"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


SCREAMING_SNAKE_CASE__   =					[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]





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





						"""simple docstring"""






						for pegasus_name, hf_name in PATTERNS:
												snake_case        =     k.replace(_UpperCamelCase	,     _UpperCamelCase   )
						return k





def 				lowerCAmelCase__      (     _UpperCamelCase    :			dict	,     _UpperCamelCase    :			dict   )   ->			PegasusForConditionalGeneration:





						"""simple docstring"""






						snake_case        =     DEFAULTS.copy()
						cfg_kwargs.update(_UpperCamelCase   )
						snake_case        =     PegasusConfig(**_UpperCamelCase   )
						snake_case        =     PegasusForConditionalGeneration(_UpperCamelCase   )
						snake_case        =     torch_model.model.state_dict()
						snake_case        =     {}
						for k, v in tf_weights.items():
												snake_case        =     rename_state_dict_key(_UpperCamelCase   )
												if new_k not in sd:
																		raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""   )

												if "dense" in k or "proj" in new_k:
																		snake_case        =     v.T
												snake_case        =     torch.tensor(_UpperCamelCase	,     dtype=sd[new_k].dtype   )
												assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
						# make sure embedding.padding_idx is respected
						snake_case        =     torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]   )
						snake_case        =     mapping['shared.weight']
						snake_case        =     mapping['shared.weight']
						snake_case        =     {k: torch.zeros_like(_UpperCamelCase   ) for k, v in sd.items() if k.endswith('bias'   ) and k not in mapping}
						mapping.update(**_UpperCamelCase   )
						snake_case   ,snake_case        =     torch_model.model.load_state_dict(_UpperCamelCase	,     strict=_UpperCamelCase   )
						snake_case        =     [
						    k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
						]
						assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
						assert extra == [], f"""no matches found for the following tf keys {extra}"""
						return torch_model





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]="./ckpt/aeslc/model.ckpt-32000"   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     tf.train.list_variables(_UpperCamelCase   )
						snake_case        =     {}
						snake_case        =     ['Adafactor', 'global_step']
						for name, shape in tqdm(_UpperCamelCase	,     desc='converting tf checkpoint to dict'   ):
												snake_case        =     any(pat in name for pat in ignore_name   )
												if skip_key:
																		continue
												snake_case        =     tf.train.load_variable(_UpperCamelCase	,     _UpperCamelCase   )
												snake_case        =     array
						return tf_weights





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





						"""simple docstring"""






						snake_case        =     Path(_UpperCamelCase   ).parent.name
						snake_case        =     task_specific_params[f"""summarization_{dataset}"""]['max_position_embeddings']
						snake_case        =     PegasusTokenizer.from_pretrained('sshleifer/pegasus'	,     model_max_length=_UpperCamelCase   )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(_UpperCamelCase   )

						# convert model
						snake_case        =     get_tf_weights_as_numpy(_UpperCamelCase   )
						snake_case        =     task_specific_params[f"""summarization_{dataset}"""]
						if dataset == "large":
												snake_case        =     task_specific_params
						snake_case        =     convert_pegasus(_UpperCamelCase	,     _UpperCamelCase   )
						torch_model.save_pretrained(_UpperCamelCase   )
						snake_case        =     torch_model.state_dict()
						sd.pop('model.decoder.embed_positions.weight'   )
						sd.pop('model.encoder.embed_positions.weight'   )
						torch.save(_UpperCamelCase	,     Path(_UpperCamelCase   ) / 'pytorch_model.bin'   )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
					parser.add_argument("save_dir", default=None, type=str, help="Path to the output PyTorch model.")
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					if args.save_dir is None:
										SCREAMING_SNAKE_CASE__   =					Path(args.tf_ckpt_path).parent.name
										SCREAMING_SNAKE_CASE__   =					os.path.join("pegasus", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


