





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) ->Optional[int]:
					lowerCamelCase__       =       R"\w+[.]\d+"
					lowerCamelCase__       =       re.findall(a,     a)
					for pat in pats:
										lowerCamelCase__       =       key.replace(a,     "_".join(pat.split(".")))
					return key





def    __UpperCamelCase      (    a,     a,     a) ->Tuple:
					lowerCamelCase__       =       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)
					):
										lowerCamelCase__       =       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:
										lowerCamelCase__       =       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:
										lowerCamelCase__       =       pt_tuple_key[:-1] + ("embedding",)
										return renamed_pt_tuple_key, pt_tensor

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

					# linear layer
					lowerCamelCase__       =       pt_tuple_key[:-1] + ("kernel",)
					if pt_tuple_key[-1] == "weight":
										lowerCamelCase__       =       pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

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

					# old PyTorch layer norm bias
					lowerCamelCase__       =       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,     a,     a=42) ->Optional[Any]:
					# Step 1: Convert pytorch tensor to numpy
					lowerCamelCase__       =       {k: v.numpy() for k, v in pt_state_dict.items()}

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

					lowerCamelCase__       =       flatten_dict(a)
					lowerCamelCase__       =       {}

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

										# Correctly rename weight parameters
										lowerCamelCase__    ,		lowerCamelCase__       =       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
										lowerCamelCase__       =       jnp.asarray(a)

					return unflatten_dict(a)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       tempfile.mkdtemp()

										# fmt: off
										lowerCamelCase__       =       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
										# fmt: on
										lowerCamelCase__       =       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]	)	)

										lowerCamelCase__       =       {
										    "do_resize": True,
										    "size": {"height": 18, "width": 18},
										    "do_normalize": True,
										    "image_mean": [0.5, 0.5, 0.5],
										    "image_std": [0.5, 0.5, 0.5],
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return BertTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										shutil.rmtree(self.tmpdirname	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       [np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)]

										lowerCamelCase__       =       [Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	) for x in image_inputs]

										return image_inputs





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       VisionTextDualEncoderProcessor(
										    tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()	)
										processor.save_pretrained(self.tmpdirname	)

										lowerCamelCase__       =       self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"	)
										lowerCamelCase__       =       self.get_image_processor(do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										lowerCamelCase__       =       VisionTextDualEncoderProcessor.from_pretrained(
										    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										self.assertEqual(processor.tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()	)
										self.assertIsInstance(processor.tokenizer	,  (BertTokenizer, BertTokenizerFast)	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       processor(images=_lowerCAmelCase	,  return_tensors="np"	)

										for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()	,  input_processor[key].sum()	,  delta=1E-2	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	)

										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)

										# test if it raises when no input is passed
										with self.assertRaises(_lowerCAmelCase	):
															processor()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__       =       processor.batch_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	)

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       VisionTextDualEncoderProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "lower newer"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  processor.model_input_names	)

