





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class  _lowerCamelCase       (			unittest.TestCase			):



      """simple docstring"""






      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        __snake_case      : int						=				tempfile.mkdtemp()

        __snake_case      : Dict						=				BlipImageProcessor()
        __snake_case      : Optional[Any]						=				BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"      )

        __snake_case      : Union[str, Any]						=				BlipProcessor(_A ,  _A      )

        processor.save_pretrained(self.tmpdirname      )







      def   UpperCAmelCase						(       self ,  **UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






        return AutoProcessor.from_pretrained(self.tmpdirname ,  **_A      ).tokenizer







      def   UpperCAmelCase						(       self ,  **UpperCAmelCase      )				->      Optional[Any]:






        '''simple docstring'''






        return AutoProcessor.from_pretrained(self.tmpdirname ,  **_A      ).image_processor







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        shutil.rmtree(self.tmpdirname      )







      def   UpperCAmelCase						(       self      )				->      int:






        '''simple docstring'''






        __snake_case      : int						=				[np.random.randint(255 ,  size=(3, 30, 400) ,  dtype=np.uinta      )]

        __snake_case      : int						=				[Image.fromarray(np.moveaxis(_A ,  0 ,  -1      )      ) for x in image_inputs]

        return image_inputs







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






        '''simple docstring'''






        __snake_case      : str						=				BlipProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

        __snake_case      : Tuple						=				self.get_tokenizer(bos_token="(BOS)" ,  eos_token="(EOS)"      )
        __snake_case      : Optional[int]						=				self.get_image_processor(do_normalize=_A ,  padding_value=1.0      )

        __snake_case      : Union[str, Any]						=				BlipProcessor.from_pretrained(
            self.tmpdirname ,  bos_token="(BOS)" ,  eos_token="(EOS)" ,  do_normalize=_A ,  padding_value=1.0      )

        self.assertEqual(processor.tokenizer.get_vocab() ,  tokenizer_add_kwargs.get_vocab()      )
        self.assertIsInstance(processor.tokenizer ,  _A      )

        self.assertEqual(processor.image_processor.to_json_string() ,  image_processor_add_kwargs.to_json_string()      )
        self.assertIsInstance(processor.image_processor ,  _A      )







      def   UpperCAmelCase						(       self      )				->      Dict:






        '''simple docstring'''






        __snake_case      : List[str]						=				self.get_image_processor()
        __snake_case      : int						=				self.get_tokenizer()

        __snake_case      : Tuple						=				BlipProcessor(tokenizer=_A ,  image_processor=_A      )

        __snake_case      : Optional[Any]						=				self.prepare_image_inputs()

        __snake_case      : int						=				image_processor(_A ,  return_tensors="np"      )
        __snake_case      : List[Any]						=				processor(images=_A ,  return_tensors="np"      )

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum() ,  input_processor[key].sum() ,  delta=1E-2      )







      def   UpperCAmelCase						(       self      )				->      Any:






        '''simple docstring'''






        __snake_case      : Optional[int]						=				self.get_image_processor()
        __snake_case      : Optional[Any]						=				self.get_tokenizer()

        __snake_case      : Any						=				BlipProcessor(tokenizer=_A ,  image_processor=_A      )

        __snake_case      : Optional[int]						=				"lower newer"

        __snake_case      : Tuple						=				processor(text=_A      )

        __snake_case      : Optional[Any]						=				tokenizer(_A ,  return_token_type_ids=_A      )

        for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key] ,  encoded_processor[key]      )







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






        '''simple docstring'''






        __snake_case      : Tuple						=				self.get_image_processor()
        __snake_case      : str						=				self.get_tokenizer()

        __snake_case      : Any						=				BlipProcessor(tokenizer=_A ,  image_processor=_A      )

        __snake_case      : Tuple						=				"lower newer"
        __snake_case      : Union[str, Any]						=				self.prepare_image_inputs()

