
'''simple docstring'''

import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_UpperCAmelCase :   Union[str, Any]			    =	"""src/diffusers"""

# Matches is_xxx_available()
_UpperCAmelCase :   Dict			    =	re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
_UpperCAmelCase :   List[str]			    =	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


_UpperCAmelCase :   Dict			    =	"""
{0} = None
"""

_UpperCAmelCase :   Optional[Any]			    =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


_UpperCAmelCase :   str			    =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







def     snake_case__						(   UpperCamelCase						)			->   Optional[Any]:
		_UpperCamelCase							:			str     =   _re_backend.findall(_A						)
		if len(_A						) == 0:
				return None

		return "_and_".join(_A						)







def     snake_case__						(   )			->   Union[str, Any]:


		with open(os.path.join(_A      ,'''__init__.py'''						)      ,'''r'''      ,encoding='''utf-8'''      ,newline='''\n'''						) as f:
				_UpperCamelCase							:			int     =   f.readlines()

		# Get to the point we do the actual imports for type checking
		_UpperCamelCase							:			Dict     =   0
		_UpperCamelCase							:			List[Any]     =   {}
		# Go through the end of the file
		while line_index < len(_A						):
				# If the line contains is_backend_available, we grab all objects associated with the `else` block
				_UpperCamelCase							:			Union[str, Any]     =   find_backend(lines[line_index]						)
				if backend is not None:
						while not lines[line_index].startswith('''else:'''						):
								line_index += 1
						line_index += 1
						_UpperCamelCase							:			Optional[int]     =   []
						# Until we unindent, add backend objects to the list
						while line_index < len(_A						) and len(lines[line_index]						) > 1:
								_UpperCamelCase							:			Optional[int]     =   lines[line_index]
								_UpperCamelCase							:			Union[str, Any]     =   _re_single_line_import.search(_A						)
								if single_line_import_search is not None:
										objects.extend(single_line_import_search.groups()[0].split(''', '''						)						)
								elif line.startswith(''' ''' * 8						):
										objects.append(line[8:-2]						)
								line_index += 1

						if len(_A						) > 0:
								_UpperCamelCase							:			List[Any]     =   objects
				else:
						line_index += 1

		return backend_specific_objects







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase						)			->   List[Any]:


		if name.isupper():
				return DUMMY_CONSTANT.format(_A						)
		elif name.islower():
				return DUMMY_FUNCTION.format(_A      ,_A						)
		else:
				return DUMMY_CLASS.format(_A      ,_A						)







def     snake_case__						(   UpperCamelCase=None						)			->   List[str]:


		if backend_specific_objects is None:
				_UpperCamelCase							:			Optional[Any]     =   read_init()
		# For special correspondence backend to module name as used in the function requires_modulename
		_UpperCamelCase							:			Any     =   {}

		for backend, objects in backend_specific_objects.items():
				_UpperCamelCase							:			Tuple     =   '''[''' + ''', '''.join(f'''\"{b}\"''' for b in backend.split('''_and_'''						)						) + ''']'''
				_UpperCamelCase							:			Dict     =   '''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
				dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
				dummy_file += "\n".join([create_dummy_object(_A      ,_A						) for o in objects]						)
				_UpperCamelCase							:			Dict     =   dummy_file

		return dummy_files







def     snake_case__						(   UpperCamelCase=False						)			->   Tuple:
		_UpperCamelCase							:			List[Any]     =   create_dummy_files()
		# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
		_UpperCamelCase							:			str     =   {'''torch''': '''pt'''}

		# Locate actual dummy modules and read their content.
		_UpperCamelCase							:			str     =   os.path.join(_A      ,'''utils'''						)
		_UpperCamelCase							:			Dict     =   {
		    backend: os.path.join(_A      ,f'''dummy_{short_names.get(_A      ,_A						)}_objects.py'''						)
		    for backend in dummy_files.keys()
		}

		_UpperCamelCase							:			Any     =   {}
		for backend, file_path in dummy_file_paths.items():
				if os.path.isfile(_A						):
						with open(_A      ,'''r'''      ,encoding='''utf-8'''      ,newline='''\n'''						) as f:
								_UpperCamelCase							:			List[Any]     =   f.read()
				else:
						_UpperCamelCase							:			List[str]     =   ''''''

		for backend in dummy_files.keys():
				if dummy_files[backend] != actual_dummies[backend]:
						if overwrite:
								print(
								    f'''Updating diffusers.utils.dummy_{short_names.get(_A      ,_A						)}_objects.py as the main '''
								    '''__init__ has new objects.'''						)
								with open(dummy_file_paths[backend]      ,'''w'''      ,encoding='''utf-8'''      ,newline='''\n'''						) as f:
										f.write(dummy_files[backend]						)
						else:
								raise ValueError(
								    '''The main __init__ has objects that are not present in '''
								    f'''diffusers.utils.dummy_{short_names.get(_A      ,_A						)}_objects.py. Run `make fix-copies` '''
								    '''to fix this.'''						)


if __name__ == "__main__":
	_UpperCAmelCase :   List[Any]			    =	argparse.ArgumentParser()
	parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
	_UpperCAmelCase :   List[str]			    =	parser.parse_args()

	check_dummies(args.fix_and_overwrite)






