



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
SCREAMING_SNAKE_CASE_: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      __UpperCamelCase     (  _lowerCAmelCase		)	->  Optional[Any]:



    """simple docstring"""






    A			:	Optional[int]										=       list(s_dict.keys()		)
    for key in keys:
        A			:	List[Any]										=       R""".*/layers_(\d+)"""
        A			:	str										=       key
        if re.match(_lowerCAmelCase				,					_lowerCAmelCase		):
            A			:	Any										=       re.sub(R"""layers_(\d+)"""				,					R"""block/\1/layer"""				,					_lowerCAmelCase		)

        A			:	Optional[Any]										=       R"""(encoder|decoder)\/"""

        if re.match(_lowerCAmelCase				,					_lowerCAmelCase		):
            A			:	Optional[int]										=       re.match(_lowerCAmelCase				,					_lowerCAmelCase		).groups()
            if groups[0] == "encoder":
                A			:	Optional[Any]										=       re.sub(R"""/mlp/"""				,					R"""/1/mlp/"""				,					_lowerCAmelCase		)
                A			:	List[str]										=       re.sub(R"""/pre_mlp_layer_norm/"""				,					R"""/1/layer_norm/"""				,					_lowerCAmelCase		)

            elif groups[0] == "decoder":
                A			:	Any										=       re.sub(R"""/mlp/"""				,					R"""/2/mlp/"""				,					_lowerCAmelCase		)
                A			:	Tuple										=       re.sub(R"""/pre_mlp_layer_norm/"""				,					R"""/2/layer_norm/"""				,					_lowerCAmelCase		)

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                A			:	Optional[int]										=       new_key.replace(_lowerCAmelCase				,					_lowerCAmelCase		)

        print(f'''{key} -> {new_key}'''		)
        A			:	Union[str, Any]										=       s_dict.pop(_lowerCAmelCase		)

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

            s_dict.pop(_lowerCAmelCase		)

    return s_dict


SCREAMING_SNAKE_CASE_:Optional[Any]					=						{
    """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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    import regex as re

    with open(_lowerCAmelCase				,					"""r"""		) as f:
        A			:	Optional[int]										=       f.read()

    A			:	Union[str, Any]										=       re.findall(R"""(.*) = ([0-9.]*)"""				,					_lowerCAmelCase		)
    A			:	int										=       {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            A			:	Union[str, Any]										=       float(_lowerCAmelCase		) if """.""" in value else int(_lowerCAmelCase		)

    A			:	List[str]										=       re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,					_lowerCAmelCase		)[0]
    A			:	Optional[int]										=       str(activation[1]		)

    A			:	Dict										=       num_experts
    A			:	int										=       SwitchTransformersConfig(**_lowerCAmelCase		)
    return config




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase=None				,					_lowerCAmelCase="./"				,					_lowerCAmelCase=8		)	->  Optional[int]:



    """simple docstring"""






    print(f'''Loading flax weights from : {flax_checkpoint_path}'''		)
    A			:	str										=       checkpoints.load_tax_checkpoint(_lowerCAmelCase		)

    if gin_file is not None:
        A			:	Union[str, Any]										=       convert_gin_to_config(_lowerCAmelCase				,					_lowerCAmelCase		)
    else:
        A			:	Tuple										=       SwitchTransformersConfig.from_pretrained(_lowerCAmelCase		)

    A			:	Optional[int]										=       SwitchTransformersForConditionalGeneration(_lowerCAmelCase		)

    A			:	str										=       flax_params["""target"""]
    A			:	Dict										=       flatten_dict(_lowerCAmelCase				,					sep="""/"""		)
    A			:	Dict										=       rename_keys(_lowerCAmelCase		)
    A			:	str										=       unflatten_dict(_lowerCAmelCase				,					sep="""/"""		)

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

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


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:int					=						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""")
     SCREAMING_SNAKE_CASE_:Any					=						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,
     )







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor






@require_vision
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	str										=       tempfile.mkdtemp()

        A			:	Any										=       [
            """[UNK]""",
            """[CLS]""",
            """[SEP]""",
            """[PAD]""",
            """[MASK]""",
            """的""",
            """价""",
            """格""",
            """是""",
            """15""",
            """便""",
            """alex""",
            """##andra""",
            """，""",
            """。""",
            """-""",
            """t""",
            """shirt""",
        ]
        A			:	Any										=       os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES["""vocab_file"""]		)
        with open(self.vocab_file,		"""w""",		encoding="""utf-8"""		) as vocab_writer:
            vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)

