




"""simple docstring"""






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      _snake_case			(				lowercase__							,    lowercase__		):
				try:
								with open(lowercase__							,    'rb'		) as flax_state_f:
												_lowerCamelCase     :			Union[str, Any]    =    from_bytes(lowercase__							,    flax_state_f.read()		)
				except UnpicklingError as e:
								try:
												with open(lowercase__		) 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(lowercase__							,    lowercase__		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				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
				_lowerCamelCase     :			Union[str, Any]    =    flatten_dict(jax.tree_util.tree_map(lambda lowercase__		: x.dtype == jnp.bfloataa							,    lowercase__		)		).values()
				if any(lowercase__		):
								# 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.'		)
								_lowerCamelCase     :			Any    =    jax.tree_util.tree_map(
								    lambda lowercase__		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params							,    lowercase__		)

				_lowerCamelCase     :			Tuple    =    ''

				_lowerCamelCase     :			Union[str, Any]    =    flatten_dict(lowercase__							,    sep='.'		)
				_lowerCamelCase     :			List[Any]    =    pt_model.state_dict()

				# keep track of unexpected & missing keys
				_lowerCamelCase     :			str    =    []
				_lowerCamelCase     :			List[str]    =    set(pt_model_dict.keys()		)

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

								if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
												_lowerCamelCase     :			str    =    flax_key_tuple_array[:-1] + ['weight']
												_lowerCamelCase     :			Optional[Any]    =    jnp.transpose(lowercase__							,    (3, 2, 0, 1)		)
								elif flax_key_tuple_array[-1] == "kernel":
												_lowerCamelCase     :			List[Any]    =    flax_key_tuple_array[:-1] + ['weight']
												_lowerCamelCase     :			Tuple    =    flax_tensor.T
								elif flax_key_tuple_array[-1] == "scale":
												_lowerCamelCase     :			str    =    flax_key_tuple_array[:-1] + ['weight']

								if "time_embedding" not in flax_key_tuple_array:
												for i, flax_key_tuple_string in enumerate(lowercase__		):
																_lowerCamelCase     :			List[str]    =    (
																    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'		)
																)

								_lowerCamelCase     :			int    =    '.'.join(lowercase__		)

								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
																_lowerCamelCase     :			str    =    np.asarray(lowercase__		) if not isinstance(lowercase__							,    np.ndarray		) else flax_tensor
																_lowerCamelCase     :			List[str]    =    torch.from_numpy(lowercase__		)
																# remove from missing keys
																missing_keys.remove(lowercase__		)
								else:
												# weight is not expected by PyTorch model
												unexpected_keys.append(lowercase__		)

				pt_model.load_state_dict(lowercase__		)

				# re-transform missing_keys to list
				_lowerCamelCase     :			Dict    =    list(lowercase__		)

				if len(lowercase__		) > 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(lowercase__		) > 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'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image







class 							A__    (       A__  ):
  A__		 =				(
      'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
      'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
      'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






       return self.pre_processor(text=[label]							,					images=[image]							,					padding=_a							,					return_tensors='pt' )





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
