






"""simple docstring"""



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class 			lowerCAmelCase__			(    unittest.TestCase   ):
   def __init__(		self      :	List[Any]							,			snake_case__      :	Dict							,			snake_case__      :	Optional[Any]=1_3							,			snake_case__      :	Tuple=3_0							,			snake_case__      :	str=2							,			snake_case__      :	List[Any]=3							,			snake_case__      :	Any=True							,			snake_case__      :	Dict=True							,			snake_case__      :	Optional[Any]=3_2							,			snake_case__      :	Any=5							,			snake_case__      :	Optional[Any]=4							,			snake_case__      :	Tuple=3_7							,			snake_case__      :	List[str]="gelu"							,			snake_case__      :	Union[str, Any]=0.1							,			snake_case__      :	List[str]=0.1							,			snake_case__      :	int=1_0							,			snake_case__      :	Any=0.02							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							parent
        UpperCAmelCase__						:    int							=							batch_size
        UpperCAmelCase__						:    Dict							=							image_size
        UpperCAmelCase__						:    str							=							patch_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_channels
        UpperCAmelCase__						:    str							=							is_training
        UpperCAmelCase__						:    Optional[Any]							=							use_labels
        UpperCAmelCase__						:    Optional[Any]							=							hidden_size
        UpperCAmelCase__						:    Tuple							=							num_hidden_layers
        UpperCAmelCase__						:    Tuple							=							num_attention_heads
        UpperCAmelCase__						:    str							=							intermediate_size
        UpperCAmelCase__						:    List[str]							=							hidden_act
        UpperCAmelCase__						:    Dict							=							hidden_dropout_prob
        UpperCAmelCase__						:    Any							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							type_sequence_label_size
        UpperCAmelCase__						:    Optional[Any]							=							initializer_range

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        UpperCAmelCase__						:    Optional[int]							=							(image_size // patch_size) ** 2
        UpperCAmelCase__						:    Tuple							=							num_patches + 1
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        UpperCAmelCase__						:    str							=							ViTConfig(
            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							,			)

        return config, pixel_values
   def       __a							(		self      :	Dict							,			snake_case__      :	int							,			snake_case__      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							FlaxViTModel(config=__UpperCAmelCase							)
        UpperCAmelCase__						:    Union[str, Any]							=							model(__UpperCAmelCase							)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        UpperCAmelCase__						:    Dict							=							(self.image_size, self.image_size)
        UpperCAmelCase__						:    Dict							=							(self.patch_size, self.patch_size)
        UpperCAmelCase__						:    Optional[Any]							=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, num_patches + 1, self.hidden_size)							)
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Optional[int]							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							self.type_sequence_label_size
        UpperCAmelCase__						:    List[Any]							=							FlaxViTForImageClassification(config=__UpperCAmelCase							)
        UpperCAmelCase__						:    Tuple							=							model(__UpperCAmelCase							)
        self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)							)

        # test greyscale images
        UpperCAmelCase__						:    Optional[Any]							=							1
        UpperCAmelCase__						:    List[Any]							=							FlaxViTForImageClassification(__UpperCAmelCase							)

        UpperCAmelCase__						:    List[str]							=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        UpperCAmelCase__						:    str							=							model(__UpperCAmelCase							)




   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase__		
        )							,				(
            UpperCAmelCase__		
        )							,						
        )						:    Optional[Any]							=							config_and_inputs
        UpperCAmelCase__						:    Any							=							{"pixel_values": pixel_values}
        return config, inputs_dict




@require_flax
class 			lowerCAmelCase__			(    A__							,						unittest.TestCase   ):
   SCREAMING_SNAKE_CASE_    =(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
   def       __a							(		self      :	int							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							FlaxViTModelTester(self							)
        UpperCAmelCase__						:    List[Any]							=							ConfigTester(self							,			config_class=__UpperCAmelCase							,			has_text_modality=__UpperCAmelCase							,			hidden_size=3_7							)
   def       __a							(		self      :	int							):


        '''simple docstring'''
        self.config_tester.run_common_tests()
   def       __a							(		self      :	Optional[int]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCAmelCase							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[Any]							=							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             UpperCAmelCase__						:    List[Any]							=							model_class(__UpperCAmelCase							)
             UpperCAmelCase__						:    Tuple							=							inspect.signature(model.__call__							)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCAmelCase__						:    Optional[Any]							=							[*signature.parameters.keys()]

             UpperCAmelCase__						:    Optional[int]							=							["pixel_values"]
             self.assertListEqual(arg_names[:1]							,			__UpperCAmelCase							)
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__							):
                  UpperCAmelCase__						:    int							=							self._prepare_for_class(__UpperCAmelCase							,			__UpperCAmelCase							)
                  UpperCAmelCase__						:    str							=							model_class(__UpperCAmelCase							)

                  @jax.jit
                  def model_jitted(snake_case__      :	int							,			**snake_case__      :	Tuple							):
                       return model(pixel_values=__UpperCAmelCase							,			**__UpperCAmelCase							)

                  with self.subTest("JIT Enabled"							):
                       UpperCAmelCase__						:    Union[str, Any]							=							model_jitted(**__UpperCAmelCase							).to_tuple()

                  with self.subTest("JIT Disabled"							):
                       with jax.disable_jit():
                            UpperCAmelCase__						:    str							=							model_jitted(**__UpperCAmelCase							).to_tuple()

                  self.assertEqual(len(__UpperCAmelCase							)							,			len(__UpperCAmelCase							)							)
                  for jitted_output, output in zip(__UpperCAmelCase							,			__UpperCAmelCase							):
                       self.assertEqual(jitted_output.shape							,			output.shape							)




   @slow
   def       __a							(		self      :	Any							):


        '''simple docstring'''
        for model_class_name in self.all_model_classes:
             UpperCAmelCase__						:    Dict							=							model_class_name.from_pretrained("google/vit-base-patch16-224"							)
             UpperCAmelCase__						:    int							=							model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
             self.assertIsNotNone(__UpperCAmelCase							)





"""simple docstring"""




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   vocab_size
						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__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