        A			:	Any										=       {
            """do_resize""": True,
            """size""": {"""height""": 224, """width""": 224},
            """do_center_crop""": True,
            """crop_size""": {"""height""": 18, """width""": 18},
            """do_normalize""": True,
            """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
            """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
            """do_convert_rgb""": True,
        }
        A			:	int										=       os.path.join(self.tmpdirname,		lowerCamelCase__		)
        with open(self.image_processor_file,		"""w""",		encoding="""utf-8"""		) as fp:
            json.dump(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return BertTokenizer.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return BertTokenizerFast.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        shutil.rmtree(self.tmpdirname		)






    def       _lowerCAmelCase (	self		):
        A			:	Optional[int]										=       [np.random.randint(255,		size=(3, 30, 400),		dtype=np.uinta		)]

        A			:	List[Any]										=       [Image.fromarray(np.moveaxis(lowerCamelCase__,		0,		-1		)		) for x in image_inputs]

        return image_inputs






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.get_tokenizer()
        A			:	Dict										=       self.get_rust_tokenizer()
        A			:	List[Any]										=       self.get_image_processor()

        A			:	int										=       ChineseCLIPProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_slow.save_pretrained(self.tmpdirname		)
        A			:	Optional[Any]										=       ChineseCLIPProcessor.from_pretrained(self.tmpdirname,		use_fast=lowerCamelCase__		)

        A			:	str										=       ChineseCLIPProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_fast.save_pretrained(self.tmpdirname		)
        A			:	Tuple										=       ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

        self.assertEqual(processor_slow.tokenizer.get_vocab(),		tokenizer_slow.get_vocab()		)
        self.assertEqual(processor_fast.tokenizer.get_vocab(),		tokenizer_fast.get_vocab()		)
        self.assertEqual(tokenizer_slow.get_vocab(),		tokenizer_fast.get_vocab()		)
        self.assertIsInstance(processor_slow.tokenizer,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.tokenizer,		lowerCamelCase__		)

        self.assertEqual(processor_slow.image_processor.to_json_string(),		image_processor.to_json_string()		)
        self.assertEqual(processor_fast.image_processor.to_json_string(),		image_processor.to_json_string()		)
        self.assertIsInstance(processor_slow.image_processor,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.image_processor,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	str										=       ChineseCLIPProcessor(tokenizer=self.get_tokenizer(),		image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

        A			:	Optional[int]										=       self.get_tokenizer(cls_token="""(CLS)""",		sep_token="""(SEP)"""		)
        A			:	Optional[int]										=       self.get_image_processor(do_normalize=lowerCamelCase__		)

        A			:	Optional[int]										=       ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname,		cls_token="""(CLS)""",		sep_token="""(SEP)""",		do_normalize=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	List[str]										=       self.get_tokenizer()

        A			:	List[str]										=       ChineseCLIPProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       self.prepare_image_inputs()

        A			:	Union[str, Any]										=       image_processor(lowerCamelCase__,		return_tensors="""np"""		)
        A			:	str										=       processor(images=lowerCamelCase__,		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       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.get_image_processor()
        A			:	Tuple										=       self.get_tokenizer()

        A			:	str										=       ChineseCLIPProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       """Alexandra，T-shirt的价格是15便士。"""

        A			:	Optional[Any]										=       processor(text=lowerCamelCase__		)

        A			:	int										=       tokenizer(lowerCamelCase__		)

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






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       self.get_image_processor()
        A			:	List[str]										=       self.get_tokenizer()

        A			:	int										=       ChineseCLIPProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	str										=       """Alexandra，T-shirt的价格是15便士。"""
        A			:	Dict										=       self.prepare_image_inputs()

        A			:	Optional[int]										=       processor(text=lowerCamelCase__,		images=lowerCamelCase__		)

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

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






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       self.get_image_processor()
        A			:	List[str]										=       self.get_tokenizer()

        A			:	List[str]										=       ChineseCLIPProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	int										=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A			:	List[Any]										=       processor.batch_decode(lowerCamelCase__		)
        A			:	str										=       tokenizer.batch_decode(lowerCamelCase__		)

        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       self.get_image_processor()
        A			:	List[str]										=       self.get_tokenizer()

        A			:	Any										=       ChineseCLIPProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	List[Any]										=       """Alexandra，T-shirt的价格是15便士。"""
        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	str										=       processor(text=lowerCamelCase__,		images=lowerCamelCase__		)

        self.assertListEqual(list(inputs.keys()		),		processor.model_input_names		)


