
"""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
lowercase__		:Union[str, 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 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
					# the original model
					lowercase						     =						list(s_dict.keys()		)
					for key in keys:
										lowercase						     =						R'''.*/layers_(\d+)'''
										lowercase						     =						key
										if re.match(_UpperCamelCase			,      _UpperCamelCase		):
															lowercase						     =						re.sub(R'''layers_(\d+)'''			,      R'''block/\1/layer'''			,      _UpperCamelCase		)

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

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

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

										print(f'{key} -> {new_key}'		)
										lowercase						     =						s_dict.pop(_UpperCamelCase		)

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

															s_dict.pop(_UpperCamelCase		)

					return s_dict


lowercase__		: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 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# Convert a google style config to the hugging face fromat
					import regex as re

					with open(_UpperCamelCase			,      '''r'''		) as f:
										lowercase						     =						f.read()

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

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

					lowercase						     =						num_experts
					lowercase						     =						SwitchTransformersConfig(**_UpperCamelCase		)
					return config



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=None			,      lowerCAmelCase__="./"			,      lowerCAmelCase__=8		):
					'''simple docstring'''
					# Initialise PyTorch model

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

					if gin_file is not None:
										lowercase						     =						convert_gin_to_config(_UpperCamelCase			,      _UpperCamelCase		)
					else:
										lowercase						     =						SwitchTransformersConfig.from_pretrained(_UpperCamelCase		)

					lowercase						     =						SwitchTransformersForConditionalGeneration(_UpperCamelCase		)

					lowercase						     =						flax_params['''target''']
					lowercase						     =						flatten_dict(_UpperCamelCase			,      sep='''/'''		)
					lowercase						     =						rename_keys(_UpperCamelCase		)
					lowercase						     =						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__":
							lowercase__		: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")
							lowercase__		:int			  =   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 inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase__		:Optional[Any]			  =   "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
lowercase__		:int			  =   direct_transformers_import(PATH_TO_TRANSFORMERS)

lowercase__		:List[Any]			  =   transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowercase__		:List[str]			  =   {
    # used to compute the property `self.chunk_length`
    "EncodecConfig": ["overlap"],
    # used as `self.bert_model = BertModel(config, ...)`
    "DPRConfig": True,
    # not used in modeling files, but it's an important information
    "FSMTConfig": ["langs"],
    # used internally in the configuration class file
    "GPTNeoConfig": ["attention_types"],
    # used internally in the configuration class file
    "EsmConfig": ["is_folding_model"],
    # used during training (despite we don't have training script for these models yet)
    "Mask2FormerConfig": ["ignore_value"],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    "OneFormerConfig": ["ignore_value", "norm"],
    # used during preprocessing and collation, see `collating_graphormer.py`
    "GraphormerConfig": ["spatial_pos_max"],
    # used internally in the configuration class file
    "T5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    "MT5Config": ["feed_forward_proj", "tokenizer_class"],
    "UMT5Config": ["feed_forward_proj", "tokenizer_class"],
    # used internally in the configuration class file
    "LongT5Config": ["feed_forward_proj"],
    # used internally in the configuration class file
    "SwitchTransformersConfig": ["feed_forward_proj"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "BioGptConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "GLPNConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "SegformerConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "CvtConfig": ["layer_norm_eps"],
    # having default values other than `1e-5` - we can't fix them without breaking
    "PerceiverConfig": ["layer_norm_eps"],
    # used internally to calculate the feature size
    "InformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate the feature size
    "AutoformerConfig": ["num_static_real_features", "num_time_features"],
    # used internally to calculate `mlp_dim`
    "SamVisionConfig": ["mlp_ratio"],
    # For (head) training, but so far not implemented
    "ClapAudioConfig": ["num_classes"],
    # Not used, but providing useful information to users
    "SpeechT5HifiGanConfig": ["sampling_rate"],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        "CLIPSegConfig": True,
        "DeformableDetrConfig": True,
        "DetaConfig": True,
        "DinatConfig": True,
        "DonutSwinConfig": True,
        "EfficientFormerConfig": True,
        "FSMTConfig": True,
        "JukeboxConfig": True,
        "LayoutLMv2Config": True,
        "MaskFormerSwinConfig": True,
        "MT5Config": True,
        "NatConfig": True,
        "OneFormerConfig": True,
        "PerceiverConfig": True,
        "RagConfig": True,
        "SpeechT5Config": True,
        "SwinConfig": True,
        "Swin2SRConfig": True,
        "Swinv2Config": True,
        "SwitchTransformersConfig": True,
        "TableTransformerConfig": True,
        "TapasConfig": True,
        "TransfoXLConfig": True,
        "UniSpeechConfig": True,
        "UniSpeechSatConfig": True,
        "WavLMConfig": True,
        "WhisperConfig": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        "JukeboxPriorConfig": True,
        # TODO: @Younes (for `is_decoder`)
        "Pix2StructTextConfig": True,
    }
)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						False
					for attribute in attributes:
										for modeling_source in source_strings:
															# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
															if (
															    f'config.{attribute}' in modeling_source
															    or f'getattr(config, "{attribute}"' in modeling_source
															    or f'getattr(self.config, "{attribute}"' in modeling_source
															):
																				lowercase						     =						True
															# Deal with multi-line cases
															elif (
															    re.search(
															        Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'			,      lowerCAmelCase__			,      )
															    is not None
															):
																				lowercase						     =						True
															# `SequenceSummary` is called with `SequenceSummary(config)`
															elif attribute in [
															    "summary_type",
															    "summary_use_proj",
															    "summary_activation",
															    "summary_last_dropout",
															    "summary_proj_to_labels",
															    "summary_first_dropout",
															]:
																				if "SequenceSummary" in modeling_source:
																									lowercase						     =						True
															if attribute_used:
																				break
										if attribute_used:
															break

