



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  				=    logging.get_logger(__name__)






def   _A					(UpperCamelCase		:   List[Any]   ,      UpperCamelCase		:   Dict   ,      UpperCamelCase		:   Optional[int]   ,      UpperCamelCase		:   Any=False			)	->Optional[int]:



			'''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:
						lowerCamelCase__			:   Union[str, Any]										=		os.path.abspath(lowerCAmelCase_			)
						logger.info(f"Loading PyTorch weights from {pt_path}"			)

						lowerCamelCase__			:   Optional[int]										=		torch.load(lowerCAmelCase_   ,      map_location="""cpu"""			)
						logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()			):,} parameters."			)

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






def   _A					(UpperCamelCase		:   Dict   ,      UpperCamelCase		:   Dict   ,      UpperCamelCase		:   Union[str, Any]   ,      UpperCamelCase		:   Optional[int]   ,      )	->(Tuple[str], np.ndarray):



			'''simple docstring'''







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

			# layer norm
			lowerCamelCase__			:   Dict										=		pt_tuple_key[:-1] + ("""scale""",)
			if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCAmelCase_			):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer mean
			lowerCamelCase__			:   List[str]										=		pt_tuple_key[:-1] + ("""mean""",)
			if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCAmelCase_			):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer var
			lowerCamelCase__			:   List[str]										=		pt_tuple_key[:-1] + ("""var""",)
			if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCAmelCase_			):
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			lowerCamelCase__			:   int										=		pt_tuple_key[:-1] + ("""embedding""",)
			if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCAmelCase_			):
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			lowerCamelCase__			:   str										=		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(lowerCAmelCase_			):
						lowerCamelCase__			:   str										=		pt_tensor.transpose(2   ,      3   ,      1   ,      0			)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			lowerCamelCase__			:   Optional[Any]										=		pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCAmelCase_			):
						lowerCamelCase__			:   List[Any]										=		pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

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

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

			return pt_tuple_key, pt_tensor






def   _A					(UpperCamelCase		:   Union[str, Any]   ,      UpperCamelCase		:   int			)	->Tuple:



			'''simple docstring'''







			lowerCamelCase__			:   List[str]										=		{k: v.numpy() for k, v in pt_state_dict.items()}

			lowerCamelCase__			:   Any										=		flax_model.base_model_prefix

			# use params dict if the model contains batch norm layers
			if "params" in flax_model.params:
						lowerCamelCase__			:   str										=		flax_model.params["""params"""]
			else:
						lowerCamelCase__			:   Optional[int]										=		flax_model.params
			lowerCamelCase__			:   int										=		flatten_dict(lowerCAmelCase_			)

			# add batch_stats keys,values to dict
			if "batch_stats" in flax_model.params:
						lowerCamelCase__			:   Dict										=		flatten_dict(flax_model.params["""batch_stats"""]			)
						random_flax_state_dict.update(lowerCAmelCase_			)

			lowerCamelCase__			:   Dict										=		{}

			lowerCamelCase__			:   Union[str, Any]										=		(model_prefix not in flax_model_params) and (
			    model_prefix in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
			)
			lowerCamelCase__			:   Tuple										=		(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():
						lowerCamelCase__			:   int										=		tuple(pt_key.split("""."""			)			)

						# remove base model prefix if necessary
						lowerCamelCase__			:   Optional[int]										=		pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
									lowerCamelCase__			:   str										=		pt_tuple_key[1:]

						# Correctly rename weight parameters
						lowerCamelCase__				,lowerCamelCase__			:   Optional[Any]										=		rename_key_and_reshape_tensor(
						    lowerCAmelCase_   ,      lowerCAmelCase_   ,      lowerCAmelCase_   ,      lowerCAmelCase_			)

						# add model prefix if necessary
						lowerCamelCase__			:   int										=		(model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									lowerCamelCase__			:   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] or "var" in flax_key[-1]:
												lowerCamelCase__			:   Any										=		jnp.asarray(lowerCAmelCase_			)
												continue
									# remove num_batches_tracked key
									if "num_batches_tracked" in flax_key[-1]:
												flax_state_dict.pop(lowerCAmelCase_   ,      lowerCAmelCase_			)
												continue

