

"""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   ):







 """simple docstring"""







 def __init__(			self							:			Dict,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Tuple=1_3,					UpperCAmelCase__							:			str=3_0,					UpperCAmelCase__							:			Dict=2,					UpperCAmelCase__							:			Optional[Any]=3,					UpperCAmelCase__							:			List[str]=True,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			Dict=3_2,					UpperCAmelCase__							:			Tuple=5,					UpperCAmelCase__							:			List[str]=4,					UpperCAmelCase__							:			List[str]=3_7,					UpperCAmelCase__							:			Any="gelu",					UpperCAmelCase__							:			List[str]=0.1,					UpperCAmelCase__							:			str=0.1,					UpperCAmelCase__							:			List[str]=1_0,					UpperCAmelCase__							:			Optional[int]=0.02,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range

    # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
    __lowercase								=						(image_size // patch_size) ** 2
    __lowercase								=						num_patches + 1


 def 					_lowercase (			self							:			str	):
    __lowercase								=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

    __lowercase								=						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=_a,					initializer_range=self.initializer_range,					)

    return config, pixel_values


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						FlaxViTModel(config=_a	)
    __lowercase								=						model(_a	)
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    __lowercase								=						(self.image_size, self.image_size)
    __lowercase								=						(self.patch_size, self.patch_size)
    __lowercase								=						(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 					_lowercase (			self							:			List[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Optional[int]	):
    __lowercase								=						self.type_sequence_label_size
    __lowercase								=						FlaxViTForImageClassification(config=_a	)
    __lowercase								=						model(_a	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)	)

    # test greyscale images
    __lowercase								=						1
    __lowercase								=						FlaxViTForImageClassification(_a	)

    __lowercase								=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
    __lowercase								=						model(_a	)



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







@require_flax
class 					_lowerCAmelCase  (	__lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Optional[int]   		=	(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						FlaxViTModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=_a,					has_text_modality=_a,					hidden_size=3_7	)


 def 					_lowercase (			self							:			Optional[Any]	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_a	)


 def 					_lowercase (			self							:			str	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase								=						['''pixel_values''']
       self.assertListEqual(arg_names[:1],					_a	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
          __lowercase								=						self._prepare_for_class(_a,					_a	)
          __lowercase								=						model_class(_a	)

          @jax.jit
          def model_jitted(UpperCAmelCase__							:			Tuple,					**UpperCAmelCase__							:			Optional[Any]	):
             return model(pixel_values=_a,					**_a	)

          with self.subTest("JIT Enabled"	):
             __lowercase								=						model_jitted(**_a	).to_tuple()

          with self.subTest("JIT Disabled"	):
             with jax.disable_jit():
                __lowercase								=						model_jitted(**_a	).to_tuple()

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



 @slow
 def 					_lowercase (			self							:			Optional[int]	):
    for model_class_name in self.all_model_classes:
       __lowercase								=						model_class_name.from_pretrained("google/vit-base-patch16-224"	)
       __lowercase								=						model(np.ones((1, 3, 2_2_4, 2_2_4)	)	)
       self.assertIsNotNone(_a	)







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			Dict	)			->     Tuple:

					"""simple docstring"""

					_a	:    Any         =		R'''\w+[.]\d+'''
					_a	:    Union[str, Any]         =		re.findall(__a		,__a	)
					for pat in pats:
										_a	:    int         =		key.replace(__a		,'''_'''.join(pat.split('''.'''	)	)	)
					return key



def    __UpperCAmelCase (	__a      :			List[str]		,__a      :			Union[str, Any]		,__a      :			Optional[int]	)			->     Tuple:

					"""simple docstring"""

					_a	:    Dict         =		pt_tuple_key[:-1] + ('''scale''',)
					if (
					    any('''norm''' in str_ for str_ in pt_tuple_key	)
					    and (pt_tuple_key[-1] == "bias")
					    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
					    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
					):
										_a	:    Dict         =		pt_tuple_key[:-1] + ('''scale''',)
										return renamed_pt_tuple_key, pt_tensor
					elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
										_a	:    Optional[int]         =		pt_tuple_key[:-1] + ('''scale''',)
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
										_a	:    Union[str, Any]         =		pt_tuple_key[:-1] + ('''embedding''',)
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					_a	:    Tuple         =		pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
										_a	:    List[str]         =		pt_tensor.transpose(2		,3		,1		,0	)
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					_a	:    Dict         =		pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight":
										_a	:    Union[str, Any]         =		pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					_a	:    Dict         =		pt_tuple_key[:-1] + ('''weight''',)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					_a	:    Union[str, Any]         =		pt_tuple_key[:-1] + ('''bias''',)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor







def    __UpperCAmelCase (	__a      :			Dict		,__a      :			str		,__a      :			str=42	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    Tuple         =		{k: v.numpy() for k, v in pt_state_dict.items()}

					# Step 2: Since the model is stateless, get random Flax params
					_a	:    List[Any]         =		flax_model.init_weights(PRNGKey(__a	)	)

					_a	:    Optional[int]         =		flatten_dict(__a	)
					_a	:    Tuple         =		{}

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										_a	:    List[str]         =		rename_key(__a	)
										_a	:    Optional[Any]         =		tuple(renamed_pt_key.split('''.'''	)	)

										# Correctly rename weight parameters
										_a      ,					_a	:    List[str]         =		rename_key_and_reshape_tensor(__a		,__a		,__a	)

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																				    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)

        # also add unexpected weight so that warning is thrown
										_a	:    Dict         =		jnp.asarray(__a	)

					return unflatten_dict(__a	)



