




'''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


UpperCamelCase__					    =    logging.get_logger(__name__)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict  ):
					__lowerCAmelCase            = R"\w+[.]\d+"
					__lowerCAmelCase            = re.findall(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
					for pat in pats:
										__lowerCAmelCase            = key.replace(SCREAMING_SNAKE_CASE_   ,   "_".join(pat.split("."  )  )  )
					return key







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       List[str]  ):
					__lowerCAmelCase            = 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)
					):
										__lowerCAmelCase            = 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:
										__lowerCAmelCase            = 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:
										__lowerCAmelCase            = pt_tuple_key[:-1] + ("embedding",)
										return renamed_pt_tuple_key, pt_tensor

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

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

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

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

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

					__lowerCAmelCase            = flatten_dict(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = {}

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

										# Correctly rename weight parameters
										__lowerCAmelCase     ,							__lowerCAmelCase            = 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
										__lowerCAmelCase            = jnp.asarray(SCREAMING_SNAKE_CASE_  )

					return unflatten_dict(SCREAMING_SNAKE_CASE_  )










import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class     a__					(      snake_case__   ):
						_a							:					Optional[Any]      =					"""M-CLIP"""






						def __init__(     self	,    _A=1_0_2_4	,    _A=7_6_8	,    **_A    ):




											"""simple docstring"""



											__lowerCAmelCase            = transformerDimSize
											__lowerCAmelCase            = imageDimSize
											super().__init__(**_A    )






class     a__					(      snake_case__   ):
						_a							:					Tuple      =					MCLIPConfig






						def __init__(     self	,    _A	,    *_A	,    **_A    ):




											"""simple docstring"""



											super().__init__(_A	,    *_A	,    **_A    )
											__lowerCAmelCase            = XLMRobertaModel(_A    )
											__lowerCAmelCase            = torch.nn.Linear(
											    in_features=config.transformerDimensions	,    out_features=config.numDims    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.transformer(input_ids=_A	,    attention_mask=_A    )[0]
											__lowerCAmelCase            = (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
											return self.LinearTransformation(_A    ), embs




