
'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class  A				(							__snake_case						):
     __magic_name__						=		42
     __magic_name__						=		42
     __magic_name__						=		None





class  A				(							__snake_case    ,			__snake_case						):
     __magic_name__						=		2





     @register_to_config
     def __init__(							self						,			SCREAMING_SNAKE_CASE = 0.02						,			SCREAMING_SNAKE_CASE = 100						,			SCREAMING_SNAKE_CASE = 1.007						,			SCREAMING_SNAKE_CASE = 80						,			SCREAMING_SNAKE_CASE = 0.05						,			SCREAMING_SNAKE_CASE = 50						,			) ->				Union[str, Any]:






         """simple docstring"""






         A       :   Optional[Any]					=						sigma_max

         # setable values
         A       :   int					=						None
         A       :   np.IntTensor					=						None
         A       :   torch.FloatTensor					=						None  # sigma(t_i)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				torch.FloatTensor:






         """simple docstring"""






         return sample





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				str:






         """simple docstring"""






         A       :   Optional[Any]					=						num_inference_steps
         A       :   Any					=						np.arange(0						,			self.num_inference_steps	)[::-1].copy()
         A       :   int					=						torch.from_numpy(SCREAMING_SNAKE_CASE	).to(SCREAMING_SNAKE_CASE	)
         A       :   List[Any]					=						[
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in self.timesteps
         ]
         A       :   Dict					=						torch.tensor(SCREAMING_SNAKE_CASE						,			dtype=torch.floataa						,			device=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[torch.FloatTensor, float]:






         """simple docstring"""






         if self.config.s_min <= sigma <= self.config.s_max:
             A       :   int					=						min(self.config.s_churn / self.num_inference_steps						,			2**0.5 - 1	)
         else:
             A       :   Optional[int]					=						0

         # sample eps ~ N(0, S_noise^2 * I)
         A       :   Any					=						self.config.s_noise * randn_tensor(sample.shape						,			generator=SCREAMING_SNAKE_CASE	).to(sample.device	)
         A       :   Union[str, Any]					=						sigma + gamma * sigma
         A       :   Dict					=						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = True						,			) ->				Union[KarrasVeOutput, Tuple]:






         """simple docstring"""






         A       :   Optional[Any]					=						sample_hat + sigma_hat * model_output
         A       :   Optional[int]					=						(sample_hat - pred_original_sample) / sigma_hat
         A       :   Optional[Any]					=						sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
             return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=SCREAMING_SNAKE_CASE						,			derivative=SCREAMING_SNAKE_CASE						,			pred_original_sample=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = True						,			) ->				Union[KarrasVeOutput, Tuple]:






         """simple docstring"""






         A       :   Tuple					=						sample_prev + sigma_prev * model_output
         A       :   Union[str, Any]					=						(sample_prev - pred_original_sample) / sigma_prev
         A       :   List[Any]					=						sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
             return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=SCREAMING_SNAKE_CASE						,			derivative=SCREAMING_SNAKE_CASE						,			pred_original_sample=SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         raise NotImplementedError()






'''simple docstring'''




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


lowercase			:				Dict    		=			logging.get_logger(__name__)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading a PyTorch model in Flax, 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

    if not is_sharded:
        A       :   Union[str, Any]					=						os.path.abspath(snake_case__						)
        logger.info(F'Loading PyTorch weights from {pt_path}'						)

        A       :   Any					=						torch.load(snake_case__		,				map_location='''cpu'''						)
        logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()						):,} parameters.'						)

        A       :   List[str]					=						convert_pytorch_state_dict_to_flax(snake_case__		,				snake_case__						)
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        A       :   Any					=						convert_pytorch_sharded_state_dict_to_flax(snake_case__		,				snake_case__						)
    return flax_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				):




    '''simple docstring'''



    def is_key_or_prefix_key_in_dict(snake_case__						) -> bool:
        return len(set(snake_case__						) & {key, (model_prefix,) + key}						) > 0