        __snake_case      : Union[str, Any]						=				processor(text=_A ,  images=_A      )

        self.assertListEqual(list(inputs.keys()      ) ,  ["pixel_values", "input_ids", "attention_mask"]      )

        # test if it raises when no input is passed
        with pytest.raises(_A      ):
          processor()







      def   UpperCAmelCase						(       self      )				->      List[Any]:






        '''simple docstring'''






        __snake_case      : Tuple						=				self.get_image_processor()
        __snake_case      : Tuple						=				self.get_tokenizer()

        __snake_case      : str						=				BlipProcessor(tokenizer=_A ,  image_processor=_A      )

        __snake_case      : str						=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __snake_case      : Union[str, Any]						=				processor.batch_decode(_A      )
        __snake_case      : Any						=				tokenizer.batch_decode(_A      )

        self.assertListEqual(_A ,  _A      )







      def   UpperCAmelCase						(       self      )				->      Any:






        '''simple docstring'''






        __snake_case      : Union[str, Any]						=				self.get_image_processor()
        __snake_case      : Optional[int]						=				self.get_tokenizer()

        __snake_case      : Tuple						=				BlipProcessor(tokenizer=_A ,  image_processor=_A      )

        __snake_case      : str						=				"lower newer"
        __snake_case      : Any						=				self.prepare_image_inputs()

        __snake_case      : int						=				processor(text=_A ,  images=_A      )

        # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
        self.assertListEqual(list(inputs.keys()      ) ,  ["pixel_values", "input_ids", "attention_mask"]      )








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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				list(s_dict.keys() )
			for key in keys:
						UpperCAmelCase   		=				R'''.*/layers_(\d+)'''
						UpperCAmelCase   		=				key
						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.sub(R'''layers_(\d+)'''			,			R'''block/\1/layer'''			,			UpperCamelCase__ )

						UpperCAmelCase   		=				R'''(encoder|decoder)\/'''

						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.match(UpperCamelCase__			,			UpperCamelCase__ ).groups()
									if groups[0] == "encoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/1/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/1/layer_norm/'''			,			UpperCamelCase__ )

									elif groups[0] == "decoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/2/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				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:
												UpperCAmelCase   		=				new_key.replace(UpperCamelCase__			,			UpperCamelCase__ )

						print(F"""{key} -> {new_key}""" )
						UpperCAmelCase   		=				s_dict.pop(UpperCamelCase__ )

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

									s_dict.pop(UpperCamelCase__ )

			return s_dict


__A				:    Optional[int]       =   {
    "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__ )  ->				List[str]:







			'''simple docstring'''

			import regex as re

			with open(UpperCamelCase__			,			'''r''' ) as f:
						UpperCAmelCase   		=				f.read()

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

			UpperCAmelCase   		=				re.findall(R'''(.*activations) = \(\'(.*)\',\)'''			,			UpperCamelCase__ )[0]
			UpperCAmelCase   		=				str(activation[1] )

			UpperCAmelCase   		=				num_experts
			UpperCAmelCase   		=				SwitchTransformersConfig(**UpperCamelCase__ )
			return config





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__="./"			,			UpperCamelCase__=8 )  ->				List[Any]:







			'''simple docstring'''

			print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
			UpperCAmelCase   		=				checkpoints.load_tax_checkpoint(UpperCamelCase__ )

			if gin_file is not None:
						UpperCAmelCase   		=				convert_gin_to_config(UpperCamelCase__			,			UpperCamelCase__ )
			else:
						UpperCAmelCase   		=				SwitchTransformersConfig.from_pretrained(UpperCamelCase__ )

			UpperCAmelCase   		=				SwitchTransformersForConditionalGeneration(UpperCamelCase__ )

			UpperCAmelCase   		=				flax_params['''target''']
			UpperCAmelCase   		=				flatten_dict(UpperCamelCase__			,			sep='''/''' )
			UpperCAmelCase   		=				rename_keys(UpperCamelCase__ )
			UpperCAmelCase   		=				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				:    Tuple       =   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				:    Tuple       =   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,
				)



