






'''simple docstring'''







import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
_A     :      List[str]   					={
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}


def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[Any]:
							# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
							# the original model
							lowerCamelCase__ :     int	  =	list(s_dict.keys()				)
							for key in keys:
														lowerCamelCase__ :     str	  =	r""".*/layers_(\d+)"""
														lowerCamelCase__ :     int	  =	key
														if re.match(UpperCamelCase    ,      UpperCamelCase				):
																					lowerCamelCase__ :     List[str]	  =	re.sub(r"""layers_(\d+)"""    ,      r"""block/\1/layer"""    ,      UpperCamelCase				)

														lowerCamelCase__ :     Tuple	  =	r"""(encoder|decoder)\/"""

														if re.match(UpperCamelCase    ,      UpperCamelCase				):
																					lowerCamelCase__ :     List[Any]	  =	re.match(UpperCamelCase    ,      UpperCamelCase				).groups()
																					if groups[0] == "encoder":
																												lowerCamelCase__ :     Optional[Any]	  =	re.sub(r"""/mlp/"""    ,      r"""/1/mlp/"""    ,      UpperCamelCase				)
																												lowerCamelCase__ :     int	  =	re.sub(r"""/pre_mlp_layer_norm/"""    ,      r"""/1/layer_norm/"""    ,      UpperCamelCase				)

																					elif groups[0] == "decoder":
																												lowerCamelCase__ :     List[str]	  =	re.sub(r"""/mlp/"""    ,      r"""/2/mlp/"""    ,      UpperCamelCase				)
																												lowerCamelCase__ :     Optional[Any]	  =	re.sub(r"""/pre_mlp_layer_norm/"""    ,      r"""/2/layer_norm/"""    ,      UpperCamelCase				)

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												lowerCamelCase__ :     Union[str, Any]	  =	new_key.replace(UpperCamelCase    ,      UpperCamelCase				)

														print(f'''{key} -> {new_key}'''				)
														lowerCamelCase__ :     str	  =	s_dict.pop(UpperCamelCase				)

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														lowerCamelCase__ :     List[Any]	  =	s_dict[
														    """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T
							if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														lowerCamelCase__ :     str	  =	s_dict[
														    """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T

							# 3. Take extra care of the EXPERTS layer
							for key in list(s_dict.keys()				):
														if "expert" in key:
																					lowerCamelCase__ :     Union[str, Any]	  =	s_dict[key].shape[0]
																					lowerCamelCase__ :     Tuple	  =	s_dict[key]
																					for idx in range(UpperCamelCase				):
																												lowerCamelCase__ :     Union[str, Any]	  =	expert_weihts[idx]
																												print(f'''{key} -> {key.replace('expert/'    ,      'nested fstring'				)}'''				)

																					s_dict.pop(UpperCamelCase				)

							return s_dict


_A     :      str   					={
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}


def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	Dict:
							# Convert a google style config to the hugging face fromat
							import regex as re

							with open(UpperCamelCase    ,      """r"""				) as f:
														lowerCamelCase__ :     Dict	  =	f.read()

							lowerCamelCase__ :     int	  =	re.findall(r"""(.*) = ([0-9.]*)"""    ,      UpperCamelCase				)
							lowerCamelCase__ :     int	  =	{}
							for param, value in regex_match:
														if param in GIN_TO_CONFIG_MAPPING and value != "":
																					lowerCamelCase__ :     Optional[Any]	  =	float(UpperCamelCase				) if """.""" in value else int(UpperCamelCase				)

							lowerCamelCase__ :     Optional[int]	  =	re.findall(r"""(.*activations) = \(\'(.*)\',\)"""    ,      UpperCamelCase				)[0]
							lowerCamelCase__ :     Optional[Any]	  =	str(activation[1]				)

							lowerCamelCase__ :     str	  =	num_experts
							lowerCamelCase__ :     str	  =	SwitchTransformersConfig(**UpperCamelCase				)
							return config


def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None    ,      UpperCamelCase="./"    ,      UpperCamelCase=8				)				->	Tuple:
							# Initialise PyTorch model

							print(f'''Loading flax weights from : {flax_checkpoint_path}'''				)
							lowerCamelCase__ :     Union[str, Any]	  =	checkpoints.load_tax_checkpoint(UpperCamelCase				)

							if gin_file is not None:
														lowerCamelCase__ :     Dict	  =	convert_gin_to_config(UpperCamelCase    ,      UpperCamelCase				)
							else:
														lowerCamelCase__ :     Dict	  =	SwitchTransformersConfig.from_pretrained(UpperCamelCase				)

							lowerCamelCase__ :     List[Any]	  =	SwitchTransformersForConditionalGeneration(UpperCamelCase				)

							lowerCamelCase__ :     List[str]	  =	flax_params["""target"""]
							lowerCamelCase__ :     Union[str, Any]	  =	flatten_dict(UpperCamelCase    ,      sep="""/"""				)
							lowerCamelCase__ :     str	  =	rename_keys(UpperCamelCase				)
							lowerCamelCase__ :     Optional[Any]	  =	unflatten_dict(UpperCamelCase    ,      sep="""/"""				)