    # layer norm
    A       :   Union[str, Any]					=						pt_tuple_key[:-1] + ('''scale''',)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    A       :   Tuple					=						pt_tuple_key[:-1] + ('''mean''',)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    A       :   Dict					=						pt_tuple_key[:-1] + ('''var''',)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    A       :   Any					=						pt_tuple_key[:-1] + ('''embedding''',)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    A       :   Optional[Any]					=						pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(snake_case__						):
        A       :   List[Any]					=						pt_tensor.transpose(2		,				3		,				1		,				0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    A       :   Optional[int]					=						pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case__						):
        A       :   str					=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

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

    # old PyTorch layer norm bias
    A       :   List[Any]					=						pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    A       :   Dict					=						None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        A       :   List[Any]					=						pt_tuple_key[-2] + '''_g'''
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        A       :   List[str]					=						pt_tuple_key[-2] + '''_v'''
    if name is not None:
        A       :   int					=						pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						{k: v.numpy() for k, v in pt_state_dict.items()}

    A       :   int					=						flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        A       :   List[str]					=						flax_model.params['''params''']
    else:
        A       :   Dict					=						flax_model.params
    A       :   List[Any]					=						flatten_dict(snake_case__						)

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        A       :   List[str]					=						flatten_dict(flax_model.params['''batch_stats''']						)
        random_flax_state_dict.update(snake_case__						)

    A       :   int					=						{}

    A       :   List[str]					=						(model_prefix not in flax_model_params) and (
        model_prefix in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
    )
    A       :   int					=						(model_prefix in flax_model_params) and (
        model_prefix not in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        A       :   str					=						tuple(pt_key.split('''.'''						)						)

        # remove base model prefix if necessary
        A       :   Union[str, Any]					=						pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            A       :   Any					=						pt_tuple_key[1:]

        # Correctly rename weight parameters
        A,  A       :   Dict					=						rename_key_and_reshape_tensor(
            snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)

        # add model prefix if necessary
        A       :   Any					=						(model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            A       :   int					=						(model_prefix,) + flax_key

        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}.'						)

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                A       :   Tuple					=						jnp.asarray(snake_case__						)
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(snake_case__		,				snake_case__						)
                continue

            # also add unexpected weight so that warning is thrown
            A       :   List[str]					=						jnp.asarray(snake_case__						)

        else:
            # also add unexpected weight so that warning is thrown
            A       :   Optional[Any]					=						jnp.asarray(snake_case__						)

    return unflatten_dict(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    # Load the index
    A       :   Union[str, Any]					=						{}
    for shard_file in shard_filenames:
        # load using msgpack utils
        A       :   List[str]					=						torch.load(snake_case__						)
        A       :   int					=						{k: v.numpy() for k, v in pt_state_dict.items()}

        A       :   Tuple					=						flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            A       :   Optional[int]					=						flax_model.params['''params''']

            A       :   List[Any]					=						flatten_dict(snake_case__						)
            random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']						)						)
        else:
            A       :   Dict					=						flax_model.params
            A       :   Tuple					=						flatten_dict(snake_case__						)

        A       :   List[str]					=						(model_prefix not in flax_model_params) and (
            model_prefix in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
        )
        A       :   List[str]					=						(model_prefix in flax_model_params) and (
            model_prefix not in {k.split('''.'''						)[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            A       :   int					=						tuple(pt_key.split('''.'''						)						)

            # remove base model prefix if necessary
            A       :   List[str]					=						pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                A       :   Union[str, Any]					=						pt_tuple_key[1:]

            # Correctly rename weight parameters
            A,  A       :   Any					=						rename_key_and_reshape_tensor(
                snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)
            # add model prefix if necessary
            A       :   int					=						(model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                A       :   int					=						(model_prefix,) + flax_key

            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}.'						)

            # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    A       :   Optional[int]					=						jnp.asarray(snake_case__						)
                    continue
                if "var" in flax_key[-1]:
                    A       :   Optional[int]					=						jnp.asarray(snake_case__						)
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(snake_case__		,				snake_case__						)
                    continue

