






"""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        snake_case   (     A__			,A__		):
			try:
						with open(A__			,"rb"		) as flax_state_f:
									UpperCAmelCase_  :				Any      =      from_bytes(A__			,flax_state_f.read()		)
			except UnpicklingError as e:
						try:
									with open(A__		) 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(A__			,A__		)




def        snake_case   (     A__			,A__		):

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

			UpperCAmelCase_  :				Optional[Any]      =      ""

			UpperCAmelCase_  :				Optional[Any]      =      flatten_dict(A__			,sep="."		)
			UpperCAmelCase_  :				Union[str, Any]      =      pt_model.state_dict()

			# keep track of unexpected & missing keys
			UpperCAmelCase_  :				Union[str, Any]      =      []
			UpperCAmelCase_  :				Optional[Any]      =      set(pt_model_dict.keys()		)

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

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									UpperCAmelCase_  :				List[Any]      =      flax_key_tuple_array[:-1] + ["weight"]
									UpperCAmelCase_  :				Dict      =      jnp.transpose(A__			,(3, 2, 0, 1)		)
						elif flax_key_tuple_array[-1] == "kernel":
									UpperCAmelCase_  :				Optional[int]      =      flax_key_tuple_array[:-1] + ["weight"]
									UpperCAmelCase_  :				str      =      flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									UpperCAmelCase_  :				Optional[Any]      =      flax_key_tuple_array[:-1] + ["weight"]

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(A__		):
												UpperCAmelCase_  :				Optional[int]      =      (
												    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"		)
												)

						UpperCAmelCase_  :				List[Any]      =      ".".join(A__		)

						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
												UpperCAmelCase_  :				List[str]      =      np.asarray(A__		) if not isinstance(A__			,np.ndarray		) else flax_tensor
												UpperCAmelCase_  :				Dict      =      torch.from_numpy(A__		)
												# remove from missing keys
												missing_keys.remove(A__		)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(A__		)

			pt_model.load_state_dict(A__		)

			# re-transform missing_keys to list
			UpperCAmelCase_  :				Dict      =      list(A__		)

			if len(A__		) > 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(A__		) > 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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       			=   {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''CLIPTokenizerFast''']

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''CLIPFeatureExtractor''']
			lowerCamelCase_       			=   ['''CLIPImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CLIPModel''',
			    '''CLIPPreTrainedModel''',
			    '''CLIPTextModel''',
			    '''CLIPTextModelWithProjection''',
			    '''CLIPVisionModel''',
			    '''CLIPVisionModelWithProjection''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFCLIPModel''',
			    '''TFCLIPPreTrainedModel''',
			    '''TFCLIPTextModel''',
			    '''TFCLIPVisionModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''FlaxCLIPModel''',
			    '''FlaxCLIPPreTrainedModel''',
			    '''FlaxCLIPTextModel''',
			    '''FlaxCLIPTextPreTrainedModel''',
			    '''FlaxCLIPVisionModel''',
			    '''FlaxCLIPVisionPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_clip import (
			    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    CLIPConfig,
			    CLIPOnnxConfig,
			    CLIPTextConfig,
			    CLIPVisionConfig,
			)
			from .processing_clip import CLIPProcessor
			from .tokenization_clip import CLIPTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_clip_fast import CLIPTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_clip import CLIPFeatureExtractor
						from .image_processing_clip import CLIPImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_clip import (
						    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CLIPModel,
						    CLIPPreTrainedModel,
						    CLIPTextModel,
						    CLIPTextModelWithProjection,
						    CLIPVisionModel,
						    CLIPVisionModelWithProjection,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_clip import (
						    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCLIPModel,
						    TFCLIPPreTrainedModel,
						    TFCLIPTextModel,
						    TFCLIPVisionModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_clip import (
						    FlaxCLIPModel,
						    FlaxCLIPPreTrainedModel,
						    FlaxCLIPTextModel,
						    FlaxCLIPTextPreTrainedModel,
						    FlaxCLIPVisionModel,
						    FlaxCLIPVisionPreTrainedModel,
						)


else:
			import sys

			lowerCamelCase_       			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



