



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 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Dict    )				->			Dict:






      '''simple docstring'''



      try:
            with open(_snake_case      ,"""rb"""    ) as flax_state_f:
                  UpperCAmelCase_=       from_bytes(_snake_case      ,flax_state_f.read()    )
      except UnpicklingError as e:
            try:
                  with open(_snake_case    ) 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(_snake_case      ,_snake_case    )



def 	__a					(   lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Optional[int]    )				->			Any:






      '''simple docstring'''



      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      ,_snake_case    )    ).values()
      if any(_snake_case    ):
            # 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      ,_snake_case    )

      UpperCAmelCase_=       ""

      UpperCAmelCase_=       flatten_dict(_snake_case      ,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(_snake_case      ,(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(_snake_case    ):
                        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(_snake_case    )

            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(_snake_case    ) if not isinstance(_snake_case      ,np.ndarray    ) else flax_tensor
                        UpperCAmelCase_=       torch.from_numpy(_snake_case    )
                        # remove from missing keys
                        missing_keys.remove(_snake_case    )
            else:
                  # weight is not expected by PyTorch model
                  unexpected_keys.append(_snake_case    )

      pt_model.load_state_dict(_snake_case    )

      # re-transform missing_keys to list
      UpperCAmelCase_=       list(_snake_case    )

      if len(_snake_case    ) > 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(_snake_case    ) > 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









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       tempfile.mkdtemp()

							UpperCAmelCase_=       BlipImageProcessor()
							UpperCAmelCase_=       GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""					)

							UpperCAmelCase_=       BlipaProcessor(__UpperCAmelCase						,  __UpperCAmelCase					)

							processor.save_pretrained(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  **__UpperCAmelCase     :						Union[str, Any]					)      ->			int:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).tokenizer




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  **__UpperCAmelCase     :						str					)      ->			Optional[int]:
							return AutoProcessor.from_pretrained(self.tmpdirname						,  **__UpperCAmelCase					).image_processor




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			List[Any]:
							shutil.rmtree(self.tmpdirname					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Any:
							UpperCAmelCase_=       [np.random.randint(255						,  size=(3, 30, 400)						,  dtype=np.uinta					)]

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

							return image_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       BlipaProcessor(tokenizer=self.get_tokenizer()						,  image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

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

							UpperCAmelCase_=       BlipaProcessor.from_pretrained(
							    self.tmpdirname						,  bos_token="""(BOS)"""						,  eos_token="""(EOS)"""						,  do_normalize=__UpperCAmelCase						,  padding_value=1.0					)

							self.assertEqual(processor.tokenizer.get_vocab()						,  tokenizer_add_kwargs.get_vocab()					)
							self.assertIsInstance(processor.tokenizer						,  __UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[int]:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       self.prepare_image_inputs()

							UpperCAmelCase_=       image_processor(__UpperCAmelCase						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processor(images=__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

							UpperCAmelCase_=       """lower newer"""

							UpperCAmelCase_=       processor(text=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer(__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Any:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor.batch_decode(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.batch_decode(__UpperCAmelCase					)

							self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       self.get_image_processor()
							UpperCAmelCase_=       self.get_tokenizer()

							UpperCAmelCase_=       BlipaProcessor(tokenizer=__UpperCAmelCase						,  image_processor=__UpperCAmelCase					)

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

							UpperCAmelCase_=       processor(text=__UpperCAmelCase						,  images=__UpperCAmelCase					)

							# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
							self.assertListEqual(list(inputs.keys()					)						,  ["""pixel_values""", """input_ids""", """attention_mask"""]					)