                # also add unexpected weight so that warning is thrown
                A       :   Optional[Any]					=						jnp.asarray(snake_case__						)

            else:
                # also add unexpected weight so that warning is thrown
                A       :   Optional[Any]					=						jnp.asarray(snake_case__						)
    return unflatten_dict(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						os.path.abspath(snake_case__						)
    logger.info(F'Loading Flax weights from {flax_checkpoint_path}'						)

    # import correct flax class
    A       :   List[str]					=						getattr(snake_case__		,				'''Flax''' + model.__class__.__name__						)

    # load flax weight dict
    with open(snake_case__		,				'''rb'''						) as state_f:
        try:
            A       :   int					=						from_bytes(snake_case__		,				state_f.read()						)
        except UnpicklingError:
            raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '						)

    return load_flax_weights_in_pytorch_model(snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading a 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
    A       :   List[str]					=						flatten_dict(jax.tree_util.tree_map(lambda snake_case__						: x.dtype == jnp.bfloataa		,				snake_case__						)						).values()
    if any(snake_case__						):
        # convert all weights to fp32 if the 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.'''						)
        A       :   Optional[Any]					=						jax.tree_util.tree_map(
            lambda snake_case__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params		,				snake_case__						)

    A       :   Union[str, Any]					=						flatten_dict(snake_case__						)
    A       :   List[Any]					=						pt_model.state_dict()

    A       :   Union[str, Any]					=						(pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split('''.'''						)[0] for k in pt_model_dict.keys()}
    )
    A       :   Tuple					=						(pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split('''.'''						)[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    A       :   int					=						[]
    A       :   Any					=						set(pt_model_dict.keys()						)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        A       :   Union[str, Any]					=						flax_key_tuple[0] == pt_model.base_model_prefix
        A       :   int					=						'''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple						) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            A       :   List[str]					=						flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            A       :   Optional[Any]					=						(pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(snake_case__						) not in pt_model_dict:
            # conv layer
            A       :   Optional[int]					=						flax_key_tuple[:-1] + ('''weight''',)
            A       :   Optional[int]					=						jnp.transpose(snake_case__		,				(3, 2, 0, 1)						)
        elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__						) not in pt_model_dict:
            # linear layer
            A       :   Tuple					=						flax_key_tuple[:-1] + ('''weight''',)
            A       :   Tuple					=						flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            A       :   Optional[int]					=						flax_key_tuple[:-1] + ('''weight''',)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            A       :   Tuple					=						flax_key_tuple[:-1] + ('''running_mean''',)
        elif "var" in flax_key_tuple[-1]:
            A       :   Tuple					=						flax_key_tuple[:-1] + ('''running_var''',)

        if "batch_stats" in flax_state:
            A       :   List[Any]					=						'''.'''.join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
        else:
            A       :   Union[str, Any]					=						'''.'''.join(snake_case__						)

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        A       :   int					=						{}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            A       :   Optional[int]					=						key.split('''.'''						)
            A       :   Dict					=						None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                A       :   List[str]					=						key_components[-2] + '''_g'''
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                A       :   List[Any]					=						key_components[-2] + '''_v'''
            if name is not None:
                A       :   str					=						key_components[:-3] + [name]
                A       :   Optional[Any]					=						'''.'''.join(snake_case__						)
                A       :   Optional[Any]					=						key

        if flax_key in special_pt_names:
            A       :   Optional[Any]					=						special_pt_names[flax_key]

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

    pt_model.load_state_dict(snake_case__						)

    # re-transform missing_keys to list
    A       :   List[Any]					=						list(snake_case__						)

    if len(snake_case__						) > 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).'''						)
    else:
        logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'						)
    if len(snake_case__						) > 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.'''						)
    else:
        logger.warning(
            F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
            '''If your task is similar to the task the model of the checkpoint was trained on, '''
            F'you can already use {pt_model.__class__.__name__} for predictions without further training.'						)

    return pt_model




