


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
 import torch


if is_vision_available():
 from PIL import Image

 from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class   __lowercase				(  unittest.TestCase  ):
      UpperCamelCase								=       ViTImageProcessor if is_vision_available() else None





      @property
      def        _lowercase				(		self						:					Tuple       )    ->   int:






         """simple docstring"""







         return self.image_processor_tester.prepare_image_processor_dict()





      def        _lowercase				(		self						:					str       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase						=     (3, 3_2, 1_2_8)
         UpperCAmelCase						=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase						=     ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
         # fmt: on
         UpperCAmelCase						=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )

         UpperCAmelCase						=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""vocab_file"""]       )
         with open(self.vocab_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write(json.dumps(__lowerCamelCase       ) + """\n"""       )

         UpperCAmelCase						=     {
             """do_normalize""": False,
             """do_resize""": True,
             """image_processor_type""": """ViTImageProcessor""",
             """resample""": 3,
             """size""": {"""height""": 3_2, """width""": 1_2_8},
         }
         UpperCAmelCase						=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







         return MgpstrTokenizer.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					int					,       **__lowerCamelCase						:					Optional[int]       )    ->   Optional[int]:






         """simple docstring"""







         return ViTImageProcessor.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Dict:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					Any       )    ->   str:






         """simple docstring"""







         UpperCAmelCase						=     np.random.randint(2_5_5					,       size=(3, 3_0, 4_0_0)					,       dtype=np.uinta       )

         UpperCAmelCase						=     Image.fromarray(np.moveaxis(__lowerCamelCase					,       0					,       -1       )       )

         return image_input





      def        _lowercase				(		self						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase						=     self.get_tokenizer()
         UpperCAmelCase						=     self.get_image_processor()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )
         UpperCAmelCase						=     MgpstrProcessor.from_pretrained(self.tmpdirname					,       use_fast=__lowerCamelCase       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

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





      def        _lowercase				(		self						:					int       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase						=     self.get_tokenizer()
         UpperCAmelCase						=     self.get_image_processor()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor.save_pretrained(self.tmpdirname       )

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

         UpperCAmelCase						=     MgpstrProcessor.from_pretrained(
             self.tmpdirname					,       bos_token="""(BOS)"""					,       eos_token="""(EOS)"""					,       do_normalize=__lowerCamelCase					,       padding_value=1.0       )

         self.assertEqual(processor.char_tokenizer.get_vocab()					,       tokenizer_add_kwargs.get_vocab()       )
         self.assertIsInstance(processor.char_tokenizer					,       __lowerCamelCase       )

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





      def        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase						=     self.get_image_processor()
         UpperCAmelCase						=     self.get_tokenizer()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase						=     self.prepare_image_inputs()

         UpperCAmelCase						=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase						=     processor(images=__lowerCamelCase					,       return_tensors="""np"""       )

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





      def        _lowercase				(		self						:					str       )    ->   int:






         """simple docstring"""







         UpperCAmelCase						=     self.get_image_processor()
         UpperCAmelCase						=     self.get_tokenizer()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase						=     """test"""

         UpperCAmelCase						=     processor(text=__lowerCamelCase       )

         UpperCAmelCase						=     tokenizer(__lowerCamelCase       )
         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]					,       encoded_processor[key]       )





      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase						=     self.get_image_processor()
         UpperCAmelCase						=     self.get_tokenizer()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase						=     """test"""
         UpperCAmelCase						=     self.prepare_image_inputs()

         UpperCAmelCase						=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

         self.assertListEqual(list(inputs.keys()       )					,       ["""pixel_values""", """labels"""]       )

         # test if it raises when no input is passed
         with pytest.raises(__lowerCamelCase       ):
            processor()





      def        _lowercase				(		self						:					str       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase						=     self.get_image_processor()
         UpperCAmelCase						=     self.get_tokenizer()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

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

         UpperCAmelCase						=     processor.char_decode(__lowerCamelCase       )
         UpperCAmelCase						=     tokenizer.batch_decode(__lowerCamelCase       )
         UpperCAmelCase						=     [seq.replace(""" """					,       """"""       ) for seq in decoded_tok]

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase						=     self.get_image_processor()
         UpperCAmelCase						=     self.get_tokenizer()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase						=     None
         UpperCAmelCase						=     self.prepare_image_inputs()