							# Load the flax params in the PT model
							load_flax_weights_in_pytorch_model(UpperCamelCase    ,      UpperCamelCase				)

							print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
							pt_model.save_pretrained(UpperCamelCase				)


if __name__ == "__main__":
			_A     :      List[str]   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--switch_t5x_checkpoint_path''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
			        ''' model architecture. If not provided, a `gin_file` has to be provided.'''
			    ),
			)
			parser.add_argument(
			    '''--gin_file''',
			    default=None,
			    type=str,
			    required=False,
			    help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
			)
			parser.add_argument(
			    '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
			)
			parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
			_A     :      int   					=parser.parse_args()
			convert_flax_checkpoint_to_pytorch(
			    args.switch_tax_checkpoint_path,
			    args.config_name,
			    args.gin_file,
			    args.pytorch_dump_folder_path,
			    args.num_experts,
			)









'''simple docstring'''







from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


_A     :      Optional[Any]   					=logging.get_logger(__name__)

@add_end_docstrings(_lowercase		)
class _lowercase     (    _lowercase		):




							def __init__(							self:    Union[str, Any]      ,							**UpperCamelCase__:    str					):
														super().__init__(**UpperCamelCase__					)

														if self.framework != "pt":
																					raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''					)
														# No specific FOR_XXX available yet




							def __call__(							self:    Optional[Any]      ,							UpperCamelCase__:    Union[np.ndarray, bytes, str]      ,							**UpperCamelCase__:    int					):
														return super().__call__(UpperCamelCase__      ,							**UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							**UpperCamelCase__:    int					):
														lowerCamelCase__ :     Optional[Any]	  =	{}
														if "candidate_labels" in kwargs:
																					lowerCamelCase__ :     str	  =	kwargs["""candidate_labels"""]
														if "hypothesis_template" in kwargs:
																					lowerCamelCase__ :     int	  =	kwargs["""hypothesis_template"""]

														return preprocess_params, {}, {}




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    List[str]=None      ,							UpperCamelCase__:    Dict="This is a sound of {}."					):
														if isinstance(UpperCamelCase__      ,							UpperCamelCase__					):
																					if audio.startswith("""http://"""					) or audio.startswith("""https://"""					):
																												# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																												# like http_huggingface_co.png
																												lowerCamelCase__ :     int	  =	requests.get(UpperCamelCase__					).content
																					else:
																												with open(UpperCamelCase__      ,							"""rb"""					) as f:
																																			lowerCamelCase__ :     Dict	  =	f.read()

														if isinstance(UpperCamelCase__      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     str	  =	ffmpeg_read(UpperCamelCase__      ,							self.feature_extractor.sampling_rate					)

														if not isinstance(UpperCamelCase__      ,							np.ndarray					):
																					raise ValueError("""We expect a numpy ndarray as input"""					)
														if len(audio.shape					) != 1:
																					raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline"""					)

														lowerCamelCase__ :     Optional[Any]	  =	self.feature_extractor(
														    [audio]      ,							sampling_rate=self.feature_extractor.sampling_rate      ,							return_tensors="""pt"""					)
														lowerCamelCase__ :     Any	  =	candidate_labels
														lowerCamelCase__ :     Any	  =	[hypothesis_template.format(UpperCamelCase__					) for x in candidate_labels]
														lowerCamelCase__ :     int	  =	self.tokenizer(UpperCamelCase__      ,							return_tensors=self.framework      ,							padding=UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	[text_inputs]
														return inputs




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Any	  =	model_inputs.pop("""candidate_labels"""					)
														lowerCamelCase__ :     Union[str, Any]	  =	model_inputs.pop("""text_inputs"""					)
														if isinstance(text_inputs[0]      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     Optional[int]	  =	text_inputs[0]
														else:
																					# Batching case.
																					lowerCamelCase__ :     Tuple	  =	text_inputs[0][0]

														lowerCamelCase__ :     Tuple	  =	self.model(**UpperCamelCase__      ,							**UpperCamelCase__					)

														lowerCamelCase__ :     str	  =	{
														    """candidate_labels""": candidate_labels,
														    """logits""": outputs.logits_per_audio,
														}
														return model_outputs




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     List[str]	  =	model_outputs.pop("""candidate_labels"""					)
														lowerCamelCase__ :     int	  =	model_outputs["""logits"""][0]

														if self.framework == "pt":
																					lowerCamelCase__ :     Optional[int]	  =	logits.softmax(dim=0					)
																					lowerCamelCase__ :     Any	  =	probs.tolist()
														else:
																					raise ValueError("""`tf` framework not supported."""					)

														lowerCamelCase__ :     Tuple	  =	[
														    {"""score""": score, """label""": candidate_label}
														    for score, candidate_label in sorted(zip(UpperCamelCase__      ,							UpperCamelCase__					)      ,							key=lambda UpperCamelCase__					: -x[0]					)
														]
														return result

