






"""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


_lowerCAmelCase         =							logging.get_logger(__name__)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Union[str, Any]	    =   R"""\w+[.]\d+"""
	A_		:						List[str]	    =   re.findall(snake_case__   ,   snake_case__					)
	for pat in pats:
		A_		:						Any	    =   key.replace(snake_case__   ,   """_""".join(pat.split("""."""					)					)					)
	return key






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Any	    =   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)
	):
		A_		:						Any	    =   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:
		A_		:						Tuple	    =   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:
		A_		:						List[str]	    =   pt_tuple_key[:-1] + ("""embedding""",)
		return renamed_pt_tuple_key, pt_tensor

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

	# linear layer
	A_		:						List[str]	    =   pt_tuple_key[:-1] + ("""kernel""",)
	if pt_tuple_key[-1] == "weight":
		A_		:						str	    =   pt_tensor.T
		return renamed_pt_tuple_key, pt_tensor

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

	# old PyTorch layer norm bias
	A_		:						Optional[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 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=42					):
	# Step 1: Convert pytorch tensor to numpy
	A_		:						List[str]	    =   {k: v.numpy() for k, v in pt_state_dict.items()}

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

	A_		:						Optional[Any]	    =   flatten_dict(snake_case__					)
	A_		:						Tuple	    =   {}

	# Need to change some parameters name to match Flax names
	for pt_key, pt_tensor in pt_state_dict.items():
		A_		:						Any	    =   rename_key(snake_case__					)
		A_		:						str	    =   tuple(renamed_pt_key.split("""."""					)					)

		# Correctly rename weight parameters
		A_		:						Tuple	    =   rename_key_and_reshape_tensor(snake_case__   ,   snake_case__   ,   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
		A_		:						Optional[int]	    =   jnp.asarray(snake_case__					)

	return unflatten_dict(snake_case__					)









"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				List[Any]    =				"""ViTImageProcessor"""
	_A       :				Optional[Any]    =				("""CLIPTokenizer""", """CLIPTokenizerFast""")





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead."""     ,     lowerCAmelCase_     ,     )
			A_		:						Union[str, Any]	    =   kwargs.pop("""feature_extractor"""							)

		A_		:						Optional[int]	    =   image_processor if image_processor is not None else feature_extractor
		if image_processor is None:
			raise ValueError("""You need to specify an `image_processor`."""							)
		if tokenizer is None:
			raise ValueError("""You need to specify a `tokenizer`."""							)

		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if text is None and visual_prompt is None and images is None:
			raise ValueError("""You have to specify either text, visual prompt or images."""							)

		if text is not None and visual_prompt is not None:
			raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""							)

		if text is not None:
			A_		:						Tuple	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None:
			A_		:						Union[str, Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						Any	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None and images is not None:
			A_		:						List[str]	    =   {
			    """pixel_values""": image_features.pixel_values,
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		elif text is not None and images is not None:
			A_		:						str	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		elif visual_prompt is not None:
			A_		:						Any	    =   {
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor_class





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor

