
"""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


lowerCamelCase__           =	logging.get_logger(__name__)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):







	try:
		with open(__UpperCamelCase ,						'rb'	) as flax_state_f:
			__lowerCAmelCase    :		str		    =   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 ,						_UpperCamelCase	):







	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    :		Optional[Any]		    =   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.'	)
		__lowerCAmelCase    :		List[str]		    =   jax.tree_util.tree_map(
		    lambda _UpperCamelCase	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params ,						__UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   ''''''

	__lowerCAmelCase    :		Any		    =   flatten_dict(__UpperCamelCase ,						sep='.'	)
	__lowerCAmelCase    :		List[Any]		    =   pt_model.state_dict()

	# keep track of unexpected & missing keys
	__lowerCAmelCase    :		str		    =   []
	__lowerCAmelCase    :		Optional[int]		    =   set(pt_model_dict.keys()	)

	for flax_key_tuple, flax_tensor in flax_state_dict.items():
		__lowerCAmelCase    :		Union[str, Any]		    =   flax_key_tuple.split('.'	)

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

		if "time_embedding" not in flax_key_tuple_array:
			for i, flax_key_tuple_string in enumerate(__UpperCamelCase	):
				__lowerCAmelCase    :		Union[str, Any]		    =   (
				    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    :		str		    =   '''.'''.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
				__lowerCAmelCase    :		str		    =   np.asarray(__UpperCamelCase	) if not isinstance(__UpperCamelCase ,						np.ndarray	) else flax_tensor
				__lowerCAmelCase    :		Dict		    =   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
	__lowerCAmelCase    :		str		    =   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

"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			['image_processor', 'tokenizer']
		A_		: Optional[Any]            =			'BlipImageProcessor'
		A_		: int            =			'AutoTokenizer'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   False
			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor

		def __call__(   self ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			if images is None and text is None:
				raise ValueError('You have to specify either images or text.'				)

			# Get only text
			if images is None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer
				__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				return text_encoding

			# add pixel_values
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE				)

			if text is not None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
			else:
				__lowerCAmelCase    :		int		    =   None

			if text_encoding is not None:
				encoding_image_processor.update(_SCREAMING_SNAKE_CASE				)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.tokenizer.model_input_names
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)