





'''simple docstring'''







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case_     :       Dict						=  logging.get_logger(__name__)





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Tuple     						=  R'''\w+[.]\d+'''
						UpperCAmelCase_			:      Tuple     						=  re.findall(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						for pat in pats:
												UpperCAmelCase_			:      Optional[int]     						=  key.replace(SCREAMING_SNAKE_CASE__,			'''_'''.join(pat.split('''.'''   )   )   )
						return key





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			Dict   ) ->	str:
						UpperCAmelCase_			:      List[str]     						=  pt_tuple_key[:-1] + ('''scale''',)
						if (
						    any('''norm''' in str_ for str_ in pt_tuple_key   )
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												UpperCAmelCase_			:      int     						=  pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												UpperCAmelCase_			:      Dict     						=  pt_tuple_key[:-1] + ('''scale''',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												UpperCAmelCase_			:      str     						=  pt_tuple_key[:-1] + ('''embedding''',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase_			:      List[Any]     						=  pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												UpperCAmelCase_			:      Union[str, Any]     						=  pt_tensor.transpose(2,			3,			1,			0   )
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase_			:      Tuple     						=  pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase_			:      str     						=  pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase_			:      Dict     						=  pt_tuple_key[:-1] + ('''weight''',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase_			:      int     						=  pt_tuple_key[:-1] + ('''bias''',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Dict=42   ) ->	List[str]:
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase_			:      int     						=  {k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						UpperCAmelCase_			:      Optional[int]     						=  flax_model.init_weights(PRNGKey(SCREAMING_SNAKE_CASE__   )   )

						UpperCAmelCase_			:      Any     						=  flatten_dict(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[Any]     						=  {}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase_			:      List[str]     						=  rename_key(SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_			:      Optional[int]     						=  tuple(renamed_pt_key.split('''.'''   )   )

												# Correctly rename weight parameters
												UpperCAmelCase_						,       UpperCAmelCase_			:      Optional[Any]     						=  rename_key_and_reshape_tensor(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

        # also add unexpected weight so that warning is thrown
												UpperCAmelCase_			:      Dict     						=  jnp.asarray(SCREAMING_SNAKE_CASE__   )

						return unflatten_dict(SCREAMING_SNAKE_CASE__   )












'''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.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
					from PIL import Image


class 					__a      (lowerCamelCase     ):
							__a					:   int									=       "dandelin/vilt-b32-finetuned-vqa"
							__a					:   Any									=       (
							    "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
							    "image containing the information, as well as a `question` which should be the question in English. It "
							    "returns a text that is the answer to the question."
							)
							__a					:   Any									=       "image_qa"
							__a					:   str									=       AutoProcessor
							__a					:   Any									=       AutoModelForVisualQuestionAnswering

							__a					:   List[Any]									=       ["image", "text"]
							__a					:   int									=       ["text"]

							def __init__(							self							:     Tuple       ,     *__magic_name__							:     Any       ,     **__magic_name__							:     Any   )					->       Tuple:







													"""simple docstring"""
													requires_backends(self       ,     ['''vision''']   )
													super().__init__(*__magic_name__       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     "Image"       ,     __magic_name__							:     str   )					->       Tuple:







													"""simple docstring"""
													return self.pre_processor(__magic_name__       ,     __magic_name__       ,     return_tensors='''pt'''   )

							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     List[str]   )					->       Optional[Any]:







													"""simple docstring"""
													with torch.no_grad():
																			return self.model(**__magic_name__   ).logits



							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     int   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  outputs.argmax(-1   ).item()
													return self.model.config.idalabel[idx]





