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


__lowercase									=   logging.get_logger(__name__)



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    try:
        with open(SCREAMING_SNAKE_CASE    ,     '''rb'''      ) as flax_state_f:
            __UpperCamelCase	:Union[str, Any]         =						from_bytes(SCREAMING_SNAKE_CASE    ,     flax_state_f.read()      )
    except UnpicklingError as e:
        try:
            with open(SCREAMING_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(SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      )



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''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	:Any         =						flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE      : x.dtype == jnp.bfloataa    ,     SCREAMING_SNAKE_CASE      )      ).values()
    if any(SCREAMING_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	:Any         =						jax.tree_util.tree_map(
            lambda SCREAMING_SNAKE_CASE      : params.astype(np.floataa      ) if params.dtype == jnp.bfloataa else params    ,     SCREAMING_SNAKE_CASE      )

    __UpperCamelCase	:str         =						''''''

    __UpperCamelCase	:Any         =						flatten_dict(SCREAMING_SNAKE_CASE    ,     sep='''.'''      )
    __UpperCamelCase	:List[Any]         =						pt_model.state_dict()

    # keep track of unexpected & missing keys
    __UpperCamelCase	:List[Any]         =						[]
    __UpperCamelCase	:Dict         =						set(pt_model_dict.keys()      )

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        __UpperCamelCase	:Optional[Any]         =						flax_key_tuple.split('''.'''      )

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

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE      ):
                __UpperCamelCase	:int         =						(
                    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	:List[Any]         =						'''.'''.join(SCREAMING_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	:int         =						np.asarray(SCREAMING_SNAKE_CASE      ) if not isinstance(SCREAMING_SNAKE_CASE    ,     np.ndarray      ) else flax_tensor
                __UpperCamelCase	:int         =						torch.from_numpy(SCREAMING_SNAKE_CASE      )
                # remove from missing keys
                missing_keys.remove(SCREAMING_SNAKE_CASE      )
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(SCREAMING_SNAKE_CASE      )

    pt_model.load_state_dict(SCREAMING_SNAKE_CASE      )

    # re-transform missing_keys to list
    __UpperCamelCase	:Optional[int]         =						list(SCREAMING_SNAKE_CASE      )

    if len(SCREAMING_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(SCREAMING_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







'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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

if is_vision_available():
						from PIL import Image

						from transformers import DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=42     )
											self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}     )

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				numpify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				np.ndarray     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				torchify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				torch.Tensor     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




