
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


lowerCamelCase      =logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__=False		):
      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:
            UpperCamelCase__    :   str					=				os.path.abspath(UpperCamelCase__		)
            logger.info(f'''Loading PyTorch weights from {pt_path}'''		)

            UpperCamelCase__    :   Union[str, Any]					=				torch.load(UpperCamelCase__ ,		map_location='''cpu'''		)
            logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()		):,} parameters.'''		)

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		):

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

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

      # batch norm layer mean
      UpperCamelCase__    :   Union[str, Any]					=				pt_tuple_key[:-1] + ('''mean''',)
      if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(UpperCamelCase__		):
            return renamed_pt_tuple_key, pt_tensor

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

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

      # conv layer
      UpperCamelCase__    :   Dict					=				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(UpperCamelCase__		):
            UpperCamelCase__    :   Optional[int]					=				pt_tensor.transpose(2 ,		3 ,		1 ,		0		)
            return renamed_pt_tuple_key, pt_tensor

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

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

      # old PyTorch layer norm bias
      UpperCamelCase__    :   Dict					=				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
      UpperCamelCase__    :   List[str]					=				None
      if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
            UpperCamelCase__    :   Optional[int]					=				pt_tuple_key[-2] + '''_g'''
      elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
            UpperCamelCase__    :   str					=				pt_tuple_key[-2] + '''_v'''
      if name is not None:
            UpperCamelCase__    :   Dict					=				pt_tuple_key[:-3] + (name,)
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      # convert pytorch tensor to numpy
      UpperCamelCase__    :   Dict					=				{k: v.numpy() for k, v in pt_state_dict.items()}

      UpperCamelCase__    :   int					=				flax_model.base_model_prefix

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

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

      UpperCamelCase__    :   Union[str, Any]					=				{}

      UpperCamelCase__    :   str					=				(model_prefix not in flax_model_params) and (
          model_prefix in {k.split('''.'''		)[0] for k in pt_state_dict.keys()}
      )
      UpperCamelCase__    :   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():
            UpperCamelCase__    :   str					=				tuple(pt_key.split('''.'''		)		)

            # remove base model prefix if necessary
            UpperCamelCase__    :   int					=				pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                  UpperCamelCase__    :   Dict					=				pt_tuple_key[1:]

            # Correctly rename weight parameters
            UpperCamelCase__				,UpperCamelCase__    :   int					=				rename_key_and_reshape_tensor(
                UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

            # add model prefix if necessary
            UpperCamelCase__    :   List[str]					=				(model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCamelCase__    :   Dict					=				(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]:
                        UpperCamelCase__    :   str					=				jnp.asarray(UpperCamelCase__		)
                        continue
                  # remove num_batches_tracked key
                  if "num_batches_tracked" in flax_key[-1]:
                        flax_state_dict.pop(UpperCamelCase__ ,		UpperCamelCase__		)
                        continue

                  # also add unexpected weight so that warning is thrown
                  UpperCamelCase__    :   Any					=				jnp.asarray(UpperCamelCase__		)

            else:
                  # also add unexpected weight so that warning is thrown
                  UpperCamelCase__    :   Dict					=				jnp.asarray(UpperCamelCase__		)

      return unflatten_dict(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      import torch

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

            UpperCamelCase__    :   str					=				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:
                  UpperCamelCase__    :   Optional[int]					=				flax_model.params['''params''']

                  UpperCamelCase__    :   str					=				flatten_dict(UpperCamelCase__		)
                  random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']		)		)
            else:
                  UpperCamelCase__    :   Optional[int]					=				flax_model.params
                  UpperCamelCase__    :   str					=				flatten_dict(UpperCamelCase__		)

            UpperCamelCase__    :   Dict					=				(model_prefix not in flax_model_params) and (
                model_prefix in {k.split('''.'''		)[0] for k in pt_state_dict.keys()}
            )
            UpperCamelCase__    :   Optional[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():
                  UpperCamelCase__    :   Union[str, Any]					=				tuple(pt_key.split('''.'''		)		)

                  # remove base model prefix if necessary
                  UpperCamelCase__    :   Any					=				pt_tuple_key[0] == model_prefix
                  if load_model_with_head_into_base_model and has_base_model_prefix:
                        UpperCamelCase__    :   Any					=				pt_tuple_key[1:]

