




import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__UpperCamelCase   :			str          = ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
__UpperCamelCase   :			str          = {"bart.large": BartModel, "bart.large.mnli": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
		raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
__UpperCamelCase   :			Union[str, Any]          = logging.get_logger(__name__)

__UpperCamelCase   :			Optional[Any]          = " Hello world! cécé herlolip"

__UpperCamelCase   :			Tuple          = [
    ("model.classification_heads.mnli.dense.weight", "classification_head.dense.weight"),
    ("model.classification_heads.mnli.dense.bias", "classification_head.dense.bias"),
    ("model.classification_heads.mnli.out_proj.weight", "classification_head.out_proj.weight"),
    ("model.classification_heads.mnli.out_proj.bias", "classification_head.out_proj.bias"),
]





def 						__A						(      __lowerCamelCase       ) ->			List[Any]:
	a									=				[
	    """encoder.version""",
	    """decoder.version""",
	    """model.encoder.version""",
	    """model.decoder.version""",
	    """_float_tensor""",
	]
	for k in ignore_keys:
		state_dict.pop(__lowerCamelCase				,     __lowerCamelCase       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[Any]:
	a									=				dct.pop(__lowerCamelCase       )
	a									=				val





def 						__A						(      __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				torch.load(__lowerCamelCase				,     map_location="""cpu"""       )
	a									=				torch.hub.load("""pytorch/fairseq"""				,     """bart.large.cnn"""       ).eval()
	hub_interface.model.load_state_dict(sd["""model"""]       )
	return hub_interface





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a							,					a									=				emb.weight.shape
	a									=				nn.Linear(__lowerCamelCase				,     __lowerCamelCase				,     bias=__lowerCamelCase       )
	a									=				emb.weight.data
	return lin_layer





@torch.no_grad()
def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Optional[int]:
	if not os.path.exists(__lowerCamelCase       ):
		a									=				torch.hub.load("""pytorch/fairseq"""				,     __lowerCamelCase       ).eval()
	else:
		a									=				load_xsum_checkpoint(__lowerCamelCase       )

	bart.model.upgrade_state_dict(bart.model.state_dict()       )
	if hf_checkpoint_name is None:
		a									=				checkpoint_path.replace("""."""				,     """-"""       )
	a									=				BartConfig.from_pretrained(__lowerCamelCase       )
	a									=				bart.encode(__lowerCamelCase       ).unsqueeze(0       )
	a									=				BartTokenizer.from_pretrained(__lowerCamelCase       ).encode(__lowerCamelCase				,     return_tensors="""pt"""       ).unsqueeze(0       )
	if not torch.eq(__lowerCamelCase				,     __lowerCamelCase       ).all():
		raise ValueError(
		    f'converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'       )

	if checkpoint_path == "bart.large.mnli":
		a									=				bart.state_dict()
		remove_ignore_keys_(__lowerCamelCase       )
		a									=				state_dict["""model.decoder.embed_tokens.weight"""]
		for src, dest in mnli_rename_keys:
			rename_key(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       )
		a									=				BartForSequenceClassification(__lowerCamelCase       ).eval()
		model.load_state_dict(__lowerCamelCase       )
		a									=				bart.predict("""mnli"""				,     __lowerCamelCase				,     return_logits=__lowerCamelCase       )
		a									=				model(__lowerCamelCase       )[0]  # logits
	else:  # no classification heads to worry about
		a									=				bart.model.state_dict()
		remove_ignore_keys_(__lowerCamelCase       )
		a									=				state_dict["""decoder.embed_tokens.weight"""]
		a									=				bart.extract_features(__lowerCamelCase       )
		if hf_checkpoint_name == "facebook/bart-large":
			a									=				BartModel(__lowerCamelCase       ).eval()
			model.load_state_dict(__lowerCamelCase       )
			a									=				model(__lowerCamelCase       ).model[0]
		else:
			a									=				BartForConditionalGeneration(__lowerCamelCase       ).eval()  # an existing summarization ckpt
			model.model.load_state_dict(__lowerCamelCase       )
			if hasattr(__lowerCamelCase				,     """lm_head"""       ):
				a									=				make_linear_from_emb(model.model.shared       )
			a									=				model.model(__lowerCamelCase       )[0]

    # Check results
	if fairseq_output.shape != new_model_outputs.shape:
		raise ValueError(
		    f'`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'       )
	if (fairseq_output != new_model_outputs).any().item():
		raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""       )
	Path(__lowerCamelCase       ).mkdir(exist_ok=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			str          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
		)
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument(
		    "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
		)
		__UpperCamelCase   :			Optional[int]          = parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)







import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__UpperCamelCase   :			Tuple          = "."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__UpperCamelCase   :			List[str]          = [
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				SavedModel()
	a									=				[]

	with open(os.path.join(__lowerCamelCase				,     """utils"""				,     """tf_ops"""				,     """onnx.json"""       )       ) as f:
		a									=				json.load(__lowerCamelCase       )["""opsets"""]

	for i in range(1				,     opset + 1       ):
		onnx_ops.extend(onnx_opsets[str(__lowerCamelCase       )]       )

	with open(__lowerCamelCase				,     """rb"""       ) as f:
		saved_model.ParseFromString(f.read()       )

	a									=				set()

	# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
	for meta_graph in saved_model.meta_graphs:
		# Add operations in the graph definition
		model_op_names.update(node.op for node in meta_graph.graph_def.node       )

		# Go through the functions in the graph definition
		for func in meta_graph.graph_def.library.function:
			# Add operations in each function
			model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
	a									=				sorted(__lowerCamelCase       )
	a									=				[]

	for op in model_op_names:
		if op not in onnx_ops and op not in INTERNAL_OPS:
			incompatible_ops.append(__lowerCamelCase       )

	if strict and len(__lowerCamelCase       ) > 0:
		raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops       )
	elif len(__lowerCamelCase       ) > 0:
		print(f'Found the following incompatible ops for the opset {opset}:'       )
		print(*__lowerCamelCase				,     sep="""\n"""       )
	else:
		print(f'The saved model {saved_model_path} can properly be converted with ONNX.'       )


if __name__ == "__main__":
		__UpperCamelCase   :			Optional[Any]          = argparse.ArgumentParser()
		parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
		parser.add_argument(
		    "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
		)
		parser.add_argument(
		    "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
		)
		parser.add_argument(
		    "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
		)
		__UpperCamelCase   :			str          = parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)

