import unittest

import numpy as np
from datasets import load_dataset

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

if is_vision_available():
							from PIL import Image

							from transformers import BeitImageProcessor


class   lowercase      (       unittest.TestCase       ):




							def __init__(  self				:   Any		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   Any=7		,							_UpperCamelCase				:   Any=3		,							_UpperCamelCase				:   Tuple=18		,							_UpperCamelCase				:   List[str]=30		,							_UpperCamelCase				:   Optional[int]=400		,							_UpperCamelCase				:   Optional[Any]=True		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   List[str]=None		,							_UpperCamelCase				:   Any=True		,							_UpperCamelCase				:   Dict=[0.5, 0.5, 0.5]		,							_UpperCamelCase				:   Optional[Any]=[0.5, 0.5, 0.5]		,							_UpperCamelCase				:   int=False		,							)   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    size if size is not None else {"height": 20, "width": 20}
												SCREAMING_SNAKE_CASE           =    crop_size if crop_size is not None else {"height": 18, "width": 18}
												SCREAMING_SNAKE_CASE           =    parent
												SCREAMING_SNAKE_CASE           =    batch_size
												SCREAMING_SNAKE_CASE           =    num_channels
												SCREAMING_SNAKE_CASE           =    image_size
												SCREAMING_SNAKE_CASE           =    min_resolution
												SCREAMING_SNAKE_CASE           =    max_resolution
												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												SCREAMING_SNAKE_CASE           =    do_center_crop
												SCREAMING_SNAKE_CASE           =    crop_size
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean
												SCREAMING_SNAKE_CASE           =    image_std
												SCREAMING_SNAKE_CASE           =    do_reduce_labels





							def 		__snake_case(  self				:   str )   ->			Tuple:



												'''simple docstring'''

												return {
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "do_center_crop": self.do_center_crop,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_reduce_labels": self.do_reduce_labels,
												}





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    load_dataset("hf-internal-testing/fixtures_ade20k" ,	split="test"					)

					SCREAMING_SNAKE_CASE           =    Image.open(dataset[0]["file"]					)
					SCREAMING_SNAKE_CASE           =    Image.open(dataset[1]["file"]					)

					return image, map





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE           =    load_dataset("hf-internal-testing/fixtures_ade20k" ,	split="test"					)

					SCREAMING_SNAKE_CASE           =    Image.open(ds[0]["file"]					)
					SCREAMING_SNAKE_CASE           =    Image.open(ds[1]["file"]					)
					SCREAMING_SNAKE_CASE           =    Image.open(ds[2]["file"]					)
					SCREAMING_SNAKE_CASE           =    Image.open(ds[3]["file"]					)

					return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class   lowercase      (       a			,    unittest.TestCase       ):
							lowercase__  :    Optional[int]									=							BeitImageProcessor if is_vision_available() else None




							def 		__snake_case(  self				:   Optional[Any] )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    BeitImageProcessingTester(self )




							@property
							def 		__snake_case(  self				:   List[str] )   ->			Optional[int]:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												self.assertTrue(hasattr(_UpperCamelCase		,							"do_resize" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"size" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"do_center_crop" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"center_crop" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"do_normalize" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"image_mean" ) )
												self.assertTrue(hasattr(_UpperCamelCase		,							"image_std" ) )




							def 		__snake_case(  self				:   str )   ->			Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class.from_dict(self.image_processor_dict )
												self.assertEqual(image_processor.size		,							{"height": 20, "width": 20} )
												self.assertEqual(image_processor.crop_size		,							{"height": 18, "width": 18} )
												self.assertEqual(image_processor.do_reduce_labels		,							_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    self.image_processing_class.from_dict(
												    self.image_processor_dict		,							size=42		,							crop_size=84		,							reduce_labels=_UpperCamelCase )
												self.assertEqual(image_processor.size		,							{"height": 42, "width": 42} )
												self.assertEqual(image_processor.crop_size		,							{"height": 84, "width": 84} )
												self.assertEqual(image_processor.do_reduce_labels		,							_UpperCamelCase )




							def 		__snake_case(  self				:   List[str] )   ->			Tuple:



												'''simple docstring'''

												pass




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



												'''simple docstring'''

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

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)




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



												'''simple docstring'''

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

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)




							def 		__snake_case(  self				:   int )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE           =    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
												SCREAMING_SNAKE_CASE           =    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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							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.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE           =    prepare_image_inputs(self.image_processor_tester		,							equal_resolution=_UpperCamelCase		,							torchify=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    []
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase		,							torch.Tensor )
																	maps.append(torch.zeros(image.shape[-2:] ).long() )

												# Test not batched input
												SCREAMING_SNAKE_CASE           =    image_processing(image_inputs[0]		,							maps[0]		,							return_tensors="pt" )
												self.assertEqual(
												    encoding["pixel_values"].shape		,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(
												    encoding["labels"].shape		,							(
												        1,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(encoding["labels"].dtype		,							torch.long )
												self.assertTrue(encoding["labels"].min().item() >= 0 )
												self.assertTrue(encoding["labels"].max().item() <= 255 )

												# Test batched
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							_UpperCamelCase		,							return_tensors="pt" )
												self.assertEqual(
												    encoding["pixel_values"].shape		,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(
												    encoding["labels"].shape		,							(
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(encoding["labels"].dtype		,							torch.long )
												self.assertTrue(encoding["labels"].min().item() >= 0 )
												self.assertTrue(encoding["labels"].max().item() <= 255 )

												# Test not batched input (PIL images)
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    prepare_semantic_single_inputs()

												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							_UpperCamelCase		,							return_tensors="pt" )
												self.assertEqual(
												    encoding["pixel_values"].shape		,							(
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(
												    encoding["labels"].shape		,							(
												        1,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(encoding["labels"].dtype		,							torch.long )
												self.assertTrue(encoding["labels"].min().item() >= 0 )
												self.assertTrue(encoding["labels"].max().item() <= 255 )

												# Test batched input (PIL images)
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    prepare_semantic_batch_inputs()

												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							_UpperCamelCase		,							return_tensors="pt" )
												self.assertEqual(
												    encoding["pixel_values"].shape		,							(
												        2,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(
												    encoding["labels"].shape		,							(
												        2,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )		,							)
												self.assertEqual(encoding["labels"].dtype		,							torch.long )
												self.assertTrue(encoding["labels"].min().item() >= 0 )
												self.assertTrue(encoding["labels"].max().item() <= 255 )





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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.image_processing_class(**self.image_processor_dict )

												# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    prepare_semantic_single_inputs()
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							_UpperCamelCase		,							return_tensors="pt" )
												self.assertTrue(encoding["labels"].min().item() >= 0 )
												self.assertTrue(encoding["labels"].max().item() <= 150 )

												SCREAMING_SNAKE_CASE           =    True
												SCREAMING_SNAKE_CASE           =    image_processing(_UpperCamelCase		,							_UpperCamelCase		,							return_tensors="pt" )
												self.assertTrue(encoding["labels"].min().item() >= 0 )
												self.assertTrue(encoding["labels"].max().item() <= 255 )





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   :				Optional[Any]     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Dict					):
					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 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):

					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