                  # Correctly rename weight parameters
                  UpperCamelCase__				,UpperCamelCase__    :   str					=				rename_key_and_reshape_tensor(
                      UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
                  # add model prefix if necessary
                  UpperCamelCase__    :   Optional[Any]					=				(model_prefix,) + flax_key in random_flax_state_dict
                  if load_base_model_into_model_with_head and require_base_model_prefix:
                        UpperCamelCase__    :   Optional[Any]					=				(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]:
                              UpperCamelCase__    :   Optional[int]					=				jnp.asarray(UpperCamelCase__		)
                              continue
                        if "var" in flax_key[-1]:
                              UpperCamelCase__    :   Dict					=				jnp.asarray(UpperCamelCase__		)
                              continue
                        # remove num_batches_tracked key
                        if "num_batches_tracked" in flax_key[-1]:
                              flax_state_dict.pop(UpperCamelCase__ ,		UpperCamelCase__		)
                              continue

                        # also add unexpected weight so that warning is thrown
                        UpperCamelCase__    :   Tuple					=				jnp.asarray(UpperCamelCase__		)

                  else:
                        # also add unexpected weight so that warning is thrown
                        UpperCamelCase__    :   Dict					=				jnp.asarray(UpperCamelCase__		)
      return unflatten_dict(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				os.path.abspath(UpperCamelCase__		)
      logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''		)

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

      # load flax weight dict
      with open(UpperCamelCase__ ,		'''rb'''		) as state_f:
            try:
                  UpperCamelCase__    :   Tuple					=				from_bytes(UpperCamelCase__ ,		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(UpperCamelCase__ ,		UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):

      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
      UpperCamelCase__    :   List[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 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.'''		)
            UpperCamelCase__    :   Tuple					=				jax.tree_util.tree_map(
                lambda UpperCamelCase__		: params.astype(np.floataa		) if params.dtype == jnp.bfloataa else params ,		UpperCamelCase__		)

      UpperCamelCase__    :   Optional[Any]					=				flatten_dict(UpperCamelCase__		)
      UpperCamelCase__    :   Tuple					=				pt_model.state_dict()

      UpperCamelCase__    :   Dict					=				(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()}
      )
      UpperCamelCase__    :   Optional[Any]					=				(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
      UpperCamelCase__    :   Dict					=				[]
      UpperCamelCase__    :   Union[str, Any]					=				set(pt_model_dict.keys()		)

      for flax_key_tuple, flax_tensor in flax_state_dict.items():
            UpperCamelCase__    :   Any					=				flax_key_tuple[0] == pt_model.base_model_prefix
            UpperCamelCase__    :   Optional[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:
                  UpperCamelCase__    :   Any					=				flax_key_tuple[1:]
            elif load_base_model_into_model_with_head and require_base_model_prefix:
                  UpperCamelCase__    :   List[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(UpperCamelCase__		) not in pt_model_dict:
                  # conv layer
                  UpperCamelCase__    :   Union[str, Any]					=				flax_key_tuple[:-1] + ('''weight''',)
                  UpperCamelCase__    :   Optional[int]					=				jnp.transpose(UpperCamelCase__ ,		(3, 2, 0, 1)		)
            elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__		) not in pt_model_dict:
                  # linear layer
                  UpperCamelCase__    :   int					=				flax_key_tuple[:-1] + ('''weight''',)
                  UpperCamelCase__    :   Optional[Any]					=				flax_tensor.T
            elif flax_key_tuple[-1] in ["scale", "embedding"]:
                  UpperCamelCase__    :   int					=				flax_key_tuple[:-1] + ('''weight''',)

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

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

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

            if flax_key in special_pt_names:
                  UpperCamelCase__    :   Optional[int]					=				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
                        UpperCamelCase__    :   List[Any]					=				np.asarray(UpperCamelCase__		) if not isinstance(UpperCamelCase__ ,		np.ndarray		) else flax_tensor
                        UpperCamelCase__    :   Union[str, Any]					=				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
      UpperCamelCase__    :   int					=				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).'''		)
      else:
            logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''		)
      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.'''		)
      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




lowerCamelCase      ={
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}