         UpperCAmelCase						=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

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





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase						=     self.get_image_processor()
         UpperCAmelCase						=     self.get_tokenizer()

         UpperCAmelCase						=     MgpstrProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase						=     torch.randn(1					,       2_7					,       3_8       )
         UpperCAmelCase						=     torch.randn(1					,       2_7					,       5_0_2_5_7       )
         UpperCAmelCase						=     torch.randn(1					,       2_7					,       3_0_5_2_2       )

         UpperCAmelCase						=     processor.batch_decode([char_input, bpe_input, wp_input]       )

         self.assertListEqual(list(results.keys()       )					,       ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""]       )







from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


__a     =			logging.get_logger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   try:
      with open(lowerCAmelCase_  ,						"""rb"""   ) as flax_state_f:
         UpperCAmelCase							=     from_bytes(lowerCAmelCase_  ,						flax_state_f.read()   )
   except UnpicklingError as e:
      try:
         with open(lowerCAmelCase_   ) as f:
            if f.read().startswith("""version"""   ):
               raise OSError(
                   """You seem to have cloned a repository without having git-lfs installed. Please"""
                   """ install git-lfs and run `git lfs install` followed by `git lfs pull` in the"""
                   """ folder you cloned."""   )
            else:
               raise ValueError from e
      except (UnicodeDecodeError, ValueError):
         raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """   )

   return load_flax_weights_in_pytorch_model(lowerCAmelCase_  ,						lowerCAmelCase_   )







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:

   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          """Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see"""
          """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
          """ instructions."""   )
      raise

   # check if we have bf16 weights
   UpperCAmelCase							=     flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase_   : x.dtype == jnp.bfloataa  ,						lowerCAmelCase_   )   ).values()
   if any(lowerCAmelCase_   ):
      # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

      # and bf16 is not fully supported in PT yet.
      logger.warning(
          """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
          """before loading those in PyTorch model."""   )
      UpperCAmelCase							=     jax.tree_util.tree_map(
          lambda lowerCAmelCase_   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     """"""

   UpperCAmelCase							=     flatten_dict(lowerCAmelCase_  ,						sep="""."""   )
   UpperCAmelCase							=     pt_model.state_dict()

   # keep track of unexpected & missing keys
   UpperCAmelCase							=     []
   UpperCAmelCase							=     set(pt_model_dict.keys()   )

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      UpperCAmelCase							=     flax_key_tuple.split("""."""   )

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     jnp.transpose(lowerCAmelCase_  ,						(3, 2, 0, 1)   )
      elif flax_key_tuple_array[-1] == "kernel":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(lowerCAmelCase_   ):
            UpperCAmelCase							=     (
                flax_key_tuple_string.replace("""_0"""  ,						""".0"""   )
                .replace("""_1"""  ,						""".1"""   )
                .replace("""_2"""  ,						""".2"""   )
                .replace("""_3"""  ,						""".3"""   )
                .replace("""_4"""  ,						""".4"""   )
                .replace("""_5"""  ,						""".5"""   )
                .replace("""_6"""  ,						""".6"""   )
                .replace("""_7"""  ,						""".7"""   )
                .replace("""_8"""  ,						""".8"""   )
                .replace("""_9"""  ,						""".9"""   )
            )

      UpperCAmelCase							=     """.""".join(lowerCAmelCase_   )

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )
         else:
            # add weight to pytorch dict
            UpperCAmelCase							=     np.asarray(lowerCAmelCase_   ) if not isinstance(lowerCAmelCase_  ,						np.ndarray   ) else flax_tensor
            UpperCAmelCase							=     torch.from_numpy(lowerCAmelCase_   )
            # remove from missing keys
            missing_keys.remove(lowerCAmelCase_   )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(lowerCAmelCase_   )

   pt_model.load_state_dict(lowerCAmelCase_   )

   # re-transform missing_keys to list
   UpperCAmelCase							=     list(lowerCAmelCase_   )

   if len(lowerCAmelCase_   ) > 0:
      logger.warning(
          """Some weights of the Flax model were not used when initializing the PyTorch model"""
          F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
          F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
          """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
          F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
          """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
          """ FlaxBertForSequenceClassification model)."""   )
   if len(lowerCAmelCase_   ) > 0:
      logger.warning(
          F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
          F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
          """ use it for predictions and inference."""   )

   return pt_model



