import argparse
import os
import re


__lowerCamelCase					:	int       =  '''src/diffusers'''

# Pattern that looks at the indentation in a line.
__lowerCamelCase					:	Union[str, Any]       =  re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCamelCase					:	List[Any]       =  re.compile(R'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCamelCase					:	Optional[Any]       =  re.compile(R'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCamelCase					:	List[str]       =  re.compile(R'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCamelCase					:	List[str]       =  re.compile(R'''\[([^\]]+)\]''')




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    _re_indent.search(lowerCAmelCase	)
		return "" if search is None else search.groups()[0]




def       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   List[Any]=""   , lowerCAmelCase							:   Dict=None   , lowerCAmelCase							:   Any=None	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    0
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    code.split("\n"	)
		if start_prompt is not None:
				while not lines[index].startswith(lowerCAmelCase	):
						index += 1
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    ["\n".join(lines[:index]	)]
		else:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    []

		# We split into blocks until we get to the `end_prompt` (or the end of the block).
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [lines[index]]
		index += 1
		while index < len(lowerCAmelCase	) and (end_prompt is None or not lines[index].startswith(lowerCAmelCase	)):
				if len(lines[index]	) > 0 and get_indent(lines[index]	) == indent_level:
						if len(lowerCAmelCase	) > 0 and get_indent(current_block[-1]	).startswith(indent_level + " "	):
								current_block.append(lines[index]	)
								blocks.append("\n".join(lowerCAmelCase	)	)
								if index < len(lowerCAmelCase	) - 1:
										SCREAMING_SNAKE_CASE_  :		Any				      =    [lines[index + 1]]
										index += 1
								else:
										SCREAMING_SNAKE_CASE_  :		Tuple				      =    []
						else:
								blocks.append("\n".join(lowerCAmelCase	)	)
								SCREAMING_SNAKE_CASE_  :		Dict				      =    [lines[index]]
				else:
						current_block.append(lines[index]	)
				index += 1

		# Adds current block if it's nonempty.
		if len(lowerCAmelCase	) > 0:
				blocks.append("\n".join(lowerCAmelCase	)	)

		# Add final block after end_prompt if provided.
		if end_prompt is not None and index < len(lowerCAmelCase	):
				blocks.append("\n".join(lines[index:]	)	)

		return blocks




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		def _inner(lowerCAmelCase							:   List[str]	):
				return key(lowerCAmelCase	).lower().replace("_"   , ""	)

		return _inner




def       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   Dict=None	):
		"""simple docstring"""







		def noop(lowerCAmelCase							:   Optional[Any]	):
				return x

		if key is None:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    noop
		# Constants are all uppercase, they go first.
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [obj for obj in objects if key(lowerCAmelCase	).isupper()]
		# Classes are not all uppercase but start with a capital, they go second.
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [obj for obj in objects if key(lowerCAmelCase	)[0].isupper() and not key(lowerCAmelCase	).isupper()]
		# Functions begin with a lowercase, they go last.
		SCREAMING_SNAKE_CASE_  :		str				      =    [obj for obj in objects if not key(lowerCAmelCase	)[0].isupper()]

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    ignore_underscore(lowerCAmelCase	)
		return sorted(lowerCAmelCase   , key=lowerCAmelCase	) + sorted(lowerCAmelCase   , key=lowerCAmelCase	) + sorted(lowerCAmelCase   , key=lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		def _replace(lowerCAmelCase							:   Any	):
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    match.groups()[0]
				if "," not in imports:
						return f'[{imports}]'
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [part.strip().replace("\""   , ""	) for part in imports.split(","	)]
				# We will have a final empty element if the line finished with a comma.
				if len(keys[-1]	) == 0:
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    keys[:-1]
				return "[" + ", ".join([f'"{k}"' for k in sort_objects(lowerCAmelCase	)]	) + "]"

