self.trunk.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     4_8
    lowerCamelCase    :   int					=     1_0_2_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   Optional[int]					=     1_2_8
    lowerCamelCase    :   "StructureModuleConfig"					=     None







    def  lowercase_							(				self      )							->	Optional[int]:
         if self.structure_module is None:
              __lowerCamelCase  :      Dict														=						StructureModuleConfig()
         elif isinstance(self.structure_module     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[Any]														=						StructureModuleConfig(**self.structure_module      )

         if self.max_recycles <= 0:
              raise ValueError(f'`max_recycles` should be positive, got {self.max_recycles}.'      )
         if self.sequence_state_dim % self.sequence_state_dim != 0:
              raise ValueError(
                  '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
                  f' {self.sequence_state_dim} and {self.sequence_state_dim}.'      )
         if self.pairwise_state_dim % self.pairwise_state_dim != 0:
              raise ValueError(
                  '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
                  f' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'      )

         __lowerCamelCase  :      Tuple														=						self.sequence_state_dim // self.sequence_head_width
         __lowerCamelCase  :      str														=						self.pairwise_state_dim // self.pairwise_head_width

         if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
              raise ValueError(
                  '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
                  f' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'      )
         if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
              raise ValueError(
                  '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
                  f' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'      )
         if self.pairwise_state_dim % 2 != 0:
              raise ValueError(f'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'      )

         if self.dropout >= 0.4:
              raise ValueError(f'`dropout` should not be greater than 0.4, got {self.dropout}.'      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      List[str]														=						asdict(self      )
         __lowerCamelCase  :      int														=						self.structure_module.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     3_8_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_6
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_2
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   float					=     0.1
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     2
    lowerCamelCase    :   int					=     7
    lowerCamelCase    :   int					=     1_0
    lowerCamelCase    :   float					=     1e-8
    lowerCamelCase    :   float					=     1e5







    def  lowercase_							(				self      )							->	Any:
         return asdict(self      )







def      UpperCAmelCase__   (      )	->						Optional[Any]:
     return (
         "<cls>",
         "<pad>",
         "<eos>",
         "<unk>",
         "L",
         "A",
         "G",
         "V",
         "S",
         "E",
         "R",
         "T",
         "I",
         "D",
         "P",
         "K",
         "Q",
         "N",
         "F",
         "Y",
         "M",
         "H",
         "W",
         "C",
         "X",
         "B",
         "U",
         "Z",
         "O",
         ".",
         "-",
         "<null_1>",
         "<mask>",
     )






