
from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

UpperCAmelCase	      = logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase   )
class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    *snake_case		,    **snake_case       ):
		super().__init__(*snake_case		,    **snake_case       )
		self.check_model_type(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=None		,    snake_case=None		,    snake_case=None		,    **snake_case       ):
		lowercase  ,					lowercase       =    {}, {}
		if padding is not None:
			lowercase       =    padding
		if truncation is not None:
			lowercase       =    truncation
		if top_k is not None:
			lowercase       =    top_k
		return preprocess_params, {}, postprocess_params





	def __call__(       self		,    snake_case		,    snake_case = None		,    **snake_case       ):
		if isinstance(snake_case		,    (Image.Image, str)       ) and isinstance(snake_case		,    snake_case       ):
			lowercase       =    {'image': image, 'question': question}
		else:
			lowercase       =    image
		lowercase       =    super().__call__(snake_case		,    **snake_case       )
		return results





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=False		,    snake_case=False       ):
		lowercase       =    load_image(inputs['image']       )
		lowercase       =    self.tokenizer(
		    inputs['question']		,    return_tensors=self.framework		,    padding=snake_case		,    truncation=snake_case       )
		lowercase       =    self.image_processor(images=snake_case		,    return_tensors=self.framework       )
		model_inputs.update(snake_case       )
		return model_inputs





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		lowercase       =    self.model(**snake_case       )
		return model_outputs





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case=5       ):
		if top_k > self.model.config.num_labels:
			lowercase       =    self.model.config.num_labels

		if self.framework == "pt":
			lowercase       =    model_outputs.logits.sigmoid()[0]
			lowercase  ,					lowercase       =    probs.topk(snake_case       )
		else:
			raise ValueError(F'''Unsupported framework: {self.framework}'''       )

		lowercase       =    scores.tolist()
		lowercase       =    ids.tolist()
		return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case		,    snake_case       )]



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


UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    r'\w+[.]\d+'
	lowercase       =    re.findall(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	for pat in pats:
		lowercase       =    key.replace(__SCREAMING_SNAKE_CASE ,							'_'.join(pat.split('.'							)							)							)
	return key






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    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)
	):
		lowercase       =    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:
		lowercase       =    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:
		lowercase       =    pt_tuple_key[:-1] + ('embedding',)
		return renamed_pt_tuple_key, pt_tensor

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

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

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

	# old PyTorch layer norm bias
	lowercase       =    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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE=42							):
	# Step 1: Convert pytorch tensor to numpy
	lowercase       =    {k: v.numpy() for k, v in pt_state_dict.items()}

	# Step 2: Since the model is stateless, get random Flax params
	lowercase       =    flax_model.init_weights(PRNGKey(__SCREAMING_SNAKE_CASE							)							)

	lowercase       =    flatten_dict(__SCREAMING_SNAKE_CASE							)
	lowercase       =    {}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		lowercase       =    rename_key(__SCREAMING_SNAKE_CASE							)
		lowercase       =    tuple(renamed_pt_key.split('.'							)							)

		# Correctly rename weight parameters
		lowercase  ,					lowercase       =    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
		lowercase       =    jnp.asarray(__SCREAMING_SNAKE_CASE							)

	return unflatten_dict(__SCREAMING_SNAKE_CASE							)

