






"""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 _lowerCAmelCase	(  __lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						try:
												with open(__lowerCamelCase ,						"rb"   ) as flax_state_f:
																		__magic_name__            =     from_bytes(__lowerCamelCase ,						flax_state_f.read()   )
						except UnpicklingError as e:
												try:
																		with open(__lowerCamelCase   ) 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(__lowerCamelCase ,						__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:str ,						__lowerCamelCase:Union[str, Any]   ):

						'''simple docstring'''






						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
						__magic_name__            =     flatten_dict(jax.tree_util.tree_map(lambda __lowerCamelCase   : x.dtype == jnp.bfloataa ,						__lowerCamelCase   )   ).values()
						if any(__lowerCamelCase   ):
												# 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."   )
												__magic_name__            =     jax.tree_util.tree_map(
												    lambda __lowerCamelCase   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params ,						__lowerCamelCase   )

						__magic_name__            =     ""

						__magic_name__            =     flatten_dict(__lowerCamelCase ,						sep="."   )
						__magic_name__            =     pt_model.state_dict()

						# keep track of unexpected & missing keys
						__magic_name__            =     []
						__magic_name__            =     set(pt_model_dict.keys()   )

						for flax_key_tuple, flax_tensor in flax_state_dict.items():
												__magic_name__            =     flax_key_tuple.split("."   )

												if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																		__magic_name__            =     flax_key_tuple_array[:-1] + ["weight"]
																		__magic_name__            =     jnp.transpose(__lowerCamelCase ,						(3, 2, 0, 1)   )
												elif flax_key_tuple_array[-1] == "kernel":
																		__magic_name__            =     flax_key_tuple_array[:-1] + ["weight"]
																		__magic_name__            =     flax_tensor.T
												elif flax_key_tuple_array[-1] == "scale":
																		__magic_name__            =     flax_key_tuple_array[:-1] + ["weight"]

												if "time_embedding" not in flax_key_tuple_array:
																		for i, flax_key_tuple_string in enumerate(__lowerCamelCase   ):
																								__magic_name__            =     (
																								    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"   )
																								)

												__magic_name__            =     ".".join(__lowerCamelCase   )

												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
																								__magic_name__            =     np.asarray(__lowerCamelCase   ) if not isinstance(__lowerCamelCase ,						np.ndarray   ) else flax_tensor
																								__magic_name__            =     torch.from_numpy(__lowerCamelCase   )
																								# remove from missing keys
																								missing_keys.remove(__lowerCamelCase   )
												else:
																		# weight is not expected by PyTorch model
																		unexpected_keys.append(__lowerCamelCase   )

						pt_model.load_state_dict(__lowerCamelCase   )

						# re-transform missing_keys to list
						__magic_name__            =     list(__lowerCamelCase   )

						if len(__lowerCamelCase   ) > 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(__lowerCamelCase   ) > 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_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										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:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

										if text is not None:
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
										else:
																__magic_name__            =     None

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

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