		SCREAMING_SNAKE_CASE_  :		str				      =    import_statement.split("\n"	)
		if len(lowerCAmelCase	) > 3:
				# Here we have to sort internal imports that are on several lines (one per name):
				# key: [
				#     "object1",
				#     "object2",
				#     ...
				# ]

				# We may have to ignore one or two lines on each side.
				SCREAMING_SNAKE_CASE_  :		str				      =    2 if lines[1].strip() == "[" else 1
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    [(i, _re_strip_line.search(lowerCAmelCase	).groups()[0]) for i, line in enumerate(lines[idx:-idx]	)]
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    sort_objects(lowerCAmelCase   , key=lambda lowerCAmelCase	: x[1]	)
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [lines[x[0] + idx] for x in sorted_indices]
				return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]	)
		elif len(lowerCAmelCase	) == 3:
				# Here we have to sort internal imports that are on one separate line:
				# key: [
				#     "object1", "object2", ...
				# ]
				if _re_bracket_content.search(lines[1]	) is not None:
						SCREAMING_SNAKE_CASE_  :		Any				      =    _re_bracket_content.sub(_replace   , lines[1]	)
				else:
						SCREAMING_SNAKE_CASE_  :		int				      =    [part.strip().replace("\""   , ""	) for part in lines[1].split(","	)]
						# We will have a final empty element if the line finished with a comma.
						if len(keys[-1]	) == 0:
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    keys[:-1]
						SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    get_indent(lines[1]	) + ", ".join([f'"{k}"' for k in sort_objects(lowerCAmelCase	)]	)
				return "\n".join(lowerCAmelCase	)
		else:
				# Finally we have to deal with imports fitting on one line
				SCREAMING_SNAKE_CASE_  :		Dict				      =    _re_bracket_content.sub(_replace   , lowerCAmelCase	)
				return import_statement




def       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[Any]=True	):
		"""simple docstring"""







		with open(lowerCAmelCase   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    f.read()

		if "_import_structure" not in code:
				return

		# Blocks of indent level 0
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    split_code_in_indented_blocks(
		    lowerCAmelCase   , start_prompt="_import_structure = {"   , end_prompt="if TYPE_CHECKING:"	)

		# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
		for block_idx in range(1   , len(lowerCAmelCase	) - 1	):
				# Check if the block contains some `_import_structure`s thingy to sort.
				SCREAMING_SNAKE_CASE_  :		int				      =    main_blocks[block_idx]
				SCREAMING_SNAKE_CASE_  :		Any				      =    block.split("\n"	)

				# Get to the start of the imports.
				SCREAMING_SNAKE_CASE_  :		str				      =    0
				while line_idx < len(lowerCAmelCase	) and "_import_structure" not in block_lines[line_idx]:
						# Skip dummy import blocks
						if "import dummy" in block_lines[line_idx]:
								SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    len(lowerCAmelCase	)
						else:
								line_idx += 1
				if line_idx >= len(lowerCAmelCase	):
						continue

				# Ignore beginning and last line: they don't contain anything.
				SCREAMING_SNAKE_CASE_  :		Any				      =    "\n".join(block_lines[line_idx:-1]	)
				SCREAMING_SNAKE_CASE_  :		Any				      =    get_indent(block_lines[1]	)
				# Slit the internal block into blocks of indent level 1.
				SCREAMING_SNAKE_CASE_  :		int				      =    split_code_in_indented_blocks(lowerCAmelCase   , indent_level=lowerCAmelCase	)
				# We have two categories of import key: list or _import_structure[key].append/extend
				SCREAMING_SNAKE_CASE_  :		Any				      =    _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
				# Grab the keys, but there is a trap: some lines are empty or just comments.
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [(pattern.search(lowerCAmelCase	).groups()[0] if pattern.search(lowerCAmelCase	) is not None else None) for b in internal_blocks]
				# We only sort the lines with a key.
				SCREAMING_SNAKE_CASE_  :		Any				      =    [(i, key) for i, key in enumerate(lowerCAmelCase	) if key is not None]
				SCREAMING_SNAKE_CASE_  :		int				      =    [x[0] for x in sorted(lowerCAmelCase   , key=lambda lowerCAmelCase	: x[1]	)]

				# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    0
				SCREAMING_SNAKE_CASE_  :		Any				      =    []
				for i in range(len(lowerCAmelCase	)	):
						if keys[i] is None:
								reordered_blocks.append(internal_blocks[i]	)
						else:
								SCREAMING_SNAKE_CASE_  :		Dict				      =    sort_objects_in_import(internal_blocks[sorted_indices[count]]	)
								reordered_blocks.append(lowerCAmelCase	)
								count += 1

        # And we put our main block back together with its first and last line.
				SCREAMING_SNAKE_CASE_  :		Dict				      =    "\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]	)

		if code != "\n".join(lowerCAmelCase	):
				if check_only:
						return True
				else:
						print(f'Overwriting {file}.'	)
						with open(lowerCAmelCase   , "w"	) as f:
								f.write("\n".join(lowerCAmelCase	)	)




def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]=True	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    []
		for root, _, files in os.walk(lowerCAmelCase	):
				if "__init__.py" in files:
						SCREAMING_SNAKE_CASE_  :		int				      =    sort_imports(os.path.join(lowerCAmelCase   , "__init__.py"	)   , check_only=lowerCAmelCase	)
						if result:
								SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [os.path.join(lowerCAmelCase   , "__init__.py"	)]
		if len(lowerCAmelCase	) > 0:
				raise ValueError(f'Would overwrite {len(lowerCAmelCase	)} files, run `make style`.'	)


if __name__ == "__main__":
		__lowerCamelCase					:	Any       =  argparse.ArgumentParser()
		parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
		__lowerCamelCase					:	Any       =  parser.parse_args()

		sort_imports_in_all_inits(check_only=args.check_only)

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
__lowerCamelCase					:	Any       =  {
    '''/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       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







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

				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    R"(encoder|decoder)\/"

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

						elif groups[0] == "decoder":
								SCREAMING_SNAKE_CASE_  :		List[str]				      =    re.sub(R"/mlp/"   , R"/2/mlp/"   , lowerCAmelCase	)
								SCREAMING_SNAKE_CASE_  :		Dict				      =    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:
								SCREAMING_SNAKE_CASE_  :		List[Any]				      =    new_key.replace(lowerCAmelCase   , lowerCAmelCase	)

				print(f'{key} -> {new_key}'	)
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    s_dict.pop(lowerCAmelCase	)

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

						s_dict.pop(lowerCAmelCase	)

		return s_dict


__lowerCamelCase					:	List[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       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		import regex as re

		with open(lowerCAmelCase   , "r"	) as f:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    f.read()

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

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    re.findall(R"(.*activations) = \(\'(.*)\',\)"   , lowerCAmelCase	)[0]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    str(activation[1]	)

		SCREAMING_SNAKE_CASE_  :		str				      =    num_experts
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    SwitchTransformersConfig(**lowerCAmelCase	)
		return config




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str   , lowerCAmelCase							:   str=None   , lowerCAmelCase							:   Optional[Any]="./"   , lowerCAmelCase							:   Dict=8	):
		"""simple docstring"""







		print(f'Loading flax weights from : {flax_checkpoint_path}'	)
		SCREAMING_SNAKE_CASE_  :		int				      =    checkpoints.load_tax_checkpoint(lowerCAmelCase	)

		if gin_file is not None:
				SCREAMING_SNAKE_CASE_  :		int				      =    convert_gin_to_config(lowerCAmelCase   , lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    SwitchTransformersConfig.from_pretrained(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		str				      =    SwitchTransformersForConditionalGeneration(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    flax_params["target"]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    flatten_dict(lowerCAmelCase   , sep="/"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    rename_keys(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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__":
		__lowerCamelCase					:	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''')
		__lowerCamelCase					:	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,
		)
