

'''simple docstring'''





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


UpperCamelCase_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]						)				->  Dict:
						lowercase :							List[str]    		=R'''\w+[.]\d+'''
						lowercase :							List[str]    		=re.findall(__magic_name__   , __magic_name__						)
						for pat in pats:
												lowercase :							Optional[int]    		=key.replace(__magic_name__   , '''_'''.join(pat.split('''.'''						)						)						)
						return key






def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Dict						)				->  str:
						lowercase :							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)
						):
												lowercase :							str    		=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:
												lowercase :							str    		=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:
												lowercase :							Dict    		=pt_tuple_key[:-1] + ('''embedding''',)
												return renamed_pt_tuple_key, pt_tensor

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

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

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

						# old PyTorch layer norm bias
						lowercase :							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 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Union[str, Any]=42						)				->  List[str]:
						# Step 1: Convert pytorch tensor to numpy
						lowercase :							Optional[Any]    		={k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						lowercase :							str    		=flax_model.init_weights(PRNGKey(__magic_name__						)						)

						lowercase :							Dict    		=flatten_dict(__magic_name__						)
						lowercase :							Dict    		={}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowercase :							Dict    		=rename_key(__magic_name__						)
												lowercase :							Optional[int]    		=tuple(renamed_pt_key.split('''.'''						)						)

												# Correctly rename weight parameters
												lowercase		,	lowercase :							Any    		=rename_key_and_reshape_tensor(__magic_name__   , __magic_name__   , __magic_name__						)

												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
												lowercase :							Tuple    		=jnp.asarray(__magic_name__						)

						return unflatten_dict(__magic_name__						)













"""simple docstring"""
import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.distilbert.modeling_flax_distilbert import (
			    FlaxDistilBertForMaskedLM,
			    FlaxDistilBertForMultipleChoice,
			    FlaxDistilBertForQuestionAnswering,
			    FlaxDistilBertForSequenceClassification,
			    FlaxDistilBertForTokenClassification,
			    FlaxDistilBertModel,
			)







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					int      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Union[str, Any]=13      ,      lowerCAmelCase_   :					Union[str, Any]=7      ,      lowerCAmelCase_   :					Optional[Any]=True      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					Any=True      ,      lowerCAmelCase_   :					Tuple=True      ,      lowerCAmelCase_   :					Tuple=99      ,      lowerCAmelCase_   :					str=32      ,      lowerCAmelCase_   :					Optional[int]=5      ,      lowerCAmelCase_   :					List[Any]=4      ,      lowerCAmelCase_   :					List[Any]=37      ,      lowerCAmelCase_   :					str="gelu"      ,      lowerCAmelCase_   :					Optional[Any]=0.1      ,      lowerCAmelCase_   :					Union[str, Any]=0.1      ,      lowerCAmelCase_   :					List[Any]=512      ,      lowerCAmelCase_   :					Dict=16      ,      lowerCAmelCase_   :					int=2      ,      lowerCAmelCase_   :					Union[str, Any]=0.0_2      ,      lowerCAmelCase_   :					Optional[Any]=4      ,      )		->     Tuple:
				UpperCAmelCase_	:					Optional[Any]     	=   parent
				UpperCAmelCase_	:					Tuple     	=   batch_size
				UpperCAmelCase_	:					str     	=   seq_length
				UpperCAmelCase_	:					Optional[int]     	=   is_training
				UpperCAmelCase_	:					Dict     	=   use_attention_mask
				UpperCAmelCase_	:					int     	=   use_token_type_ids
				UpperCAmelCase_	:					Tuple     	=   use_labels
				UpperCAmelCase_	:					Tuple     	=   vocab_size
				UpperCAmelCase_	:					Union[str, Any]     	=   hidden_size
				UpperCAmelCase_	:					int     	=   num_hidden_layers
				UpperCAmelCase_	:					List[str]     	=   num_attention_heads
				UpperCAmelCase_	:					str     	=   intermediate_size
				UpperCAmelCase_	:					List[str]     	=   hidden_act
				UpperCAmelCase_	:					Tuple     	=   hidden_dropout_prob
				UpperCAmelCase_	:					Optional[Any]     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					Optional[int]     	=   max_position_embeddings
				UpperCAmelCase_	:					Optional[Any]     	=   type_vocab_size
				UpperCAmelCase_	:					Dict     	=   type_sequence_label_size
				UpperCAmelCase_	:					List[Any]     	=   initializer_range
				UpperCAmelCase_	:					Any     	=   num_choices





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[str]     	=   ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size		)

				UpperCAmelCase_	:					Tuple     	=   None
				if self.use_attention_mask:
							UpperCAmelCase_	:					Dict     	=   random_attention_mask([self.batch_size, self.seq_length]		)

				UpperCAmelCase_	:					Any     	=   DistilBertConfig(
				    vocab_size=self.vocab_size      ,      dim=self.hidden_size      ,      n_layers=self.num_hidden_layers      ,      n_heads=self.num_attention_heads      ,      hidden_dim=self.intermediate_size      ,      hidden_act=self.hidden_act      ,      dropout=self.hidden_dropout_prob      ,      attention_dropout=self.attention_probs_dropout_prob      ,      max_position_embeddings=self.max_position_embeddings      ,      initializer_range=self.initializer_range      ,      tie_weights_=lowerCAmelCase_      ,      )

				return config, input_ids, attention_mask





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Any     	=   self.prepare_config_and_inputs()
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   config_and_inputs
				UpperCAmelCase_	:					Optional[Any]     	=   {"input_ids": input_ids, "attention_mask": attention_mask}
				return config, inputs_dict







@require_flax
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   FlaxDistilBertModelTester(self		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				for model_class_name in self.all_model_classes:
							UpperCAmelCase_	:					str     	=   model_class_name.from_pretrained("distilbert-base-uncased"		)
							UpperCAmelCase_	:					List[str]     	=   model(np.ones((1, 1)		)		)
							self.assertIsNotNone(lowerCAmelCase_		)







@require_flax
class      UpperCamelCase_			(unittest.TestCase	):





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     Optional[int]:
				UpperCAmelCase_	:					Any     	=   FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"		)
				UpperCAmelCase_	:					int     	=   np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]		)
				UpperCAmelCase_	:					List[str]     	=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
				UpperCAmelCase_	:					Any     	=   model(lowerCAmelCase_      ,      attention_mask=lowerCAmelCase_		)[0]
				UpperCAmelCase_	:					Union[str, Any]     	=   (1, 11, 768)
				self.assertEqual(output.shape      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]		)

				self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]      ,      lowerCAmelCase_      ,      atol=1e-4		)		)