    # common and important attributes, even if they do not always appear in the modeling files
					lowercase						     =						[
					    '''bos_index''',
					    '''eos_index''',
					    '''pad_index''',
					    '''unk_index''',
					    '''mask_index''',
					    '''image_size''',
					    '''use_cache''',
					    '''out_features''',
					    '''out_indices''',
					]
					lowercase						     =						['''encoder_no_repeat_ngram_size''']

					# Special cases to be allowed
					lowercase						     =						True
					if not attribute_used:
										lowercase						     =						False
										for attribute in attributes:
															# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
															if attribute in ["is_encoder_decoder"] and default_value is True:
																				lowercase						     =						True
															elif attribute in ["tie_word_embeddings"] and default_value is False:
																				lowercase						     =						True

															# Allow cases without checking the default value in the configuration class
															elif attribute in attributes_to_allow + attributes_used_in_generation:
																				lowercase						     =						True
															elif attribute.endswith('''_token_id'''		):
																				lowercase						     =						True

															# configuration class specific cases
															if not case_allowed:
																				lowercase						     =						SPECIAL_CASES_TO_ALLOW.get(config_class.__name__			,      []		)
																				lowercase						     =						allowed_cases is True or attribute in allowed_cases

					return attribute_used or case_allowed



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						dict(inspect.signature(config_class.__init__		).parameters		)
					lowercase						     =						[x for x in list(signature.keys()		) if x not in ['''self''', '''kwargs''']]
					lowercase						     =						[signature[param].default for param in parameter_names]

					# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
					# as one variant is used, the test should pass
					lowercase						     =						{}
					if len(config_class.attribute_map		) > 0:
										lowercase						     =						{v: k for k, v in config_class.attribute_map.items()}

					# Get the path to modeling source files
					lowercase						     =						inspect.getsourcefile(lowerCAmelCase__		)
					lowercase						     =						os.path.dirname(lowerCAmelCase__		)
					# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
					lowercase						     =						[os.path.join(lowerCAmelCase__			,      lowerCAmelCase__		) for fn in os.listdir(lowerCAmelCase__		) if fn.startswith('''modeling_'''		)]

					# Get the source code strings
					lowercase						     =						[]
					for path in modeling_paths:
										if os.path.isfile(lowerCAmelCase__		):
															with open(lowerCAmelCase__		) as fp:
																				modeling_sources.append(fp.read()		)

					lowercase						     =						[]
					for config_param, default_value in zip(lowerCAmelCase__			,      lowerCAmelCase__		):
										# `attributes` here is all the variant names for `config_param`
										lowercase						     =						[config_param]
										# some configuration classes have non-empty `attribute_map`, and both names could be used in the
										# corresponding modeling files. As long as one of them appears, it is fine.
										if config_param in reversed_attribute_map:
															attributes.append(reversed_attribute_map[config_param]		)

										if not check_attribute_being_used(lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
															unused_attributes.append(attributes[0]		)

					return sorted(lowerCAmelCase__		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						{}
					for _config_class in list(CONFIG_MAPPING.values()		):
										# Skip deprecated models
										if "models.deprecated" in _config_class.__module__:
															continue
										# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
										lowercase						     =						[
										    cls
										    for name, cls in inspect.getmembers(
										        inspect.getmodule(_config_class		)			,      lambda lowerCAmelCase__		: inspect.isclass(lowerCAmelCase__		)
										        and issubclass(lowerCAmelCase__			,      lowerCAmelCase__		)
										        and inspect.getmodule(lowerCAmelCase__		) == inspect.getmodule(_config_class		)			,      )
										]
										for config_class in config_classes_in_module:
															lowercase						     =						check_config_attributes_being_used(lowerCAmelCase__		)
															if len(lowerCAmelCase__		) > 0:
																				lowercase						     =						unused_attributes

					if len(lowerCAmelCase__		) > 0:
										lowercase						     =						'''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
										for name, attributes in configs_with_unused_attributes.items():
															error += f'{name}: {attributes}\n'

										raise ValueError(lowerCAmelCase__		)


if __name__ == "__main__":
							check_config_attributes()




