


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


lowerCAmelCase__  :int   			=							logging.get_logger(__name__)

def        lowerCAmelCase__ (			a__:		List[Any]       )  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase 		=			R'\w+[.]\d+'
							_UpperCAmelCase 		=			re.findall(UpperCamelCase_			,  UpperCamelCase_       )
							for pat in pats:
														_UpperCAmelCase 		=			key.replace(UpperCamelCase_			,  '_'.join(pat.split('.'       )       )       )
							return key

def        lowerCAmelCase__ (			a__:		Dict			,  a__:		Union[str, Any]			,  a__:		Union[str, Any]       )  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase 		=			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)
							):
														_UpperCAmelCase 		=			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:
														_UpperCAmelCase 		=			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:
														_UpperCAmelCase 		=			pt_tuple_key[:-1] + ('embedding',)
														return renamed_pt_tuple_key, pt_tensor

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

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

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

							# old PyTorch layer norm bias
							_UpperCAmelCase 		=			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__ (			a__:		List[Any]			,  a__:		List[Any]			,  a__:		Dict=4_2       )  ->		int:


							'''simple docstring'''





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

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

							_UpperCAmelCase 		=			flatten_dict(UpperCamelCase_       )
							_UpperCAmelCase 		=			{}

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														_UpperCAmelCase 		=			rename_key(UpperCamelCase_       )
														_UpperCAmelCase 		=			tuple(renamed_pt_key.split('.'       )       )

														# Correctly rename weight parameters
														_UpperCAmelCase		,       _UpperCAmelCase 		=			rename_key_and_reshape_tensor(UpperCamelCase_			,  UpperCamelCase_			,  UpperCamelCase_       )

														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
														_UpperCAmelCase 		=			jnp.asarray(UpperCamelCase_       )

							return unflatten_dict(UpperCamelCase_       )


import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =LxmertTokenizer
	A__    :   List[Any]				       =LxmertTokenizerFast
	A__    :   Any				       =True
	A__    :   List[Any]				       =True
	def      A_				(			self							:   Optional[Any]							):
						super().setUp()

						SCREAMING_SNAKE_CASE__													= [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE__													= os.path.join(self.tmpdirname       ,   VOCAB_FILES_NAMES['vocab_file']							)
						with open(self.vocab_file       ,   'w'       ,   encoding='utf-8'							) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
	def      A_				(			self							:   int       ,   UpperCAmelCase_							:   int							):
						SCREAMING_SNAKE_CASE__													= 'UNwant\u00E9d,running'
						SCREAMING_SNAKE_CASE__													= 'unwanted, running'
						return input_text, output_text
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= self.tokenizer_class(self.vocab_file							)

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize('UNwant\u00E9d,running'							)
						self.assertListEqual(UpperCAmelCase_       ,   ['un', '##want', '##ed', ',', 'runn', '##ing']							)
						self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)       ,   [7, 4, 5, 10, 8, 9]							)





	def      A_				(			self							:   List[str]							):
						if not self.test_rust_tokenizer:
											return

						SCREAMING_SNAKE_CASE__													= self.get_tokenizer()
						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()

						SCREAMING_SNAKE_CASE__													= 'I was born in 92000, and this is falsé.'

						SCREAMING_SNAKE_CASE__													= tokenizer.tokenize(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.tokenize(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_       ,   add_special_tokens=UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE__													= tokenizer.encode(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= rust_tokenizer.encode(UpperCAmelCase_							)
						self.assertListEqual(UpperCAmelCase_       ,   UpperCAmelCase_							)