'''simple docstring'''




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 							snake_case__				(     _A:   Dict					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    os.path.join(args.tf_model_dir ,  """parameters.json"""					)
		lowerCAmelCase     =    json.loads(open(_A					).read()					)
		if not params:
				raise ValueError(
				    f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."					)
		if not args.output.endswith(""".pt"""					):
				lowerCAmelCase     =    args.output + """.pt"""
		lowerCAmelCase     =    OrderedDict()
		with tf.device("""/CPU:0"""					):
				lowerCAmelCase     =    tf.train.load_checkpoint(args.tf_model_dir					)
				lowerCAmelCase     =    reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						lowerCAmelCase     =    reader.get_tensor(_A					).astype(np.floataa					)
						if key_name.endswith("""/adam_m"""					) or key_name.endswith("""/adam_v"""					):
								continue
						if key_name.startswith("""pasts/"""					):
								if key_name.startswith("""pasts/mlp"""					):
										lowerCAmelCase     =    int(key_name[9]					)
								elif key_name.startswith("""pasts/out"""					):
										lowerCAmelCase     =    8
								lowerCAmelCase     =    """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/moe"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/switch_gating/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/softmlp/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/wo/kernel"""					) or key_name.endswith("""/wi/kernel"""					):
										lowerCAmelCase     =    key_name[-9:-7]
										for i in range(16					):
												lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
												lowerCAmelCase     =    (
												    vnp[i].transpose([1, 0]					).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/mlp"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/p1/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p1/bias"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p2/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
										lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/p2/bias"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/ln"""					):
								lowerCAmelCase     =    int(key_name[8:].split("""/"""					)[0]					)
								if key_name.endswith("""/b"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.norm.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/g"""					):
										lowerCAmelCase     =    """model.blocks.%d.feed_forward.norm.weight""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/att"""					):
								lowerCAmelCase     =    int(key_name[9:].split("""/"""					)[0]					)
								if key_name.endswith("""/qkv/kernel"""					):
										lowerCAmelCase     =    vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										lowerCAmelCase     =    state[:, 0, :, :]
										lowerCAmelCase     =    state[:, 1, :, :]
										lowerCAmelCase     =    state[:, 2, :, :]
										lowerCAmelCase     =    (
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]					)
										    .transpose([1, 0]					)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    (
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]					)
										    .transpose([1, 0]					)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    (
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]					)
										    .transpose([1, 0]					)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/o/kernel"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
										lowerCAmelCase     =    (
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/an"""					):
								lowerCAmelCase     =    int(key_name[8:].split("""/"""					)[0]					)
								if key_name.endswith("""/b"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.norm.bias""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
								elif key_name.endswith("""/g"""					):
										lowerCAmelCase     =    """model.blocks.%d.self_attn.norm.weight""" % player
										lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
										lowerCAmelCase     =    torch.tensor(_A					)
						elif (
						    key_name.startswith("""model/wte"""					)
						    or key_name.startswith("""model/wpe"""					)
						    or key_name.startswith("""model/ete"""					)
						):
								lowerCAmelCase     =    {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
								    key_name[-3:]
								]
								lowerCAmelCase     =    """model.%s.weight""" % nlayer
								lowerCAmelCase     =    vnp.copy()  # same in embedded
								lowerCAmelCase     =    torch.tensor(_A					)
								if key_name.startswith("""model/wte"""					):
										lowerCAmelCase     =    """lm_head.weight"""
										lowerCAmelCase     =    vnp.copy()  # same in embedded
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/wob"""					):
								lowerCAmelCase     =    """final_logits_bias"""
								lowerCAmelCase     =    vnp.copy()  # same in embedded
								lowerCAmelCase     =    state.reshape((1, -1)					)
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name == "model/dense/kernel":
								lowerCAmelCase     =    """model.last_project.weight"""
								lowerCAmelCase     =    vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
								lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name == "model/dense_1/bias":
								lowerCAmelCase     =    """model.last_project.bias"""
								lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
								lowerCAmelCase     =    torch.tensor(_A					)
		torch.save(_A ,  args.output					)


if __name__ == "__main__":
						__lowercase        =    argparse.ArgumentParser(
						    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
						)
						parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
						parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
						__lowercase        =    parser.parse_args()
						convert_tf_gptsan_to_pt(args)




