




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


lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      Union[str, Any]				)   ->					Union[str, Any]:
      try:
            with open(__UpperCAmelCase     ,     'rb'				) as flax_state_f:
                  SCREAMING_SNAKE_CASE_      		=     from_bytes(__UpperCAmelCase     ,     flax_state_f.read()				)
      except UnpicklingError as e:
            try:
                  with open(__UpperCAmelCase				) 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(__UpperCAmelCase     ,     __UpperCAmelCase				)





def    UpperCAmelCase_				( __UpperCAmelCase		:      str     ,     __UpperCAmelCase		:      Optional[int]				)   ->					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
      SCREAMING_SNAKE_CASE_      		=     flatten_dict(jax.tree_util.tree_map(lambda __UpperCAmelCase				: x.dtype == jnp.bfloataa     ,     __UpperCAmelCase				)				).values()
      if any(__UpperCAmelCase				):
            # 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.'				)
            SCREAMING_SNAKE_CASE_      		=     jax.tree_util.tree_map(
                lambda __UpperCAmelCase				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params     ,     __UpperCAmelCase				)

      SCREAMING_SNAKE_CASE_      		=     ''

      SCREAMING_SNAKE_CASE_      		=     flatten_dict(__UpperCAmelCase     ,     sep='.'				)
      SCREAMING_SNAKE_CASE_      		=     pt_model.state_dict()

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

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            SCREAMING_SNAKE_CASE_      		=     flax_key_tuple.split('.'				)

            if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
                  SCREAMING_SNAKE_CASE_      		=     flax_key_tuple_array[:-1] + ['weight']
                  SCREAMING_SNAKE_CASE_      		=     jnp.transpose(__UpperCAmelCase     ,     (3, 2, 0, 1)				)
            elif flax_key_tuple_array[-1] == "kernel":
                  SCREAMING_SNAKE_CASE_      		=     flax_key_tuple_array[:-1] + ['weight']
                  SCREAMING_SNAKE_CASE_      		=     flax_tensor.T
            elif flax_key_tuple_array[-1] == "scale":
                  SCREAMING_SNAKE_CASE_      		=     flax_key_tuple_array[:-1] + ['weight']

            if "time_embedding" not in flax_key_tuple_array:
                  for i, flax_key_tuple_string in enumerate(__UpperCAmelCase				):
                        SCREAMING_SNAKE_CASE_      		=     (
                            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'				)
                        )

            SCREAMING_SNAKE_CASE_      		=     '.'.join(__UpperCAmelCase				)

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

      pt_model.load_state_dict(__UpperCAmelCase				)

      # re-transform missing_keys to list
      SCREAMING_SNAKE_CASE_      		=     list(__UpperCAmelCase				)

      if len(__UpperCAmelCase				) > 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(__UpperCAmelCase				) > 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



'''simple docstring'''
import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCAmelCase						=					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class        lowerCAmelCase_  (     unittest.TestCase	):






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=7			,     _UpperCamelCase=3			,     _UpperCamelCase=18			,     _UpperCamelCase=30			,     _UpperCamelCase=400			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     )->      Dict:
            _A				=							size if size is not None else {'height': 20, 'width': 20}
            _A				=							parent
            _A				=							batch_size
            _A				=							num_channels
            _A				=							image_size
            _A				=							min_resolution
            _A				=							max_resolution
            _A				=							size
            _A				=							do_normalize
            _A				=							do_convert_rgb
            _A				=							[512, 1024, 2048, 4096]
            _A				=							patch_size if patch_size is not None else {'height': 16, 'width': 16}






       def   UpperCamelCase						( self   )->      Dict:
            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
            _A				=							Image.open(requests.get(_UpperCamelCase			,     stream=_UpperCamelCase   ).raw   ).convert('RGB'   )
            return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							PixaStructImageProcessingTester(self   )






       @property
       def   UpperCamelCase						( self   )->      Tuple:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processor_tester.prepare_dummy_image()

            _A				=							self.image_processing_class(**self.image_processor_dict   )
            _A				=							2048

            _A				=							image_processor(_UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   )
            self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,     torch.tensor(0.0606   )			,     atol=1e-3			,     rtol=1e-3   )   )






       def   UpperCamelCase						( self   )->      int:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      List[str]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            _A				=							True

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 with self.assertRaises(_UpperCamelCase   ):
                      _A				=							image_processor(
                          image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches

                 _A				=							'Hello'

                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase			,     header_text=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random numpy tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     numpify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     np.ndarray   )

            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PyTorch tensors
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase			,     torchify=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     torch.Tensor   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * self.image_processor_tester.num_channels
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11      ,							reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'      ,							)
@require_torch
@require_vision
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=PixaStructImageProcessor if is_vision_available() else None






       def   UpperCamelCase						( self   )->      str:
            _A				=							PixaStructImageProcessingTester(self			,     num_channels=4   )
            _A				=							3






       @property
       def   UpperCamelCase						( self   )->      str:
            return self.image_processor_tester.prepare_image_processor_dict()






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_normalize'   )   )
            self.assertTrue(hasattr(_UpperCamelCase			,     'do_convert_rgb'   )   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            # Initialize image_processor
            _A				=							self.image_processing_class(**self.image_processor_dict   )
            # create random PIL images
            _A				=							prepare_image_inputs(self.image_processor_tester			,     equal_resolution=_UpperCamelCase   )
            for image in image_inputs:
                 self.assertIsInstance(_UpperCamelCase			,     Image.Image   )

            # Test not batched input
            _A				=							(
                (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
                * (self.image_processor_tester.num_channels - 1)
            ) + 2

            for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 _A				=							image_processor(
                     image_inputs[0]			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (1, max_patch, expected_hidden_dim)			,     )

                 # Test batched
                 _A				=							image_processor(
                     _UpperCamelCase			,     return_tensors='pt'			,     max_patches=_UpperCamelCase   ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape			,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,     )





