
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

if is_torch_available():
						import torch


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


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_ = None						,				A_ = None						,				A_=None						,				A_=None     )->		Optional[Any]:







											'''simple docstring'''






											if not conversation_id:
																		UpperCamelCase   			=     uuid.uuida()
											if past_user_inputs is None:
																		UpperCamelCase   			=     []
											if generated_responses is None:
																		UpperCamelCase   			=     []

											UpperCamelCase   			=     conversation_id
											UpperCamelCase   			=     past_user_inputs
											UpperCamelCase   			=     generated_responses
											UpperCamelCase   			=     text







				def __eq__(       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ):
																		return False
											if self.uuid == other.uuid:
																		return True
											return (
											    self.new_user_input == other.new_user_input
											    and self.past_user_inputs == other.past_user_inputs
											    and self.generated_responses == other.generated_responses
											)







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False     )->		int:







											'''simple docstring'''






											if self.new_user_input:
																		if overwrite:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
																									    F'''with: "{text}".'''     )
																									UpperCamelCase   			=     text
																		else:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
																									    F'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''     )
											else:
																		UpperCamelCase   			=     text







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input     )
											UpperCamelCase   			=     None







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											self.generated_responses.append(A_     )







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											for user_input, generated_response in zip(self.past_user_inputs						,				self.generated_responses     ):
																		yield True, user_input
																		yield False, generated_response
											if self.new_user_input:
																		yield True, self.new_user_input







				def __repr__(       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		UpperCamelCase   			=     'user' if is_user else 'bot'
																		output += F'''{name} >> {text} \n'''
											return output


@add_end_docstrings(
    snake_case_							,       R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """							,       )
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				*A_						,				**A_     )->		Any:







											'''simple docstring'''






											super().__init__(*A_						,				**A_     )
											if self.tokenizer.pad_token_id is None:
																		UpperCamelCase   			=     self.tokenizer.eos_token







				def 						UpperCAmelCase_    (       self						,				A_=None						,				A_=None						,				A_=None						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}

											if min_length_for_response is not None:
																		UpperCamelCase   			=     min_length_for_response
											if minimum_tokens is not None:
																		UpperCamelCase   			=     minimum_tokens

											if "max_length" in generate_kwargs:
																		UpperCamelCase   			=     generate_kwargs['max_length']
																		# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
											if clean_up_tokenization_spaces is not None:
																		UpperCamelCase   			=     clean_up_tokenization_spaces

											if generate_kwargs:
																		forward_params.update(A_     )
											return preprocess_params, forward_params, postprocess_params







				def __call__(       self						,				A_						,				A_=0						,				**A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     super().__call__(A_						,				num_workers=A_						,				**A_     )
											if isinstance(A_						,				A_     ) and len(A_     ) == 1:
																		return outputs[0]
											return outputs







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=32     )->		Dict[str, Any]:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ):
																		raise ValueError('ConversationalPipeline, expects Conversation as inputs'     )
											if conversation.new_user_input is None:
																		raise ValueError(
																		    F'''Conversation with UUID {type(conversation.uuid     )} does not contain new user input to process. '''
																		    'Add user inputs with the conversation\'s `add_user_input` method'     )
											if hasattr(self.tokenizer						,				'_build_conversation_input_ids'     ):
																		UpperCamelCase   			=     self.tokenizer._build_conversation_input_ids(A_     )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		UpperCamelCase   			=     self._legacy_parse_and_tokenize(A_     )

											if self.framework == "pt":
																		UpperCamelCase   			=     torch.LongTensor([input_ids]     )
											elif self.framework == "tf":
																		UpperCamelCase   			=     tf.constant([input_ids]     )
											return {"input_ids": input_ids, "conversation": conversation}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=10						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     generate_kwargs.get('max_length'						,				self.model.config.max_length     )

											UpperCamelCase   			=     model_inputs['input_ids'].shape[1]
											if max_length - minimum_tokens < n:
																		logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''     )
																		UpperCamelCase   			=     max_length - minimum_tokens
																		UpperCamelCase   			=     model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									UpperCamelCase   			=     model_inputs['attention_mask'][:, -trim:]
											UpperCamelCase   			=     model_inputs.pop('conversation'     )
											UpperCamelCase   			=     max_length
											UpperCamelCase   			=     self.model.generate(**A_						,				**A_     )
											if self.model.config.is_encoder_decoder:
																		UpperCamelCase   			=     1
											else:
																		UpperCamelCase   			=     n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=True     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     model_outputs['output_ids']
											UpperCamelCase   			=     self.tokenizer.decode(
											    output_ids[0]						,				skip_special_tokens=A_						,				clean_up_tokenization_spaces=A_						,				)
											UpperCamelCase   			=     model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(A_     )
											return conversation







				def 						UpperCAmelCase_    (       self						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer.eos_token_id
											UpperCamelCase   			=     []
											for is_user, text in conversation.iter_texts():
																		if eos_token_id is not None:
																									input_ids.extend(self.tokenizer.encode(A_						,				add_special_tokens=A_     ) + [eos_token_id]     )
																		else:
																									input_ids.extend(self.tokenizer.encode(A_						,				add_special_tokens=A_     )     )

											if len(A_     ) > self.tokenizer.model_max_length:
																		UpperCamelCase   			=     input_ids[-self.tokenizer.model_max_length :]
											return input_ids






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

        a__		=			r'(encoder|decoder)\/'

        if re.match(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			re.match(UpperCamelCase							,		UpperCamelCase     ).groups()
            if groups[0] == "encoder":
                a__		=			re.sub(r'/mlp/'							,		r'/1/mlp/'							,		UpperCamelCase     )
                a__		=			re.sub(r'/pre_mlp_layer_norm/'							,		r'/1/layer_norm/'							,		UpperCamelCase     )

            elif groups[0] == "decoder":
                a__		=			re.sub(r'/mlp/'							,		r'/2/mlp/'							,		UpperCamelCase     )
                a__		=			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:
                a__		=			new_key.replace(UpperCamelCase							,		UpperCamelCase     )

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

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        a__		=			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:
        a__		=			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:
            a__		=			s_dict[key].shape[0]
            a__		=			s_dict[key]
            for idx in range(UpperCamelCase     ):
                a__		=			expert_weihts[idx]
                print(f'{key} -> {key.replace("expert/"							,		"nested fstring"     )}'     )

            s_dict.pop(UpperCamelCase     )

    return s_dict


a		:       List[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     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Optional[int]     )     ->						Optional[int]:
    # Convert a google style config to the hugging face fromat
    import regex as re

    with open(UpperCamelCase							,		'r'     ) as f:
        a__		=			f.read()

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

    a__		=			re.findall(r'(.*activations) = \(\'(.*)\',\)'							,		UpperCamelCase     )[0]
    a__		=			str(activation[1]     )

    a__		=			num_experts
    a__		=			SwitchTransformersConfig(**UpperCamelCase     )
    return config



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Union[str, Any]=None							,		UpperCamelCase     :		List[str]="./"							,		UpperCamelCase     :		Any=8     )     ->						Union[str, Any]:
    # Initialise PyTorch model

    print(f'Loading flax weights from : {flax_checkpoint_path}'     )
    a__		=			checkpoints.load_tax_checkpoint(UpperCamelCase     )

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

    a__		=			SwitchTransformersForConditionalGeneration(UpperCamelCase     )

    a__		=			flax_params['target']
    a__		=			flatten_dict(UpperCamelCase							,		sep='/'     )
    a__		=			rename_keys(UpperCamelCase     )
    a__		=			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		:       Dict     						=       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		:       List[str]     						=       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,
    )