									# also add unexpected weight so that warning is thrown
									lowerCamelCase__			:   List[Any]										=		jnp.asarray(lowerCAmelCase_			)

						else:
									# also add unexpected weight so that warning is thrown
									lowerCamelCase__			:   int										=		jnp.asarray(lowerCAmelCase_			)

			return unflatten_dict(lowerCAmelCase_			)






def   _A					(UpperCamelCase		:   List[Any]   ,      UpperCamelCase		:   Dict			)	->str:



			'''simple docstring'''







			import torch

			# Load the index
			lowerCamelCase__			:   int										=		{}
			for shard_file in shard_filenames:
						# load using msgpack utils
						lowerCamelCase__			:   Any										=		torch.load(lowerCAmelCase_			)
						lowerCamelCase__			:   Optional[int]										=		{k: v.numpy() for k, v in pt_state_dict.items()}

						lowerCamelCase__			:   Any										=		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:
									lowerCamelCase__			:   Dict										=		flax_model.params["""params"""]

									lowerCamelCase__			:   str										=		flatten_dict(lowerCAmelCase_			)
									random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]			)			)
						else:
									lowerCamelCase__			:   Any										=		flax_model.params
									lowerCamelCase__			:   Union[str, Any]										=		flatten_dict(lowerCAmelCase_			)

						lowerCamelCase__			:   Tuple										=		(model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("""."""			)[0] for k in pt_state_dict.keys()}
						)
						lowerCamelCase__			:   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():
									lowerCamelCase__			:   Any										=		tuple(pt_key.split("""."""			)			)

									# remove base model prefix if necessary
									lowerCamelCase__			:   Optional[int]										=		pt_tuple_key[0] == model_prefix
									if load_model_with_head_into_base_model and has_base_model_prefix:
												lowerCamelCase__			:   List[Any]										=		pt_tuple_key[1:]

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

												# also add unexpected weight so that warning is thrown
												lowerCamelCase__			:   int										=		jnp.asarray(lowerCAmelCase_			)

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






def   _A					(UpperCamelCase		:   Dict   ,      UpperCamelCase		:   Dict			)	->Any:



			'''simple docstring'''







			lowerCamelCase__			:   str										=		os.path.abspath(lowerCAmelCase_			)
			logger.info(f"Loading Flax weights from {flax_checkpoint_path}"			)

			# import correct flax class
			lowerCamelCase__			:   Union[str, Any]										=		getattr(lowerCAmelCase_   ,      """Flax""" + model.__class__.__name__			)

			# load flax weight dict
			with open(lowerCAmelCase_   ,      """rb"""			) as state_f:
						try:
									lowerCamelCase__			:   Dict										=		from_bytes(lowerCAmelCase_   ,      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(lowerCAmelCase_   ,      lowerCAmelCase_			)






def   _A					(UpperCamelCase		:   Union[str, Any]   ,      UpperCamelCase		:   List[str]			)	->str:



			'''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
			lowerCamelCase__			:   str										=		flatten_dict(jax.tree_util.tree_map(lambda UpperCamelCase			: x.dtype == jnp.bfloataa   ,      lowerCAmelCase_			)			).values()
			if any(lowerCAmelCase_			):
						# 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."""			)
						lowerCamelCase__			:   str										=		jax.tree_util.tree_map(
						    lambda UpperCamelCase			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params   ,      lowerCAmelCase_			)

			lowerCamelCase__			:   Any										=		flatten_dict(lowerCAmelCase_			)
			lowerCamelCase__			:   Tuple										=		pt_model.state_dict()

			lowerCamelCase__			:   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()}
			)
			lowerCamelCase__			:   int										=		(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
			lowerCamelCase__			:   Optional[Any]										=		[]
			lowerCamelCase__			:   Optional[Any]										=		set(pt_model_dict.keys()			)

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

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

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

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

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








"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	str           =  '''openai/whisper-base'''
   A__		:	List[Any]           =  (
       '''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
       '''transcribed text.'''
   )
   A__		:	str           =  '''transcriber'''
   A__		:	List[Any]           =  WhisperProcessor
   A__		:	Optional[int]           =  WhisperForConditionalGeneration

   A__		:	List[str]           =  ['''audio''']
   A__		:	Optional[int]           =  ['''text''']



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[Any]				):



     """simple docstring"""
     return self.pre_processor(__lowerCamelCase							,       return_tensors='''pt'''				).input_features



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return self.model.generate(inputs=__lowerCamelCase				)





   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.pre_processor.batch_decode(__lowerCamelCase							,       skip_special_tokens=__lowerCamelCase				)[0]





